<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Gwern.net — AI: Scaling Hypothesis &amp; Forecasting</title>
    <link>https://gwern.net</link>
    <description>Pages from gwern.net in the 'AI: Scaling Hypothesis &amp; Forecasting' section</description>
    <lastBuildDate>Sat, 07 Mar 2026 22:52:09 +0000</lastBuildDate>
    <generator>GwernRSSBuilder/1.0</generator>
    <atom:link href="https://gwern.net/feed.xml" rel="self" type="application/rss+xml"/>
    <item>
      <title>AI Cannibalism Can Be Good, by Gwern · Gwern.net</title>
      <link>https://gwern.net/blog/2025/ai-cannibalism</link>
      <guid isPermaLink="true">https://gwern.net/blog/2025/ai-cannibalism</guid>
      <description>Training new LLMs on old LLM outputs is a good way to get a lot more data and recycle compute, and is not paradoxically self-defeating nor necessarily harmful.</description>
      <author>Gwern</author>
      <pubDate>Sun, 27 Apr 2025 00:00:00 +0000</pubDate>
      <dc:date>2025-04-27T00:00:00Z</dc:date>
      <category>essay</category>
      <category>ai/nn/sparsity/knowledge-distillation</category>
      <category>ai/scaling</category>
      <category>reinforcement-learning/exploration/active-learning</category>
    </item>
    <item>
      <title>Scaling Hypothesis Revisited: GPT-3’s 2^(nd) Anniversary &amp; Looking Forward 2 Years · Gwern.net</title>
      <link>https://gwern.net/scaling-hypothesis-revisited</link>
      <guid isPermaLink="true">https://gwern.net/scaling-hypothesis-revisited</guid>
      <description>Revisiting GPT-3 and my ‘Scaling Hypothesis’ 2 years: it holds up and scaling has been too successful to be strangled in th ecrub. I speculate about the immediate future of scaling for 2023–2024 and beyond.</description>
      <author>Gwern</author>
      <pubDate>Sat, 28 May 2022 00:00:00 +0000</pubDate>
      <dc:date>2025-03-17T00:00:00Z</dc:date>
      <category>essay</category>
    </item>
    <item>
      <title>Scaling ‘Diminishing Returns’, by Gwern · Gwern.net</title>
      <link>https://gwern.net/blog/2024/diminishing-returns</link>
      <guid isPermaLink="true">https://gwern.net/blog/2024/diminishing-returns</guid>
      <description>What do we mean by dimishing returns in scaling? Returns have always missed— what it really means is &lt;em&gt;worse&lt;/em&gt; than extrapolation of the known scaling power laws.</description>
      <author>Gwern</author>
      <pubDate>Thu, 14 Nov 2024 00:00:00 +0000</pubDate>
      <dc:date>2024-11-19T00:00:00Z</dc:date>
      <category>essay</category>
    </item>
    <item>
      <title>The State of Chinese AI, by Gwern · Gwern.net</title>
      <link>https://gwern.net/blog/2024/china-dl</link>
      <guid isPermaLink="true">https://gwern.net/blog/2024/china-dl</guid>
      <description>N/A</description>
      <author>Gwern</author>
      <pubDate>Fri, 15 Nov 2024 00:00:00 +0000</pubDate>
      <dc:date>2024-11-19T00:00:00Z</dc:date>
      <category>essay</category>
    </item>
    <item>
      <title>Hardware Hedging Scaling Risks, by Gwern · Gwern.net</title>
      <link>https://gwern.net/blog/2024/hardware-hedging</link>
      <guid isPermaLink="true">https://gwern.net/blog/2024/hardware-hedging</guid>
      <description>N/A</description>
      <author>Gwern</author>
      <pubDate>Thu, 22 Aug 2024 00:00:00 +0000</pubDate>
      <dc:date>2024-08-26T00:00:00Z</dc:date>
      <category>essay</category>
    </item>
    <item>
      <title>The Scaling Hypothesis · Gwern.net</title>
      <link>https://gwern.net/scaling-hypothesis</link>
      <guid isPermaLink="true">https://gwern.net/scaling-hypothesis</guid>
      <description>On GPT-3: meta-learning, scaling, implications, and deep theory. The scaling hypothesis: neural nets absorb data &amp;amp; compute, generalizing and becoming more Bayesian as problems get harder, manifesting new abilities even at trivial-by-global-standards-scale. The deep learning revolution has begun as foretold.</description>
      <author>Gwern</author>
      <pubDate>Thu, 28 May 2020 00:00:00 +0000</pubDate>
      <dc:date>2022-01-02T00:00:00Z</dc:date>
      <category>essay</category>
      <category>ai/nn/transformer/gpt/3</category>
      <category>ai/scaling</category>
      <category>cs/algorithm</category>
      <category>insight-porn</category>
      <category>reinforcement-learning/safe</category>
      <category>reinforcement-learning/scaling</category>
      <category>sociology</category>
      <category>transhumanism</category>
    </item>
    <item>
      <title>ML Scaling Subreddit, by Gwern · Gwern.net</title>
      <link>https://gwern.net/blog/2020/mlscaling</link>
      <guid isPermaLink="true">https://gwern.net/blog/2020/mlscaling</guid>
      <description>N/A</description>
      <author>Gwern</author>
      <pubDate>Fri, 30 Oct 2020 00:00:00 +0000</pubDate>
      <dc:date>2021-08-25T00:00:00Z</dc:date>
      <category>essay</category>
    </item>
    <item>
      <title>Technology Forecasting: The Garden of Forking Paths · Gwern.net</title>
      <link>https://gwern.net/forking-path</link>
      <guid isPermaLink="true">https://gwern.net/forking-path</guid>
      <description>Pessimistic forecasters are overconfident in fixating, hedgehog-like, on only &lt;em&gt;one&lt;/em&gt; scenario for how they think something &lt;em&gt;must&lt;/em&gt; happen; in reality, there are always many ways through the garden of forking paths, and something needs only one path to happen.</description>
      <author>Gwern</author>
      <pubDate>Sun, 01 Jun 2014 00:00:00 +0000</pubDate>
      <dc:date>2019-06-09T00:00:00Z</dc:date>
      <category>essay</category>
      <category>ai/scaling</category>
      <category>economics/experience-curve</category>
      <category>statistics/prediction</category>
    </item>
    <item>
      <title>Slowing Moore’s Law: How It Could Happen · Gwern.net</title>
      <link>https://gwern.net/slowing-moores-law</link>
      <guid isPermaLink="true">https://gwern.net/slowing-moores-law</guid>
      <description>Weak points in the networks powering technological progress: chip factories</description>
      <author>Gwern</author>
      <pubDate>Fri, 16 Mar 2012 00:00:00 +0000</pubDate>
      <dc:date>2017-10-09T00:00:00Z</dc:date>
      <category>essay</category>
      <category>ai/scaling/economics</category>
      <category>ai/scaling/hardware</category>
      <category>crime/terrorism</category>
      <category>cs/hardware</category>
      <category>economics</category>
    </item>
  </channel>
</rss>
