The Hardware Lottery
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Abstract (arXiv)
Hardware, systems and algorithms research communities have historically had different incentive structures and fluctuating motivation to engage with each other explicitly. This historical treatment is odd given that hardware and software have frequently determined which research ideas succeed (and fail). This essay introduces the term hardware lottery to describe when a research idea wins because it is suited to the available software and hardware and not because the idea is superior to alternative research directions. Examples from early computer science history illustrate how hardware lotteries can delay research progress by casting successful ideas as failures. These lessons are particularly salient given the advent of domain specialized hardware which make it increasingly costly to stray off of the beaten path of research ideas. This essay posits that the gains from progress in computing are likely to become even more uneven, with certain research directions moving into the fast-lane while progress on others is further obstructed.
Key facts. Cited by 160 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2020.
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Citing papers per year: 2021: 24; 2022: 23; 2023: 32; 2024: 29; 2025: 25; 2026: 27.
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- 2025SD2: Self-Distilled Sparse Drafters10 Apr 2025
- 2024CHAI: Clustered Head Attention for Efficient LLM Inference12 Mar 2024
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- 2022Hungry Hungry Hippos: Towards Language Modeling with State Space Modelscited by 495
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 457
- 2024The Platonic Representation Hypothesiscited by 362
- 2021High-Performance Large-Scale Image Recognition Without Normalizationcited by 359
- 2021AI and the Everything in the Whole Wide World Benchmarkcited by 340
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- 2021The Benchmark Lotterycited by 133
- 2021Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systemscited by 125
- 2022Monarch: Expressive Structured Matrices for Efficient and Accurate Trainingcited by 100
- 2021The Efficiency Misnomercited by 98
12 most cited of 147 All 147 (JSON)
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- 2026Analog-Friendly Predictive Coding without Activation Derivatives26 Sep 2026
- 2026Programs-of-Layers in LLMs through the Lens of Cortical Areas25 Sep 2026
- 2026Vision-centric generative AI models: A software-hardware perspective27 Aug 2026
- 2026CN101 - A Digital Thermodynamic Computer for Generative AI1 Aug 2026
- 2026Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks20 Jul 2026
- 2026A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing17 Jul 2026
- 2026Financing Artificial Intelligence Infrastructure: Mapping AI Infrastructure Investment and Compute Governance Across Africa24 Jun 2026
- 2026Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention24 Jun 2026
- 2026Pretraining Recurrent Networks without Recurrence4 Jun 2026
- 2026HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces31 May 2026
- 2026Training-Inference Kernel Contracts: Bounding Divergence in Post-Training and Deployment26 May 2026
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