gapnium

Tier-2 coefficient · weight 0.10

gapnium.compute

The physical substrate: chips, data centres, energy.

Now0.2630
12 months+0.0060
Since 2020+0.123

Hover the chart to read values · click a marker to open its articles

What it measures

Training and inference capacity available to frontier AI: accelerator generations, gigawatt-scale data-centre build-outs, capital committed, and algorithmic efficiency gains that make the same hardware go further.

Evidence the sorting agent looks for

  • New accelerator generations
  • Committed data-centre capacity (GW, $)
  • Training-run scale (FLOP)
  • Algorithmic efficiency results

What 1.0 would mean

Compute is no longer the binding constraint on AI progress; capacity grows as fast as AI systems can usefully consume it.

Feedback on this coefficient

Events that moved gapnium.compute

  1. Claude Opus 5.5: frontier performance at lower cost

    Opus 5.5 reports 66.4% on Terminal-Bench 4.0, 81.8% on OSWorld 2.1 and matches Fable 5.1 on most work for ~40% less than Opus 5.

    Autonomy +0.016 Automation +0.010 Compute +0.006
    Anthropic · Introducing Claude Opus 5.5

  2. AlphaEvolve improves algorithms — and Google's own AI stack

    A Gemini-powered coding agent discovers a faster 4×4 complex matrix multiplication and recovers ~0.7% of Google's worldwide compute.

    Recursion +0.025 Discovery +0.015 Compute +0.005
    Google DeepMind · AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

  3. Stargate: $500 billion AI infrastructure project

    OpenAI, SoftBank, Oracle and MGX announce plans to invest up to $500B in US AI data centres over four years.

    Compute +0.030
    OpenAI · Announcing The Stargate Project

  4. DeepSeek-R1: open reasoning model at a fraction of the cost

    DeepSeek releases an open-weights reasoning model performing on par with o1 on maths and code.

    Compute +0.012 Cognition +0.008 Automation +0.005
    DeepSeek · DeepSeek-R1 Release

  5. OpenAI o1 introduces reasoning models

    o1 "thinks" before answering using RL-trained chain of thought, jumping from 13% to 83% on an IMO qualifying exam.

    Cognition +0.040 Recursion +0.010 Compute +0.008
    OpenAI · Learning to reason with LLMs

  6. Nvidia announces Blackwell

    Nvidia's Blackwell platform promises large gains in training and inference throughput for trillion-parameter models.

    Compute +0.020
    NVIDIA Newsroom · NVIDIA Blackwell Platform Arrives to Power a New Era of Computing

  7. Meta releases LLaMA, open weights spread

    Meta's LLaMA models match much larger systems, and their weights quickly spread to the open research community.

    Automation +0.006 Compute +0.004
    Meta AI · Introducing LLaMA: A foundational, 65-billion-parameter large language model

  8. ChatGPT launches

    OpenAI releases ChatGPT; it becomes the fastest-growing consumer application in history.

    Automation +0.035 Cognition +0.010 Compute +0.010
    OpenAI · Introducing ChatGPT

  9. PaLM scales to 540B parameters with reasoning gains

    Google's PaLM shows breakthrough performance on multi-step reasoning with chain-of-thought prompting.

    Cognition +0.012 Compute +0.006
    Google Research · Pathways Language Model (PaLM): Scaling to 540 Billion Parameters for Breakthrough Performance

  10. Chinchilla rewrites the scaling laws

    DeepMind shows most large models were under-trained; a 70B model on more data beats much larger ones.

    Compute +0.012 Recursion +0.003
    arXiv · Training Compute-Optimal Large Language Models

  11. GPT-3 shows few-shot learning at scale

    OpenAI's 175B-parameter GPT-3 performs new tasks from a handful of examples in the prompt, without fine-tuning.

    Cognition +0.030 Compute +0.010
    arXiv · Language Models are Few-Shot Learners