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<atom:link href="tecunningham.github.io/index.xml" rel="self" type="application/rss+xml"/>
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<title>An Apple-Picking Model of AI R&amp;D</title>
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<dc:creator>Tom Cunningham</dc:creator>
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docs/posts/2025-09-13-recursive-self-improvement-explosion-optimization-offcuts.html

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<title>AI Recursive Self-Improvement OFFCUTS | Tom Cunningham – Tom Cunningham</title>
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<div class="quarto-title-meta-heading">Published</div>
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<p>==OFFCUTS FILE==</p>
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<p>Core model:</p>
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<p><span class="math display">\[\begin{aligned}
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\utt{\dot{A}}{algorithmic}{progress}
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&amp;&amp; \text{(progress)}\\
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<dt>The strength of the feedback depends on TH-&gt;uplift.</dt>
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<dd>
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<p>Historically algorithmic progress has been a function of human R&amp;D, but we now expect the AI to itself increase the rate of algorithmic progress, through increasing effective R&amp;D: <span class="math display">\[
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&amp; *++[F]{\text{R\&amp;D}_t}\ar[d]
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&amp; *++[F]{\text{R\&amp;D}_{t+1}}\ar[d]
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&amp; \\
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\ar[r]
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&amp; *++[F]{\text{Algorithms}_{t+1}}\ar[r]\ar[ur]|{??}
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<p>There is still no consensus on the key diminishing-returns parameter <span class="math inline">\(\beta\)</span>. The cleanest direct AI-domain estimates I found are Ho &amp; Whitfill’s computer-vision, RL, and NLP posteriors, which are around <span class="math inline">\(\beta \approx 1.3\)</span>-<span class="math inline">\(1.6\)</span> but very noisy. By contrast, several software-intelligence-explosion calibrations imply much lower values like <span class="math inline">\(\beta \approx 0.15\)</span>-<span class="math inline">\(0.25\)</span>, while Davidson et al.&nbsp;summarize the software evidence as roughly <span class="math inline">\(\beta_S \approx 1\)</span> <span class="citation" data-cites="ho2025explosionexperiments">Ho and Whitfill (<a href="#ref-ho2025explosionexperiments" role="doc-biblioref">2025</a>)</span>; <span class="citation" data-cites="davidson2021could">Davidson (<a href="#ref-davidson2021could" role="doc-biblioref">2021</a>)</span>; <span class="citation" data-cites="davidson2026automatingairesearch">Davidson et al. (<a href="#ref-davidson2026automatingairesearch" role="doc-biblioref">2026</a>)</span>.</p>
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<p>A lot of the nearby literature is actually written in terms of returns parameters like <span class="math inline">\(r=\lambda/\beta\)</span> or <span class="math inline">\(r=\lambda \alpha / \beta\)</span> rather than <span class="math inline">\(\beta\)</span> itself. That matters because <span class="math inline">\(r\)</span> is informative about long-run dynamics, but it does not separately identify <span class="math inline">\(\beta\)</span> unless the authors also pin down <span class="math inline">\(\lambda\)</span> and sometimes <span class="math inline">\(\alpha\)</span> <span class="citation" data-cites="erdil2024estimating">Erdil, Besiroglu, and Ho (<a href="#ref-erdil2024estimating" role="doc-biblioref">2024</a>)</span>; <span class="citation" data-cites="davidson2025howquickandbigwo">Davidson and Houlden (<a href="#ref-davidson2025howquickandbigwo" role="doc-biblioref">2025</a>)</span>.</p>
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<div id="ref-agrawal2019needles" class="csl-entry" role="listitem">
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Agrawal, Ajay, John McHale, and Alexander Oettl. 2019. <span>“Finding Needles in Haystacks: Artificial Intelligence and Recombinant Growth.”</span> In <em>The Economics of Artificial Intelligence: An Agenda</em>, edited by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, 149–74. Chicago, IL: University of Chicago Press. <a href="https://doi.org/10.7208/9780226613475-007">https://doi.org/10.7208/9780226613475-007</a>.
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<div id="ref-davidson2021could" class="csl-entry" role="listitem">
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Davidson, Tom. 2021. <span>“Could Advanced AI Drive Explosive Economic Growth.”</span> <em>Open Philanthropy</em> 25. <a href="https://www.openphilanthropy.org/research/could-advanced-ai-drive-explosive-economic-growth">https://www.openphilanthropy.org/research/could-advanced-ai-drive-explosive-economic-growth</a>.
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<div id="ref-davidson2026automatingairesearch" class="csl-entry" role="listitem">
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Davidson, Tom, Basil Halperin, Thomas Houlden, and Anton Korinek. 2026. <span>“When Does Automating AI Research Produce Explosive Growth?”</span> <a href="https://www.basilhalperin.com/papers/shs.pdf">https://www.basilhalperin.com/papers/shs.pdf</a>.
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</div>
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<div id="ref-davidson2025howquickandbigwo" class="csl-entry" role="listitem">
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Davidson, Tom, and Tom Houlden. 2025. <span>“How Quick and Big Would a Software Intelligence Explosion Be?”</span> <a href="https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be">https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be</a>.
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<div id="ref-erdil2024estimating" class="csl-entry" role="listitem">
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