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  "url": "https://aleximas.substack.com/p/are-we-approaching-an-intelligence",
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{"ok":true,"url":"https://aleximas.substack.com/p/are-we-approaching-an-intelligence","text":"Are we approaching an intelligence explosion?\nGhosts of Electricity\nSubscribe Sign in\nAre we approaching an intelligence explosion?\nWhat can we learn from the current evidence\nAlex Imas and Luis Garicano\nNov 13, 2026\n28\n4\n9\nShare\nDisclaimer: The analysis herein represents the authors’ own research and conclusions. It does not necessarily reflect the official stance, views, or strategic policies of their employers.\nThe most important question we are facing today is probably whether we are approaching an intelligence explosion. After a recent report released by METR using internal data from a frontier lab (Anthropic) [2], many researchers and AI analysts have worried (or hoped) that it shows AI advances are accelerating. In a post accompanying Figure 1A, METR says:\n“We believe that, as of Oct 2026, the pace of capability growth on well-specified tasks is likely about 2x what it was from Mar 2024 to Oct 2025. We’re highly uncertain and would not be surprised by 1.3x–3x.”\nAre we nearing an intelligence explosion? The METR report graphs capabilities against calendar time. But identifying an intelligence explosion requires estimating the pace of output improvements relative to inputs. An explosion requires increasing returns, where each unit of research effort generates more and more proportional progress as capability grows (i.e., the intelligence production function turns convex). In this post, we estimate the research production function and see how far we can get with the data that we have.\nWhile many of their findings are redacted, METR showed that, based on the AECI index used to measure model capabilities, AI systems are improving at about twice the pace they were before October 2025. However, research spending has increased sixfold and the technical workforce has multiplied by three (according to public filings [3, 4, 6], as explained below). Hence capability gains per dollar decreased by two-thirds, while gains per technical employee-year fell by about two-fifths. Putting the data together gives you the two graphs below.\nFigure 1. The estimated capability trend is expressed against time and cumulative R&D expenditure. Spending is accumulated from January 2024, assuming a constant daily rate within each reported financial period. The vertical marker identifies October 2025. Appendix A gives the construction .\nPanel A reproduces the METR chart from their report. Panel B shows the same METR index against cumulative research spending ( Seb Krier and Ramez Naam published similar graphs as well). It shows that the line on this scale becomes flatter, rather than steeper. That is, the rate of progress is slowing down as a function of the inputs used, rather than increasing, as it does in calendar time.\nExponential increases in capabilities requiring exponential increases in resources is at the heart of the original conceptualization of scaling laws. But the scaling laws are distinct from an intelligence explosion, which requires increasing returns to resources, i.e., fewer inputs to generate the same level of output. The difference between these two possibilities has important implications for humanity: if maintaining growth in capabilities requires ever-increasing research effort, eventually the gains in capabilities will necessarily have to slow down. Capital and the supply of chips and power bound what a lab can spend. Progress does not stop in this case, but slows down to the pace that these constraints allow. Only an intelligence explosion escapes this arithmetic.\nThis post aims to put some further structure on the data to get closer to answering the question we pose. It has three sections of increasing difficulty, and the reader can stop at any of them and jump to the organizational economics section with an almost complete picture.\nThe first section documents the relationship between research investment and measured capability. We divide the gains in capability by the research inputs that produced them. We find that progress is faster, but each “unit” of progress takes more and more resources.\nIn order to draw some inference on the intelligence acceleration, we need to put some structure on the relationship between research inputs and capabilities. In the second section we introduce a simple power law production function, consistent with the usual evidence on the scaling laws. We find that (as in Bloom et al.[1]) ideas are getting harder to find in AI research. In other words, progress would have slowed down if research inputs had been held constant. Progress sped up because inputs grew fast enough to overcome the increasing difficulty of making progress. Introducing a break in the model around October 2025 suggests a one-time step in research effectiveness rather than a continuing acceleration; this is the kind of jump the introduction of coding agents would deliver.\nThe model we introduce in Section 2 is restrictive. In the third section we generalize the production function by making it more flexible and allowing AI to do research by itself. An intelligence explosion is precisely the case where AI systems do the research that improves them. We use the extension of Aghion, Jones and Jones [14], in which AI can supply research inputs. The model tells us what an intelligence explosion would look like in the data. It is advanced material, only for those in the field or gluttons for punishment, and the reader may jump to the “Lessons from organizational economics” section and the Conclusion without wading through it. In that model, an intelligence explosion needs AI to supply enough of its own research input to more than offset the increasing difficulty of further advances. At least in the available data, we do not yet see convincing evidence that AI is supplying its own research input, which is what an explosion requires.\nIn sum, both models suggest that we are not seeing an intelligence explosion (yet!).\n1. Economic benchmark\nThe fact that maintaining the speed of advance requires ever-increasing research effort is also at the center of an important paper by Bloom, Jones, Van Reenen and Webb, “Are Ideas Getting Harder to Find?” [1], which motivates a lot of what follows. Bloom and coauthors consider three case studies. In semiconductors, under Moore’s law, chip density doubled every two years, but sustaining that pace took 18 times as much research effort in 2014 as in 1971. In agriculture, yields of corn, soybeans, cotton and wheat rose by 1.5% a year, but yield growth per unit of research effort decreased by 4–6% a year between 1969 and 2009. In medicine, drugs approved per unit of research effort fell by a factor of five between 1970 and 2014. To obtain these results, Bloom and coauthors divide R&D expenditure by skilled wages, yielding innovation per effective researcher, or per dollar of spending adjusted for skilled wages. The result is not that we are not progressing, but that each “unit” of progress took more and more resources.\nTo do the Bloom et al. exerci","chars":7000,"total_chars":30079,"offset":0,"truncated":true,"next_offset":7000}