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Can AI Scaling Continue Through 2030? (Epoch AI)
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High(4)High quality. Established institution or organization with editorial oversight and accountability.
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Epoch AI is a leading research organization tracking AI trends; this analysis is widely cited in discussions about future AI capabilities trajectories and is relevant to forecasting transformative AI timelines.
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Importance: 72/100blog postanalysis
Summary
Epoch AI analyzes the key constraints and bottlenecks that could limit continued AI scaling through 2030, examining factors such as compute availability, energy infrastructure, data availability, and algorithmic progress. The analysis assesses whether current scaling trends in large language models and other AI systems can realistically be sustained over the next several years.
Key Points
- •Examines multiple potential bottlenecks to AI scaling: compute supply chains, energy infrastructure, training data exhaustion, and financial constraints.
- •Assesses whether current exponential growth in training compute can realistically continue at historical rates through 2030.
- •Considers algorithmic efficiency improvements as a potential offset to hardware and data limitations.
- •Evaluates geopolitical and supply chain risks (e.g., chip manufacturing) that could constrain scaling trajectories.
- •Provides quantitative projections and scenario analysis for AI capabilities development over the next several years.
Cited by 7 pages
| Page | Type | Quality |
|---|---|---|
| The Case For AI Existential Risk | Argument | 66.0 |
| Is Scaling All You Need? | Crux | 42.0 |
| AGI Development | -- | 52.0 |
| Novel / Unknown Approaches | Capability | 53.0 |
| AI Risk Critical Uncertainties Model | Crux | 71.0 |
| Epoch AI | Organization | 51.0 |
| Long-Timelines Technical Worldview | Concept | 91.0 |
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Can AI scaling continue through 2030? | Epoch AI
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Article
Can AI scaling continue through 2030?
report
Can AI scaling continue through 2030?
We investigate the scalability of AI training runs. We identify electric power, chip manufacturing, data and latency as constraints. We conclude that 2e29 FLOP training runs will likely be feasible by 2030.
Cite
Published
Aug 20, 2024
Authors
Jaime Sevilla,
Tamay Besiroglu,
Ben Cottier,
Josh You,
Edu Roldán,
Pablo Villalobos,
Ege Erdil
Resources
Source Code
Introduction
In recent years, the capabilities of AI models have significantly improved. Our research suggests that this growth in computational resources accounts for a significant portion of AI performance improvements . 1 The consistent and predictable improvements from scaling have led AI labs to aggressively expand the scale of training , with training compute expanding at a rate of approximately 4x per year.
To put this 4x annual growth in AI training compute into perspective, it outpaces even some of the fastest technological expansions in recent history. It surpasses the peak growth rates of mobile phone adoption (2x/year, 1980-1987), solar energy capacity installation (1.5x/year, 2001-2010), and human genome sequencing (3.3x/year, 2008-2015).
Here, we examine whether it is technically feasible for the current rapid pace of AI training scaling—approximately 4x per year—to continue through 2030. We investigate four key factors that might constrain scaling: power availability, chip manufacturing capacity, data scarcity, and the “latency wall”, a fundamental speed limit i
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