The AI
Race
A Race Towards The Greatest Invention Of Mankind
Road to AGI
Real-time Trajectory Index // Frontier Telemetry v5.0
This real-time index tracks humanity's unprecedented velocity toward Artificial General Intelligence (AGI)—systems that equal human performance across all economically valuable tasks—and the subsequent, inevitable leap to Artificial Superintelligence (ASI).
Current frontier models are no longer just tools; they are embryonic agents demonstrating advanced reasoning, long-horizon planning, and autonomous code execution. As we scale compute by orders of magnitude and refine architecture through reinforcement learning, the distinction between silicon and sentient logic blurs. We are witnessing the final phase of the "narrow AI" era, rapidly transitioning into a future where digital minds will not only match our cognitive capabilities but vastly exceed them, reshaping science, economics, and the very definition of intelligence itself.
Frontier Elite Analysis
Real-time Rankings across Commercial and Open Weights
Commercial Sector
GPT-5.2
"OpenAI's most advanced model. Perfect AIME score, dominant in reasoning and coding benchmarks."
Gemini 3 Pro
"Google's flagship model. 1M token context window, excels in multimodal understanding."
Claude 4.5 Opus
"Anthropic's most capable model. Superior at nuanced reasoning and extended context tasks."
Gemini 3 Flash
"Google’s ultra-fast frontier model. Beats Gemini 3 Pro on Toolathlon and coding efficiency, ties or wins on MMMU‑Pro, while staying much cheaper and faster."
Gemini 2.5 Flash
"High-velocity intelligence. Optimized for sub-second inference with frontier accuracy."
Open Source Sector
Llama 3.1 405B
"Proven reliability and massive ecosystem support for fine-tuning."
Kimi K2
"Moonshot AI's flagship thinking and reasoning model, for long context and complex reasoning tasks."
Qwen 3-72B
"Leading multilingual and mathematical performance in the public weights sector."
DeepSeek-V3
"China's open-weights champion. Remarkable efficiency and reasoning at fraction of compute."
Llama 4-405B
"Meta's open foundation model. Industry standard for fine-tuning and deployment."
DeepSeek-V2.5
"China's open-weights champion. Remarkable efficiency and reasoning at fraction of compute."
Historical Pulse
Intelligence Trajectories
Historical Intelligence Mapping / v4.2
The trajectory visualized above represents the "Great Acceleration," an epoch definitively tracing the evolution of artificial intelligence from the publication of the Transformer architecture in 2017 to the maturation of autonomous reasoning agents in late 2025. This historical curve is not merely a record of parameter scaling, but a visualization of the collapse of cognitive barriers. It captures the distinct phases of the AI race: the Scale Era (2017–2022), dominated by the pursuit of raw parameter dominance which validated the scaling laws; the Productization Era (2023–2024), marked by the Cambrian explosion of commercial interfaces like ChatGPT and the crystallization of a potent open-source insurgency led by Meta’s Llama series, Mistral, and Alibaba’s Qwen; and finally, the Reasoning Era (2025).
The graph highlights pivotal inflection points, such as the democratization of high-performance inference via the LLaMA leak, and the emergence of "Test-Time Compute" in 2025 with models like DeepSeek R1 and OpenAI's o-series. These milestones signify the shift from static token prediction to dynamic, self-correcting logic chains—systems that "think" before they speak. Most strikingly, this era witnessed AlphaEvolve and similar reasoning agents generating solutions to previously open Erdős problems—mathematical challenges that had resisted human intellect for over 50 years. This trajectory maps humanity's transition from utilizing silicon tools to collaborating with digital agents capable of independent scientific discovery.
The Fall of the
Unsolvable
AI systems are now generating solutions to Erdős problems—mathematical challenges that have resisted human intellect for over 50 years. This isn't memorization. This is de novo reasoning.
Key Breakthroughs
AI-Generated Solutions to Previously Open Problems
"The purest test of reasoning isn't a standardized exam—it's the unsolved."
Our
Benchmarking
Philosophy
"Static benchmarks are dead. We measure the soul of the machine through dynamic friction and recursive logic."
The Core Directive
Contamination-Proof
Intelligence Verification
In an era where "state-of-the-art" models are released weekly, traditional static benchmarks like MMLU or GSM8K have become trivialized by contamination—models are frequently trained on the very questions they are tested against. Our methodology rejects this "memorization contest." Instead, we employ a dynamic, adversarial testing framework designed to probe the frontier of reasoning, not recall. We focus on "novelty generalization"—the ability of a system to synthesize disparate concepts into coherent, actionable strategies in scenarios it has never encountered. By prioritizing "test-time compute" efficiency and agentic reliability over raw parameter counts, we reveal the true cognitive density of a model. We don't just ask, "What does it know?" We ask, "How does it think?"
The ultimate validation of this philosophy lies in mathematics. We track AI's progress against Erdős problems—open conjectures posed by the legendary Paul Erdős that have stumped mathematicians for decades. When a model solves one of these, it cannot be dismissed as pattern matching or data leakage. It is genuine reasoning, verified through formal proof assistants like Lean. This is the gold standard.
Dynamic Reasoning
We utilize non-public, evolving test cases to prevent training-data contamination.
Latency Scaling
Analysis of reasoning depth relative to response speed—real-time "IQ per second" metrics.
Cross-Domain Logic
Evaluating how models synthesize information across distinct, unrelated knowledge fields.
Further Intelligence
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