Mike's Notes
These data centres are very inefficient power users. The AI models are wasteful. As a result, I can see working people's power bills rising, causing more suffering.
This is a great article.
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References
- Hooker, Sara, On the Slow Death of Scaling (December 06, 2025). Available at SSRN: https://ssrn.com/abstract=5877662 or http://dx.doi.org/10.2139/ssrn.5877662
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Last Updated
04/02/2026
On the Slow Death of Scaling
Adaption Research Scientist.
Abstract
For the last decade, it has been hard to stray off the beaten path of accepted wisdom for what drives innovation. We have been held hostage to a painfully simple formula: scale model size and training data. A pervasive belief in scaling has resulted in a massive windfall in capital for industry labs and fundamentally reshaped the culture of conducting science in our field. Academia has been marginalized from meaningfully participating in AI progress and industry labs have stopped publishing. Yet, this essay will posit that the relationship between training compute and performance is highly uncertain and rapidly changing. Relying on scaling alone misses a critical shift that is underway, and ignores more interesting levers of progress. All this suggests that key disruptions lie ahead.
1 How we got here.
Source: Epoch, 2023 | Chart: 2024 AI Index report
Publication date
Figure 1: Estimated training cost of select AI models (Maslej et al., 2024). The last decade has been characterized by an explosion in the size of models and cost of participating at the frontier of research.
Many inventions are re-purposed for means unintended by their designers. Initially, the magnetron tube was developed for radar technology during World War II. In 1945, a self-taught American engineer, Percy Spencer, noticed that a chocolate bar melted in his pocket whenever he was close to a radar set. This innocuous discovery resulted in the patent for the first microwave (Zhang, 2017). In a similar vein, deep neural networks only began to work when an existing technology was unexpectedly re-purposed.
A graphical processing unit (GPU) was originally introduced in the 1970s as a specialized accelerator for video games and for developing graphics for movies and animation. In the 2000s, like the magnetron tube, GPUs were re-purposed for an entirely unimagined use case – to train deep neural networks (Chellapilla et al., 2006; Hooker, 2021b; Oh & Jung, 2004; Payne et al., 2005).
GPUs had one critical advantage over CPUs - they were far better at parallelizing matrix multiplication (Brodtkorb et al., 2013; Dettmers, 2023), a mathematical operation which dominates the definition of deep neural network layers (Fawzi et al., 2022; Davies et al., 2024). This higher number of floating operation points per second (FLOP/s) combined with the clever distribution of training between GPUs unblocked the training of deeper networks. The depth of the network turned out to be critical. Performance on ImageNet jumped with ever deeper networks in 2011 (Ciresan et al., 2011), 2012 (Krizhevsky et al., 2012) and 2015 (Szegedy et al., 2014). A striking example of this jump in compute is a comparison of the now famous 2012 Google paper which used 16,000 CPU cores to classify cats (Le et al., 2012) to a paper published a mere year later that solved the same task with only two CPU cores and four GPUs (Coates et al., 2013).
This would ignite a rush for compute which has led to a bigger-is-better race in the number of model parameters over the last decade (Canziani et al., 2016; Strubell et al., 2019b; Rae et al., 2021; Raffel et al., 2020; Bommasani et al., 2021; Bender et al., 2021). The computer scientist Ken Thompson famously said “When in doubt, use brute force.” This was formalized as the “bitter lesson” by Rich Sutton who posited that computer science history tells us that throwing more compute at a problem has consistently outperformed all attempts to leverage human knowledge of a domain to teach a model (Sutton, 2019). In a punch to the ego of every computer scientist out there, what Sutton is saying is that symbolic methods that codify human knowledge have not worked as well as letting a model learn patterns for itself coupled with ever-vaster amounts of compute.
This essay will ask: is bigger always better? For the last decade, computer science progress has been caught by our own Moore’s law (Schaller, 1997) of a painfully simple formula for innovation by adding more model parameters and data to training. Yet, this essay will posit it is far from clear that future innovation or large gains in performance will come from training compute alone. As we will see in the next section, the relationship between compute and performance is far from straightforward. Compute is changing rapidly, as fast as the technology it serves.
Source: Epoch, 2023 | Chart: 2024 AI Index report
Figure 2: Number of notable machine learning models by geography (Maslej et al., 2024). The explosion in both necessary compute and associated cost has compounded concentration of breakthroughs in a few regions of the world.
Why does answering this question matter? The pervasive belief that compute drives progress has fundamentally reshaped the culture of conducting science in our field. Academia has been left unable to participate in breakthroughs because of a lack of access to compute (Maslej et al., 2024). Compute access disparities persist by region, concentrating participation in the West and China (Longpre et al., 2023; Maslej et al., 2024; Singh et al., 2024a). The large capital investment required for compute hungry workloads has also led to a changing publication culture. Our historically very open field is increasingly closed. The pervasive view in industry labs is to limit publishing to preserve important commercial advantages (Morris & Heikkilä, 2025).
The implication that scale is the key lever for progress also pervades responsible scaling policies released by key industry players like Anthropic (Anthropic, 2023) and Open AI (OpenAI, 2023). These frameworks implicitly assume scaling is inevitable – with the only open question being how to do it responsibly. The assumption that scaling is the only reliable marker of progress extends to government interventions in policing AI. The introduction of compute thresholds as part of the EU AI act and other legislation rests on the assumption models will always trend bigger (Hooker, 2024; The White House, 2023; European Union, 2024; Linghan et al., 2024; Senate, 2024; Linghan et al., 2024) and access to compute or hardware (on Foreign Affairs, 2024; Reuters, 2024; Peppin et al., 2025) is the best indicator of increased capabilities and risk. This all makes probing the assumption that scaling is inevitable even more critical. We have re-orientated our entire field and culture of discovery around bigger is better. Is it always?
2 A shift in the relationship between compute and performance.
(a) Open Leaderboard Scores For Small Models (<13B) Over Time
Figure 3: Left: Plot of the best daily 13B or smaller model submitted to the Open LLM leaderboard over time. Even amongst comparable small sized models, performance has been growing rapidly. Right: The best small models (under 13B) submitted to the Open LLM leaderboard easily outperform far larger models. Over time there, larger models have out-performed small <13B models.
It is controversial in many circles to state that scaling is dying. This is largely because all the evidence from the last decade suggests it is sensible to keep scaling. Scaling compute unlocks larger model sizes or datasets. It is a widely favored formula because it has provided persuasive gains in overall performance. As the computer scientist Michael Jordan quipped “Today we can’t think without holding a piece of metal.” Increasing compute also conveniently fits into the cadence of quarterly industry planning, it is less risky to propose training a larger model than it is to propose an alternative optimization technique.
Relying on compute alone misses a critical shift that is underway in the relationship between scaling and performance. It is not always the case that bigger models result in better performance. The bitter lesson doesn’t explain why Falcon 180B (Almazrouei et al., 2023) is easily outperformed by far smaller open weights models such as Llama-3 8B (AI@Meta, 2024), Command R 35B (Cohere & Team, 2024), Gemma 3 27B (Team, 2024). It also doesn’t explain why Aya 23 8B (Aryabumi et al., 2024) and Aya Expanse 8B (Dang et al., 2024b) both easily outperform BLOOM 176B (Workshop et al., 2023) despite each having only 4.5% of the parameters. These are not isolated examples, but part of a systematic trend where there is no guaranty larger models consistently outperform smaller models. In Figure 3b, we plot the scores of models submitted to the Open LLM Leaderboard (Beeching et al., 2023) over the last two years. The trend is striking: over time there has been a surge in the number of small compact models that outperform far larger ones.
To understand why this is the case, we must understand what key variables have been driving gains in performance over the last decade. In an era where there are diminishing returns for the amount of compute available (Lohn & Jackson, 2022; Thompson et al., 2020), optimization and architecture breakthroughs define the rate of return for a given unit of compute. It is this rate of return which is most critical to the pace of progress and to the level of risk incurred by additional compute.
3 What influences the rate of return for compute?
In complex systems, it is challenging to manipulate one variable in isolation and foresee all implications. Throughout the 20th century doctors recommended removing tonsils in response to swelling or infection, but research has recently shown the removal may lead to higher incidence of throat cancer (Liang et al., 2023). Early televised drug prevention advertisements in the 2000s led to increased drug use rather than curbing abuse of drugs as intended (Terry-McElrath et al., 2011). In a similar vein, the belief that more compute equates with predictable gains in capabilities belies a far more complex picture. Below we explore some of the core contradictions.
3.1 Diminishing returns of increasing model size
Why do we even need extra weights in the first place? Model size is often quantified by the number of trainable weights or parameters. This metric has exploded over the last decade. Some of the first widely adopted deep neural networks like Inception (Szegedy et al., 2016) had 23 million weights. In contrast, recent releases like Qwen3-235B-A22B (Team, 2025) have 235 billion parameters. While this startling growth in model size has been driven by empirical gains from larger models, a key limitation of simply throwing more weights at a task is that the relationship between additional trainable weights and generalization remains poorly understood. A deep neural network learns and adjusts model weights during training to improve performance. When we scale the size of models, typically we are adding to the number of total weights that are learned over training. However, it is unclear why we need so many additional weights. What is particularly puzzling is that we also observe that we can get rid of most of these weights after we reach the end of training with minimal loss to overall performance. For example, it is well accepted you can completely remove the majority of trained weights (Gale et al., 2019; Li et al., 2020; Hou et al., 2020; Chen et al., 2021; Bai et al., 2020; Han et al., 2015; Evci et al., 2019; Denil et al., 2013; Ahmadian et al., 2023) in a network after training while not incurring sizable performance degradation. However, if you start training without these weights active it is impossible to reach the same end performance. If we can get rid of them afterwards, why do we need them in the first place?
Denil et al. (2014) find that a small set of weights can be used to predict 95% of weights in the network. This suggests many weights are highly correlated and there is a high degree of redundancy in the learned feature space. All of this suggests there is considerable redundancy in the size of the network. This may have more to do with the inefficiency of our learning techniques for deep neural networks, and how unstable optimization is if we start with a smaller network. If we had better learning techniques, we would probably need far smaller networks.
Increasing model size is a very costly way to learn the long tail. Deep neural networks are incredibly inefficient learners. Although deep neural networks learn common and frequent features efficiently and early in training (Agarwal & Hooker, 2020; Paul et al., 2021; Mangalam & Prabhu, 2019; Siddiqui et al., 2022; Abbe et al., 2021), these architectures require an incredible amount of compute and training time to learn infrequent features. This is because all modern networks are trained based upon minimization of average error (Goodfellow et al., 2016). Our typical training regime requires that all examples are shown the same number of times during the training (Xue et al., 2023), hence the signal of infrequent attributes is diluted in batch updates (Achille et al., 2017; Jiang et al., 2020; Mangalam & Prabhu, 2019; Faghri et al., 2020; Frankle et al., 2020; Arpit et al., 2017; Hooker et al., 2020; Hooker, 2021a). Most attributes in the real world are infrequent, part of what makes human intelligence unique is our ability to pattern match and process long tail and previously unseen instances efficiently. This is exactly where deep neural networks struggle the most. The bulk of compute during training is spent memorizing the long tail in a prohibitively costly way. It is akin to building a ladder to the moon.
3.2 Data quality reduces reliance on compute.
Models trained on better data do not require as much compute. A large body of work has emerged which shows that efforts to better curate training corpus, including de-duping (Taylor et al., 2022; Kocetkov et al., 2022), data pruning (Marion et al., 2023; Singh et al., 2024b; Sorscher et al., 2023; Albalak et al., 2024; Tirumala et al., 2023; Chimoto et al., 2024) or data prioritization (Boubdir et al., 2023; Thakkar et al., 2023) can compensate for larger models. This suggests that the number of learnable parameters is not definitively the constraint on improving performance; investments in better data quality mitigate the need for more weights (Singh et al., 2024b; Penedo et al., 2023; Raffel et al., 2020; Lee et al., 2022; D’souza et al., 2025). If the size of a training dataset can be reduced without impacting performance (Marion et al., 2023), training time is reduced. This directly impacts the amount of training time and means less compute is needed.
3.3 New algorithmic techniques compensate for compute.
Progress over the last few years has been as much due to algorithmic improvements as it has been due to compute. This includes extending pre-training with instruction finetuning to teach models instruction following (Singh et al., 2024a), model distillation using synthetic data from larger more performant "teachers" to train highly capable, smaller "students" (Team et al., 2024b; Aryabumi et al., 2024), chain-of-thought reasoning (Wei et al., 2023; Hsieh et al., 2023), increased context-length (Xiong et al., 2023), retrieval augmented generation (Pozzobon et al., 2023; Lewis et al., 2020), and preference training to align models with human feedback (Dang et al., 2024a; Ahmadian et al., 2024; Ouyang et al., 2022; Bai et al., 2022; Lee et al., 2023; Tunstall et al., 2023; Khalifa et al., 2021; Rafailov et al., 2023; Azar et al., 2023). All of these techniques compensate for the need for heavy weights or expensive prolonged training (Ho et al., 2024b). All things equal, these have been shown to dramatically improve model performance relative to a model trained without these optimization tricks given the same level of compute (Davidson et al., 2023; Hernandez & Brown, 2020; Erdil & Besiroglu, 2023; METR Team, 2023; Liu et al., 2024). We are doing significantly more with the same amount of resources.
3.4 Architecture plays a significant role in determining scalability
Architecture plays an enormous role at determining the overall rate of return in performance given a unit of compute. It also plays a crucial role in determining the ceiling of progress. The introduction of a new architecture design can fundamentally change the relationship between compute and performance (Tay et al., 2022; Sevilla et al., 2022; Ho et al., 2024a) and render any existing scaling law irrelevant. For example, the key breakthroughs in AI adoption around the world were the introduction of architectures like convolutional neural networks (CNNs) for vision (Ciresan et al., 2011; Krizhevsky et al., 2012; Szegedy et al., 2014) and Transformers for language modeling (Vaswani et al., 2023).
4 The limits of scaling laws.
Warren Buffett once said,
“Don’t ask the barber if you need a haircut.”
In the same vein, don’t ask a computer scientist or economist whether they can predict the future. The temptation to say yes often overrides a necessary humility about what can and cannot be predicted accurately. One such area where hubris has overridden common sense is attempts to predict the relationship between scale and performance in the form of scaling laws (Kaplan et al., 2020; Hernandez et al., 2021; Dhariwal et al., 2021) which either try and predict how a model’s pre-training loss scales (Bowman, 2023) or how downstream properties emerge with scale.
Figure 4: Bytes Magazine Cover, Volume 5, 1980. Compute is rarely the only determinant of progress. Data quality, instructionfinetuning, preference training, retrieval augmented networks, enabled tool use, chain-of-thought reasoning, increased context-length are all algorithmic techniques which add little or no training compute but result in significant gains in performance.
Scaling laws emerged as a symptom of our extreme trust in compute being one of the primary catalysts of progress. It has entered the mainstream discussion as a catchall phrase to justify everything from massive capital investments in AI startups to policy decisions about compute thresholds. It is easy to understand why scaling laws are enticing, if you can predict how capabilities change with the amount of compute you can justify capital expenditures on compute. However, while performance typically increases with scaling, our track record of predicting exactly how much it does is surprisingly lacking. This means that it is difficult to scientifically determine a rate of return for a given level of compute.
One of the biggest limitations of scaling laws is that they have only been shown to hold when predicting a model’s pretraining test loss (Bowman, 2023), which measures the model’s ability to correctly predict how an incomplete piece of text will be continued. Indeed, when actual performance on downstream tasks is used, the results are often murky or inconsistent (Ganguli et al., 2022; Schaeffer et al., 2023; Anwar et al., 2024a; Ganguli et al., 2022; Schaeffer et al., 2024; Hu et al., 2024). Ironically, the term emerging properties is often used to describe this discrepancy (Wei et al., 2022; Srivastava et al., 2023): a property that appears “suddenly” as the complexity of the system increases and cannot be predicted. Somewhat humorously, the acceptance that there are emergent properties which appear out of nowhere is another way of saying our scaling laws don’t actually equip us to know what is coming.
Even when limited to predicting test loss, there have been issues with replicability of scaling results under slightly different assumptions about the distribution (Besiroglu et al., 2024; Anwar et al., 2024b). Research has also increasingly found that many downstream capabilities display irregular scaling curves (Srivastava et al., 2023) or non power-law scaling (Caballero et al., 2023). For complex systems that require projecting into the future, small errors end up accumulating due to time step dependencies being modeled. This makes accurate predictions of when risks will emerge inherently difficult, which is compounded by the small sample sizes that are often available for analysis. Each data point is a model, and computation cost means scaling “laws” are frequently based upon analysis of less than 100 data points (Ruan et al., 2024)). This means that many reported power law relationships can lack statistical support and power (Stumpf & Porter, 2012). The reliability of the scaling laws varies considerably by domain. For example, code-generation has shown fairly predictable power law scaling across 10 orders of magnitude of compute (Hu et al., 2024; Anwar et al., 2024a). However, other capabilities have been shown to scale far more erratically (Srivastava et al., 2023; Caballero et al., 2023).
Scaling laws may be useful for planning training runs because they hold well when architecture, optimization and data quality stay the same. These tend to be short term changes in a controlled regime. However, scaling laws have not stood up to rigor when extrapolated over even medium time horizons (Stumpf & Porter, 2012). The failure of scaling laws supports the takeaway that scaling compute is far from a straightforward axis of progress. Indeed, frontier AI companies which place disproportionate emphasis on scaling laws are likely under-investing in other directions of innovation which will unlock future gains.
5 The way forward.
In folklore, the silver bullet was one of the few techniques that was an effective defense not only against werewolves, but also a protection against vampires and witches. This led to the term silver bullet to describe an intervention that solves many things at once. In computer science, we have treated compute as our silver bullet.
We are observing a bifurcation in compute trends. On the one hand, at least in the short term, models are likely to continue to get bigger as we attempt to squeeze more out of our dying architecture. On the other hand, the relationship between compute and performance is increasingly strained and hard to predict (Niu et al., 2024).
Figure 5: It is a fun time to be a computer scientist as our levers for teaching models how to think are changing rapidly. These new techniques are often far more efficient and many don’t even require gradient updates. Many of these spaces are also under-explored and will evolve rapidly over the next few years.
So where should we go next? The frontier labs which will lead in innovation will not bet on compute alone. Indeed, the most interesting axes of progress are due to fundamental paradigm shifts in the optimization spaces available to computer scientists. One key aspect that makes this era different is the expanded set of tools computer scientists must optimize. This will change a great deal about where computer scientists spend time and the nature of discovery itself. I will include some thoughts below on the most exciting to explore.
5.1 New Optimization spaces
Gradient free exploration Increasingly, a lot of computation is spent outside of training to improve performance of a model. Traditionally, if you wanted higher performance from a machine learning model, you paid for it with more training or data or parameters. A key departure from this is the recent emphasis on scaling up compute at inference time rather than at training time (Khairi et al., 2025; Hooker, 2024; Wei et al., 2023; Hsieh et al., 2023; Wang et al., 2023; Mora et al., 2025). These strategies which include search, tool use, agentic swarms and adaptive compute allow for improvements in performance by spending more compute without any alterations to the model itself. In a radical departure from the last 30 years of AI progress, many of these techniques are gradient free involving no updates to the parameters in order to induce changes in performance. The limited work to-date which has evaluated a subset of ‘inference-time compute” improvements estimates these can impart gains between 5x and 20x of base level posttraining performance (Davidson et al., 2023). Relative to the large volumes of compute needed for pre-training these techniques have minimal footprint (Villalobos & Atkinson, 2023; Huang et al., 2022; D’souza et al., 2025).
A malleable data space Historically, high-quality labeled data has been costly to curate due to, amongst other factors, scarcity of available data (Singh et al., 2024a; 2025) and financial cost (Gilardi et al., 2023; Boubdir et al., 2023). This high cost has precluded adapting training sets “on-the-fly” to increase coverage or task diversity. As a result, researchers have often treated datasets as static representations of the world, far from the rich, ever-evolving environment we navigate as humans. These frozen snapshots in time like MNIST (Deng, 2012), ImageNet (Deng et al., 2009), SQuAD (Rajpurkar et al., 2016) were the foundation upon which progress in AI has been built.
The cost of having historically static and rigid training datasets is extremely high. Models perform better on the distribution they are trained to mimic (Schwartz et al., 2022; Vashishtha et al., 2023; Khondaker et al., 2023). At inference time, data points are not equally relevant, but it is often prohibitively expensive to go back and change the training distribution for each individual inference request. Hence, there is a mismatch in the distribution at training and inference time: training time distribution is often determined by ease of access to prior data collections and data augmentation efforts, while at inference time, new use cases might be underrepresented in the data but highly relevant to the user.
A fundamental revolution is underway where the cost of generating synthetic data is now low enough that we can treat the data space as malleable and something which can be optimized. We can steer synthetic data towards desirable properties (Shimabucoro et al., 2024; Dash et al., 2025), and make previously invisible worlds with limited data coverage more visible (Aryabumi et al., 2024; Üstün et al., 2024; Team et al., 2024a; Mora et al., 2025). The importance of this is hard to overstate, with a malleable data space it is possible to target parts of the distribution that are less frequent. It is also a radical departure from assumptions that have guided machine learning fundamentals such as assuming IID (independent and identically distributed) samples. We are now able to intentionally skew the distribution towards what we hope to represent, rather than accepting a random sample of the world as it is.
The role of design and interface The most intelligent system will increasingly be defined by building an algorithm that can interact with the world. This means for the first time researchers who care about intelligence need also be obsessed with how a model interacts. What was previously the narrow purview of UX designers, artists and human computer interaction specialists, should now be of great interest to all computer scientists. Increasingly, progress at the frontier will require building a system involving multiple components rather than a single algorithm to rule them all.
5.2 Future odds of a return to scaling
Does this mean we will never return to scaling? As long as we are stuck with transformers as an architecture it doesn’t make sense to keep scaling compute. Our current architecture shows all the signs of plateauing in returns from additional compute. While progress has revolved around deep neural networks for the last decade, there is much to suggest that the next significant step forward will require an entirely different architecture. As our models interact with the world, we need new ways to mitigate catastrophic forgetting, where performance deteriorates on the original task because new information interferes with previously learned behavior (Mcclelland et al., 1995; Pozzobon et al., 2023). Deep neural networks are particularly poor at continual learning because of our reliance on global updates, which leads to more stable training but doesn’t allow for specialization of knowledge in similar ways to what we have with regions of the brain.
What does the slow death of scaling training compute mean for the environmental impact of AI? It is important to make a distinction between the shifting trends between compute and performance, and overall computational overhead of AI as a whole. While we will see ever smaller, more performant models – AI workloads will also be deployed in many more settings. This means that this essay should not be taken as a position that the overall environmental impact and energy cost of AI is not a formidable problem. This caveat is important to make, because the majority of energy requirements of AI workloads is not in training, but instead the cost to productionize an ML workload and serve it to billions of users. Even if model size is trending smaller, the widespread adoption of AI means overall energy requirements will likely continue to rise and is far from negligible (Strubell et al., 2019a; Schwartz et al., 2020; Derczynski, 2020; Patterson et al., 2021; Luccioni et al., 2025; Wu et al., 2022; Treviso et al., 2023).
5.3 Parting thoughts
It is meaningful that the statement that we can’t rely solely on compute is gaining recognition in the mainstream conversation. This essay brings together previous writing on several topics that are timely: the changing relationship we have with compute, our expanded optimization spaces and how scaling has irrevocably changed out research culture. One thing is certain, is the less reliable gains from compute makes our purview as computer scientists interesting again. We can now stray from the beaten path of boring, predictable gains from throwing compute at the problem. It is fitting to conclude with a quote from Alan Turing
“We can only see a short distance ahead, but we can see plenty there that needs to be done.”
5.4 Acknowledgments
This work draws upon reflections I have shared in talks over the last few years with some of my existing writing on the topic (Hooker, 2020; 2024). Decreasing returns to scaling has started to gain prominence in the wider conversation, which has resulted in renewed interest in these works. A warm thank you to Sudip Roy, Hugo Larochelle and John Dang who read and provided feedback on a version of this draft. Thanks to Thomas Euyang who helped with an earlier version of the design for Figure 5.
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