DeepMind's own index puts AI progress at 0.1 points a month even with AI writing over 75% of its code

The 30% is the figure most likely to be misread. Crypto Briefing, summarising the report, gives the monthly gain for public Gemini models as 0.091 index points before April and 0.118 after ^[26]. That is 0.027 points, or about 30% faster ^[27]. On a chart it looks like acceleration. DeepMind's analysis found no substantial difference between the two ^[26], working from about six months of post-April data ^[28]. Neither write-up reproduces the confidence interval around either slope, so a reader cannot tell a flat line from a test with too little data to see a 30% change.

Argon is the second puzzle. At roughly 3.3 it sits about 0.4 points above Gemini 3.8 Flash, which scored just above 2.9 in September (Argon was unveiled on September 30) ^[5][17][21]. Nearly every release hugs the trend line and Argon's error bar stretches low enough to touch it ^[6], so if Flash is on the line, 0.4 points at 0.1 a month is four months of progress ^[4][21]. Haydn Belfield, a DeepMind researcher who worked on the assessment, wrote that Gemini 4 Argon sits above the trend but that the team traces this to increased compute and organizational investment, not to AI-driven acceleration ^[7][8]. On that account the four months were paid for, and the next four will have to be paid for the same way.

The 75% and the 88% count different things. More than 75% of production code at DeepMind is AI-written ^[9], yet only 11.9% of pull requests are fully delegated ^[10], and 100 minus 11.9 is 88.1 ^[22], close to the roughly 88% that Belfield says still need multi-turn human steering or human code authorship ^[11]. The delegated share is rising, by 4.4 points between April and September ^[23]. A review of 1,389 Argon agent logs found that the bigger the task, the less often the model finished it alone ^[13].

The tripwire sits further out. DeepMind treats a 90% score on its research engineering tasks as a red flag for full automation ^[16]. The step from Gemini 3.7 Flash to Argon was 23 points ^[24], and the remaining 40 points to that line is about 1.7 such steps ^[25]. Two steps of that size would cross it. The two scores come from different model lines, so the yardstick is crude.

Three outcomes would change the picture. The 30% could be real and turn significant as the sample grows. The index could hold near 0.1 while a larger compute budget lifts the next model above the line again; that would look like acceleration and be spending. Or the research-engineering score could cross 90% while the index stays flat.

We think the report earns a narrow conclusion: inside DeepMind, through September, no acceleration beyond what compute and investment explain ^[1]. "We therefore conclude that the risk from AI-driven ML R&D is currently low," Belfield wrote ^[18]. The counter-thesis is a short record, an internal index ^[3] with about six months of post-April data ^[28], and one lab's models cannot settle a claim about the whole industry. Crypto Briefing put the proof this way: a sustained break above roughly 0.1 points a month without a matching jump in compute ^[29]. We would add a score above 90% on the research-engineering tasks.