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    <loc>https://data-today.net/inference-price-uneven/</loc>
    <lastmod>2026-05-04T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/inference-uneven.png</image:loc>
      <image:title>Inference got cheap, but not for the work that pays</image:title>
      <image:caption>Three declining lines on a log scale showing LLM inference price falling at 9x, 40x and 900x per year for different task difficulties</image:caption>
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  <url>
    <loc>https://data-today.net/compute-stock-doubling/</loc>
    <lastmod>2026-05-06T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/compute-stock.png</image:loc>
      <image:title>The world&#39;s AI compute now doubles every seven months</image:title>
      <image:caption>Line chart of cumulative AI compute capacity in millions of H100-equivalents rising steeply from 2022 to 2025</image:caption>
    </image:image>
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  <url>
    <loc>https://data-today.net/context-windows-30x/</loc>
    <lastmod>2026-05-09T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/context-windows.png</image:loc>
      <image:title>Context windows grew 30x a year. Your retrieval stack noticed</image:title>
      <image:caption>Line chart of LLM context window maxima in thousands of tokens climbing from 8k in 2023 to about a million in 2025</image:caption>
    </image:image>
  </url>
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    <loc>https://data-today.net/consumer-hardware-lag/</loc>
    <lastmod>2026-05-12T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/consumer-hardware.png</image:loc>
      <image:title>Last year&#39;s frontier model now runs on a laptop</image:title>
      <image:caption>Two rising lines showing frontier capability and consumer-hardware capability, with the consumer line trailing by about eight months</image:caption>
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  </url>
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    <loc>https://data-today.net/gigawatt-buildout/</loc>
    <lastmod>2026-05-15T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/datacenter-buildout.png</image:loc>
      <image:title>The gigawatt build-out is the real AI race now</image:title>
      <image:caption>Bar chart comparing AI data center capacities in thousands of H100-equivalents, with a planned 2027 site towering over current ones</image:caption>
    </image:image>
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    <loc>https://data-today.net/open-models-default/</loc>
    <lastmod>2026-05-15T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/open-models-hero.png</image:loc>
      <image:title>Open-weight models closed the gap to 1.7 points</image:title>
      <image:caption>Line chart showing the performance gap between top open-weight and closed AI models shrinking from 8 to under 2 percentage points</image:caption>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/agentic-cancellation-cliff/</loc>
    <lastmod>2026-05-18T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/agentic-cancellation.png</image:loc>
      <image:title>Most agentic AI projects will be cancelled before they ship</image:title>
      <image:caption>Descending bar chart showing agentic AI projects thinning out from pilot to production across four stages</image:caption>
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  </url>
  <url>
    <loc>https://data-today.net/without-ai-quit-my-job/</loc>
    <lastmod>2026-05-19T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <image:image>
      <image:loc>https://data-today.net/posts/jobs-chart.png</image:loc>
      <image:title>How cheap inference made the one-person studio viable</image:title>
      <image:caption>Line chart of LLM inference price collapsing at a fixed performance level from late 2022 to late 2024</image:caption>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/training-cost-vs-efficiency/</loc>
    <lastmod>2026-05-21T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <image:image>
      <image:loc>https://data-today.net/posts/cost-vs-efficiency.png</image:loc>
      <image:title>Training costs rise 3.5x a year. Efficiency is the only brake</image:title>
      <image:caption>Two rising lines comparing frontier training cost growth at 3.5x per year against compute efficiency gains at 3x per year</image:caption>
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  </url>
  <url>
    <loc>https://data-today.net/scaling-curve-extrapolate/</loc>
    <lastmod>2026-05-22T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/compute-scatter.png</image:loc>
      <image:title>The scaling curve nobody wants to extrapolate</image:title>
      <image:caption>Scatter plot of model capability against training compute on a log scale with a fitted regression line</image:caption>
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  <url>
    <loc>https://data-today.net/compute-geography/</loc>
    <lastmod>2026-05-25T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
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      <image:loc>https://data-today.net/posts/compute-geography.png</image:loc>
      <image:title>Three-quarters of the world&#39;s AI compute sits in one country</image:title>
      <image:caption>Bar chart of global GPU cluster performance share by country, with the United States dominating at about 75 percent</image:caption>
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  </url>
  <url>
    <loc>https://data-today.net/everyone-automated-everything/</loc>
    <lastmod>2026-05-26T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/automation-hero.png</image:loc>
      <image:title>Everyone automated everything. Then they hired more people.</image:title>
      <image:caption>Bar chart of the share of organisations using AI rising from about 50 percent in 2022 to 78 percent in 2024</image:caption>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/benchmark-heatmap-explainer/</loc>
    <lastmod>2026-05-28T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/benchmark-heatmap.png</image:loc>
      <image:title>The model leaderboard is tightening to a photo finish</image:title>
      <image:caption>Heatmap grid of model performance across task categories, with brighter cells marking stronger relative scores</image:caption>
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  <url>
    <loc>https://data-today.net/hardware-value-per-dollar/</loc>
    <lastmod>2026-05-28T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/hardware-value.png</image:loc>
      <image:title>AI chips give 24x more per dollar, if you can afford the sticker</image:title>
      <image:caption>Bar chart of AI chip performance per dollar rising across four generations from the P100 baseline to 24x at the GB300</image:caption>
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  <url>
    <loc>https://data-today.net/agents-working-too-hard/</loc>
    <lastmod>2026-05-30T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/agent-throughput-line.png</image:loc>
      <image:title>Your agents are working too hard</image:title>
      <image:caption>Bar chart of how often developers use AI agents at work, with the largest group reporting no plans to adopt them</image:caption>
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  <url>
    <loc>https://data-today.net/capability-acceleration/</loc>
    <lastmod>2026-05-31T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/capability-accel.png</image:loc>
      <image:title>AI capability stopped slowing down. It sped up after 2024</image:title>
      <image:caption>Line chart of model capability over time bending upward after 2024, showing acceleration rather than a plateau</image:caption>
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  </url>
  <url>
    <loc>https://data-today.net/labs-promise-agi-consultants/</loc>
    <lastmod>2026-05-31T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/model-costs-chart.png</image:loc>
      <image:title>The bill for frontier AI is now measured in gigawatts</image:title>
      <image:caption>Line chart of cumulative AI compute stock growing about 3.4 times per year from 2022 to 2025</image:caption>
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  <url>
    <loc>https://data-today.net/vibecoding-to-production/</loc>
    <lastmod>2026-06-02T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/vibecoding-hero.png</image:loc>
      <image:title>Vibe coding is loud online and rare in real codebases</image:title>
      <image:caption>Bar chart of the most common developer frustrations with AI coding tools, led by output that is almost right but not quite</image:caption>
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  <url>
    <loc>https://data-today.net/debate-data-cleaning/</loc>
    <lastmod>2026-06-03T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/debate-data-cleaning.png</image:loc>
      <image:title>Multi-agent debate needs a boring data-cleaning cop</image:title>
      <image:caption>Multi-agent debate effects in data cleaning: generation down 1.6 to 15.5 points, detection up 27.4 points.</image:caption>
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  <url>
    <loc>https://data-today.net/google-ai-opt-out-lever/</loc>
    <lastmod>2026-06-03T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/google-ai-opt-out-lever.png</image:loc>
      <image:title>Google AI opt out gives publishers a real lever now</image:title>
      <image:caption>Google AI opt out chart showing lower click rates when AI summaries appear in search results</image:caption>
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    <loc>https://data-today.net/unit-distance-openai-proof/</loc>
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      <image:loc>https://data-today.net/posts/unit-distance-openai-proof.png</image:loc>
      <image:title>Unit distance proof moves AI past clever math demos</image:title>
      <image:caption>Unit distance proof comparison of known asymptotic exponents: linear grid at 1, Sawin lower bound at 1.014, upper bound at 1.333</image:caption>
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    <loc>https://data-today.net/articles/</loc>
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    <loc>https://data-today.net/author/lars-cornelissen/</loc>
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