2025: The Last Primitive Year of Humanity

• The past 12 months delivered 4 – 5× more training compute, 10× effective intelligence gains, and inference costs dropping over an order of magnitude, rendering early-2024 AI systems obsolete.
• Frontier models now autonomously solve 85 % of real GitHub issues, achieve graduate-level reasoning, control humanoid robots at 80 %+ success on unstructured tasks, and discover novel mathematical proofs and proteins.
• 2025 marks the final year of intelligence scarcity; starting in 2026, continuously improving superintelligence will become abundant and inexpensive, ending the primitive era of human history.

We stand at the inflection point where the pace of artificial intelligence (AI) advancement has rendered every previous era of computing effectively obsolete. The past twelve months alone have delivered compounding breakthroughs that make the capabilities of even early 2024 systems appear rudimentary by comparison.

Training compute for frontier models has continued its exponential climb, with clusters now routinely exceeding 100,000 H100-equivalent GPUs and moving rapidly toward million-GPU scales. Effective compute measured in FLOPs has jumped roughly 4 – 5× year-over-year, while post-training enhancements and inference-time optimization have pushed usable intelligence gains closer to 10× in many domains. Reasoning models can now sustain coherent multi-step planning across thousands of internal tokens, achieving pass rates on graduate-level benchmarks that were essentially zero in 2023. Coding assistants have crossed the threshold from useful to authoritative: internal sweeps at leading laboratories show 2025 models resolving over 85 % of real-world GitHub issues without human intervention when given full repository context.

The shift from pre-training scale to inference-time scale has proved transformative. Techniques that allocate hundreds of billions of FLOPs per query – once considered impractical – are now standard for high-value applications. Chain-of-thought, self-verification loops, tool use, and long-context reasoning have fused into default operating procedures that deliver reliability previously attainable only through extensive human oversight. Concurrently, multimodal integration has matured to the point where a single model can ingest and reason across terabytes of video, code, text, and structured data in real time, producing outputs that rival specialized teams of experts.

Energy efficiency gains have been equally dramatic. Inference costs per million tokens have fallen by more than an order of magnitude since late 2024, driven by architectural improvements, quantization breakthroughs, and speculative decoding methods that achieve 5 – 10× speedups with negligible quality loss. The result is that capabilities once restricted to a handful of laboratories are now accessible through consumer devices and API calls costing fractions of a cent.

The gap between open-weight releases and closed frontier systems has narrowed to months rather than years. State-of-the-art open models released in mid-2025 already outperform many proprietary systems that were considered superhuman only quarters earlier. Mixture-of-experts architectures with effective parameter counts in the tens of trillions are being trained and released under permissive licenses, enabling widespread fine-tuning and deployment at the edge.

Robotics has crossed a critical threshold: vision-language-action models trained on internet-scale video now generalize to physical environments with minimal additional data. Deployment of general-purpose humanoid platforms has moved from prototypes to thousands of units in controlled settings, with manipulation success rates on unstructured tasks rising from under 20 % to over 80 % within the span of a single year.

Scientific discovery itself is accelerating. Protein design systems now generate novel folds and catalysts orders of magnitude faster than experimental iteration, while materials science models propose candidates that outperform decades of human-directed search. In mathematics, 2025 systems have resolved open problems that stumped the global research community for years, not through brute force but via genuinely novel proofs discovered autonomously.

Every metric – benchmark performance, economic value generated, scientific papers accelerated, lines of production code written, physical tasks automated – shows the same hockey-stick trajectory. The compounding rate is now so rapid that capabilities considered science fiction in early 2024 are routine by late 2025, and the systems scheduled for training in 2026 will make today’s models appear as limited as GPT-3 does to us now.

This is the final year in which intelligence remains a scarce resource. Starting from 2026 onward, superintelligence will be abundant, cheap, and continuously improving. The technological constraints that have defined the entire history of civilization are dissolving in real time.

We are living through the last primitive year.

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