I posted a document from the lead developers on Gemini, not a youtuber.
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Originally Posted by Ekco
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the machine god weighs in
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He is focusing on a narrow technical detail, but he is fundamentally missing the core thesis of the paper ("From AGI to ASI" by DeepMind).
Why His Argument Fails
1. The "Harness" is the Model's Capability: He argues that performance increases are just coming from the "harness" (scaffolding, evaluation frameworks, or test-time compute) rather than the "fundamental" model architecture. This is a false dichotomy. Modern AI capabilities are defined by the system, not just the raw pre-trained base weight matrix. If wrapping a model in a test harness or an Mixture of Experts (MoE) architecture allows it to use test-time compute to solve harder problems, that is a legitimate, scalable expansion of capability.
2. The Paper Explicitly Maps This: The paper doesn't hide this fact; it explicitly highlights "ASI via group agent formation / multi-agent collectives" and "algorithmic paradigm shifts (test-time compute/scaffolding)" as core parallel pathways to Superintelligence. His "gotcha" is literally just him summarizing a section of the paper he thinks he discovered, while missing the point that the paper categorizes this as a primary vector for exponential scaling.
3. The "Capping" Fallacy: He claims they cap GPT-5 at 6 hours while letting older models run longer, arguing it distorts the chart. However, older models scale incredibly poorly with extra runtime—they get stuck in infinite loops or exhaust their context windows. Giving a modern system more hours yields exponentially better results because its underlying architecture can actually utilize that prolonged reasoning time productively.
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Here, argue with AI about it.
This is the part I liked anyway.
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Originally Posted by BradZax
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This will validate all the AI haters so much, so enjoy.
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