Scale in 2036?

📰 Medium · AI

Learn how scaling AI models can lead to significant improvements in performance and potentially achieve Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI)

advanced Published 20 Jun 2026

Action Steps

Read the research paper on data limitations at https://arxiv.org/abs/2509.14786 to understand the current state of data availability
Explore the concept of physical limitations in AI development at https://medium.com/@elisacmpereira/ais-high-wall-894932a5d19e
Analyze the performance differences between 1 billion and 1 trillion parameter models to understand the impact of scaling
Consider the conjectures of achieving AGI at 1 quadrillion parameters and ASI at 1 quintillion parameters
Design and train AI models with scaling in mind to potentially achieve significant performance improvements

Who Needs to Know This

AI researchers and engineers can benefit from understanding the relationship between scaling AI models and achieving AGI and ASI, as it can inform their model development and training strategies

Key Insight

💡 Scaling AI models can lead to significant improvements in performance, and potentially achieve AGI and ASI

Key Takeaways

Learn how scaling AI models can lead to significant improvements in performance and potentially achieve Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI)

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Title: Scale in 2036?

URL Source: https://medium.com/@eternalyze0/scale-in-2036-561b69317946?source=rss------artificial_intelligence-5

Published Time: 2026-06-20T22:52:17Z

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# Scale in 2036?. The first thing people mention when you… | by Eternalyze | Jun, 2026 | Medium

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# Scale in 2036?

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The first thing people mention when you bring up scaling is data limitations. But we don’t need additional data: [https://arxiv.org/abs/2509.14786](https://arxiv.org/abs/2509.14786).

The next thing they’ll mention is physical limitations: [https://medium.com/@elisacmpereira/ais-high-wall-894932a5d19e](https://medium.com/@elisacmpereira/ais-high-wall-894932a5d19e). And these are very much real.

Lastly, they’ll bring up that intelligence isn’t about scale. But I don’t see anyone choosing 1 billion parameter models over 1 trillion parameter models.

In fact I’ll go further and make two conjectures:

**AGI at 1 quadrillion parameters.**

**ASI at 1 quintillion parameters.**

The jump between a billion and a trillion is staggering. The model goes from barely being able to solve high school math pro

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Scale in 2036?

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