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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:image>
      <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>
    </image:image>
  </url>
  <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>
  </url>
  <url>
    <loc>https://data-today.net/context-windows-30x/</loc>
    <lastmod>2026-05-09T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <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>
  <url>
    <loc>https://data-today.net/consumer-hardware-lag/</loc>
    <lastmod>2026-05-12T00:00:00.000Z</lastmod>
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    <image:image>
      <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>
  <url>
    <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>
  </url>
  <url>
    <loc>https://data-today.net/open-models-default/</loc>
    <lastmod>2026-05-15T00:00:00.000Z</lastmod>
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    <image:image>
      <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:image>
      <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/without-ai-quit-my-job/</loc>
    <lastmod>2026-05-19T00:00:00.000Z</lastmod>
    <changefreq>weekly</changefreq>
    <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>
    <changefreq>weekly</changefreq>
    <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/scaling-curve-extrapolate/</loc>
    <lastmod>2026-05-22T00:00:00.000Z</lastmod>
    <changefreq>weekly</changefreq>
    <priority>0.8</priority>
    <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>
    </image:image>
  </url>
  <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>
    <image:image>
      <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/everyone-automated-everything/</loc>
    <lastmod>2026-05-26T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/hardware-value-per-dollar/</loc>
    <lastmod>2026-05-28T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <image:image>
      <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/agents-working-too-hard/</loc>
    <lastmod>2026-05-30T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/capability-acceleration/</loc>
    <lastmod>2026-05-31T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <image:image>
      <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/labs-promise-agi-consultants/</loc>
    <lastmod>2026-05-31T00:00:00.000Z</lastmod>
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    <priority>0.8</priority>
    <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>
    </image:image>
  </url>
  <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>
    </image:image>
  </url>
  <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>
    </image:image>
  </url>
  <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>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/unit-distance-openai-proof/</loc>
    <lastmod>2026-06-03T00:00:00.000Z</lastmod>
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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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  </url>
  <url>
    <loc>https://data-today.net/audit-then-score-ground-truth/</loc>
    <lastmod>2026-06-04T00:00:00.000Z</lastmod>
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      <image:loc>https://data-today.net/posts/audit-then-score-ground-truth.png</image:loc>
      <image:title>Audit then score turns AI ground truth into a process</image:title>
      <image:caption>Audit then score benchmark accuracy rises from 60.8 percent in Round 0 to 80.4 percent in Round 1, 85.3 percent in Round 2, and 90.9 percent in Round 3.</image:caption>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/generalist-agents-data-curation/</loc>
    <lastmod>2026-06-04T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/generalist-agents-data-curation.png</image:loc>
      <image:title>Generalist agents hit data curation’s hard budget wall</image:title>
      <image:caption>Donut visualization for generalist agents in data curation showing a scaffolded agent using 10 percent of the published baseline data budget and avoiding 90 percent.</image:caption>
    </image:image>
  </url>
  <url>
    <loc>https://data-today.net/virtual-power-plants-data-centers/</loc>
    <lastmod>2026-06-04T00:00:00.000Z</lastmod>
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    <image:image>
      <image:loc>https://data-today.net/posts/virtual-power-plants-data-centers.png</image:loc>
      <image:title>Virtual power plants meet AI’s data center power wall</image:title>
      <image:caption>Virtual power plants capacity scale for AI data centers showing Texas large load threshold at 75 megawatts, Google Voltus VPP at 100 megawatts, and Duke flexible load estimate at 100,000 megawatts.</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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    <loc>https://data-today.net/subscribe/</loc>
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