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October 2, 2025. <a href="https://tecunningham.github.io/posts/2025-09-19-transformative-AI-notes.html">tecunningham.github.io/posts/2025-09-19-transformative-AI-notes.html</a>.
April 28, 2024. <a href="https://tecunningham.github.io/posts/2023-01-23-peer-effects-norms-culture-sin-taxes.html">tecunningham.github.io/posts/2023-01-23-peer-effects-norms-culture-sin-taxes.html</a>.
<title>The Influence of AI on Content Moderation and Communication</title>
@@ -3637,7 +3637,7 @@ Cunningham, Tom. 2023. <span>“The Influence of AI on Content Moderation
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and Communication.”</span> December 11, 2023. <a href="https://tecunningham.github.io/posts/2023-06-06-effect-of-ai-on-communication.html">tecunningham.github.io/posts/2023-06-06-effect-of-ai-on-communication.html</a>.
<dt>The strength of the feedback depends on TH->uplift.</dt>
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<dd>
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<p>Historically algorithmic progress has been a function of human R&D, but we now expect the AI to itself increase the rate of algorithmic progress, through increasing effective R&D: <spanclass="math display">\[
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\xymatrix@C=1.4em@R=1.4em{
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& *++[F]{\text{R\&D}_t}\ar[d]
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& *++[F]{\text{R\&D}_{t+1}}\ar[d]
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& *++[F]{\text{R\&D}_{t+2}}\ar[d]
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& \\
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\ar[r]
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& *++[F]{\text{Algorithms}_t}\ar[r]
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& *++[F]{\text{Algorithms}_{t+1}}\ar[r]\ar[ur]|{??}
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& *++[F]{\text{Algorithms}_{t+2}}\ar[r]\ar[ur] &
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}
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\]</span></p>
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<p>There is still no consensus on the key diminishing-returns parameter <spanclass="math inline">\(\beta\)</span>. The cleanest direct AI-domain estimates I found are Ho & Whitfill’s computer-vision, RL, and NLP posteriors, which are around <spanclass="math inline">\(\beta \approx 1.3\)</span>-<spanclass="math inline">\(1.6\)</span> but very noisy. By contrast, several software-intelligence-explosion calibrations imply much lower values like <spanclass="math inline">\(\beta \approx 0.15\)</span>-<spanclass="math inline">\(0.25\)</span>, while Davidson et al. summarize the software evidence as roughly <spanclass="math inline">\(\beta_S \approx 1\)</span><spanclass="citation" data-cites="ho2025explosionexperiments">Ho and Whitfill (<ahref="#ref-ho2025explosionexperiments" role="doc-biblioref">2025</a>)</span>; <spanclass="citation" data-cites="davidson2021could">Davidson (<ahref="#ref-davidson2021could" role="doc-biblioref">2021</a>)</span>; <spanclass="citation" data-cites="davidson2026automatingairesearch">Davidson et al. (<ahref="#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 <spanclass="math inline">\(r=\lambda/\beta\)</span> or <spanclass="math inline">\(r=\lambda \alpha / \beta\)</span> rather than <spanclass="math inline">\(\beta\)</span> itself. That matters because <spanclass="math inline">\(r\)</span> is informative about long-run dynamics, but it does not separately identify <spanclass="math inline">\(\beta\)</span> unless the authors also pin down <spanclass="math inline">\(\lambda\)</span> and sometimes <spanclass="math inline">\(\alpha\)</span><spanclass="citation" data-cites="erdil2024estimating">Erdil, Besiroglu, and Ho (<ahref="#ref-erdil2024estimating" role="doc-biblioref">2024</a>)</span>; <spanclass="citation" data-cites="davidson2025howquickandbigwo">Davidson and Houlden (<ahref="#ref-davidson2025howquickandbigwo" role="doc-biblioref">2025</a>)</span>.</p>
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. <ahref="https://doi.org/10.7208/9780226613475-007">https://doi.org/10.7208/9780226613475-007</a>.
Davidson, Tom, Basil Halperin, Thomas Houlden, and Anton Korinek. 2026. <span>“When Does Automating AI Research Produce Explosive Growth?”</span><ahref="https://www.basilhalperin.com/papers/shs.pdf">https://www.basilhalperin.com/papers/shs.pdf</a>.
Davidson, Tom, and Tom Houlden. 2025. <span>“How Quick and Big Would a Software Intelligence Explosion Be?”</span><ahref="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>.
Jones, Charles I. 2023. <span>“Recipes and Economic Growth: A Combinatorial March down an Exponential Tail.”</span><em>Journal of Political Economy</em> 131 (8): 1994–2031. <ahref="https://doi.org/10.1086/723631">https://doi.org/10.1086/723631</a>.
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