6/15 The laws of AI scaling...
Our sixth article in AI Masterclass. Everything you needed to know about .AI Models Scaling.
Artificial intelligence has made astounding progress in recent years, much of it driven by one key idea: scale. Ever-bigger models trained on more data with more computation have led to striking improvements in performance. This trend has prompted what some call the AI Scaling Hypothesis – the notion that simply making AI models larger (in parameters and data) and training them longer might be the most effective path toward more general and powerful AI. In this thought-leadership piece, we explore the theory behind scaling, the practical hurdles it faces, and examples of how scaling has yielded cutting-edge AI systems. Throughout, we balance visionary insights with real-world considerations, drawing on recent research and commentary in the field.
Scaling Hypotheses
The Era of Scale: The past decade of AI has been defined by bigger models and more data. As one analysis put it, “The past decade of progress in AI can largely be summed up by one word: scale”. Since the deep learning revolution around 2010, state-of-the-art models have grown exponentially in size and computational requirements. This explosion in scale has accelerated in recent years, leading many in the field to believe in the AI Scaling Hypothesis – the idea that increasing computational resources and training data might be the most reliable way to advance AI towards its long-term goals. In other words, if we keep making models bigger and train them on more data, they will keep getting better in a predictable way.
Foundations of the Scaling Hypothesis: A key insight backing this hypothesis came from Rich Sutton’s essay “The Bitter Lesson” (2019). Sutton observed that for decades, AI researchers often hand-coded knowledge or specific strategies into their systems, only to see short-term gains plateau. In the long run, approaches that relied on scaling computation (e.g. through learning and search) tended to win out. The “bitter lesson” was that investing in compute and general methods yields more progress over time than human-designed fixes. This set the stage for an emphasis on scalable learning algorithms that improve as you throw more data and compute at them.
Scaling Laws: In 2020, researchers at OpenAI put the scaling hypothesis on a quantitative footing. They published “Scaling Laws for Neural Language Models,” showing how model performance follows smooth, predictable improvements as model size, dataset size, and compute are increased in tandem. Remarkably, they found that across a wide range of scales, larger models trained on more data yield lower error rates in a very regular, law-like way – often a power-law relationship. As the OpenAI team concluded:
“These results show that language modeling performance improves smoothly and predictably as we appropriately scale up model size, data, and compute. We expect that larger language models will perform better and be more sample efficient than current models.” In simple terms, if you make your model 10x bigger (and give it more data and compute proportional to that growth), you can expect a consistent boost in performance on the task of predicting text. Such scaling laws have been influential because they suggest a clear predictive roadmap: if we want better AI, one way to get there is to keep scaling up.
Emergent Capabilities: Early experiments with scaling hinted not just at better performance, but at qualitatively different capabilities emerging at scale. A prime example is OpenAI’s GPT-3, introduced in 2020. GPT-3 has 175 billion parameters – over 10× larger than its predecessor GPT-2 – making it by far the largest AI model of its time. This jump in scale brought not only numerical improvements but also something novel: few-shot learning. GPT-3 could perform tasks like translation, question-answering, or summarization without any additional training for those tasks, simply by being given a few examples in its input prompt. This was a striking departure from previous models, which almost always needed fine-tuning on task-specific data. The scale of GPT-3 endowed it with a form of generality – an emergent behavior that researchers hadn’t explicitly programmed. As the GPT-3 team noted, scaling up the model greatly improved its ability to adapt to new tasks with minimal examples, in some cases matching the performance of prior models that had been fine-tuned for those tasks. This unexpected leap prompted researchers to ask: what other capabilities might emerge if we scale AI models even further?
Such observations strengthened believers in the scaling approach. If quantity (of data and parameters) can transform into quality (of capabilities), then scaling might eventually yield highly general AI systems. This optimism is evident in the words of AI commentator Gwern Branwen, who wrote that GPT-3 – by virtue of being an order of magnitude larger than any previous model – suggested we might reach artificial general intelligence by “just keep doing the same thing, make it bigger”. This is the crux of the scaling hypothesis: that progress in AI will continue as a direct function of scale, perhaps all the way to human-level intelligence and beyond.
Implications for Research and Industry: Embracing the scaling hypothesis has major implications. For researchers, it provides a guiding star – focusing on scaling up models and datasets as a primary route to breakthroughs. For the tech industry, it has fueled an arms race in AI compute and model size. Companies are pouring resources into building ever-larger neural networks, from OpenAI’s GPT series to Google’s massive language models, because scaling laws predict consistent gains if they do so. It also shifts how we measure progress: instead of solely novel algorithms, compute and data become key ingredients of cutting-edge AI. However, not everyone agrees that endless scaling is a panacea. Some experts caution that merely increasing size might hit fundamental limits or diminishing returns, and that truly new capabilities may require new ideas beyond brute-force growth. There’s an active debate: will scaling alone eventually lead to human-like AI (or even superintelligence), or will it run out of steam? Some, including groups like EleutherAI, take the possibility of achieving AGI through scaling seriously, while others remain skeptical and point out that intelligence may require more than just big models. Regardless of where one stands, the scaling hypothesis has undeniably been a driving force in recent AI developments, providing a theoretical lens through which to view the rapid progress of AI capabilities.
Deepseek R1 Costs of Scaling
Deepseek R1 is a perfect example of how headline costs can mask a much larger, multifaceted investment. While many reports cite a $6 million price tag for its final run, that figure represents only a portion of the overall cost. Consider these factors:
New Methods Developed: Deepseek R1 introduced several algorithmic and engineering innovations designed to improve efficiency and convergence. These include dynamic resource scheduling, optimized gradient accumulation strategies, and novel data augmentation techniques that reduced the number of passes over data. Such methods not only cut down the final training cost but also minimized wasted compute in pre-production experiments.
Capital Expenditure (CapEx): The reported $6 million covers the final run on a high-end, purpose-built hardware cluster. However, the CapEx to purchase or lease the necessary hardware was a significant investment on its own—often running into tens of millions when you factor in state-of-the-art GPUs or TPUs, custom networking, and supporting infrastructure. These assets are typically amortized over multiple projects, but their upfront cost is non-trivial.
Preruns and Iterative Development: The final run is just the culmination of extensive prototyping and testing. Numerous preruns and iterative experiments are necessary to debug, optimize hyperparameters, and validate new methods. These preruns, though individually less expensive than the final run, collectively add a substantial cost, both in compute resources and in the time spent by researchers.
Human Capital: Last but not least, a considerable team of engineers and researchers was dedicated to developing Deepseek R1. The expertise required for distributed training, custom hardware setup, and the development of new algorithms represents a major cost center that is often underreported. This investment in human capital is critical for pushing the boundaries of scaling AI models.
In summary, the $6 million figure for the final run is just the tip of the iceberg. When you include hardware capex, extensive preruns, and the salaries of a highly specialized team, the total cost and complexity of bringing Deepseek R1 to fruition are significantly higher, somewhere around $200M+. This comprehensive investment underscores the challenges—and the opportunities—of scaling AI models in today’s competitive landscape.
Challenges in Scaling
Scaling up AI models has clearly delivered impressive results – but it comes with formidable challenges. As models grow from millions to billions (and now trillions) of parameters, researchers and organizations encounter technical and logistical hurdles that must be addressed. Here we discuss some of the key challenges in scaling AI, from the cost of computation to ethical and environmental concerns, and how the AI community is grappling with them.
Computational Cost: Perhaps the most immediate obstacle to scaling is the sheer amount of computation (and money) required to train and run giant models. Training a large neural network is an expensive endeavor that grows almost cubicly with model size (since you need more computation for more parameters and more data). For example, OpenAI’s GPT-3 (175 billion parameters) is estimated to have consumed on the order of 3.14×10^23 FLOPs (floating-point operations) during training – an almost astronomic number. In terms of cloud compute hours, one estimate pegged GPT-3’s training cost in the range of $500,000 to $4.6 million USD. Another analysis noted it would take 355 GPU-years on a top-of-line GPU (the Nvidia V100) to train GPT-3 just once, translating to about $4.6M in compute cost. These figures are from 2020; newer models like GPT-4, with even greater complexity, likely cost tens of millions of dollars to develop. Such costs are prohibitively high for most academic labs and startups, meaning that only industry players or well-funded initiatives can afford to explore the upper echelons of model scaling. This leads to a resource concentration in AI: cutting-edge research favors those with access to massive compute budgets.
Hardware and Infrastructure Limits: Alongside cost, there are fundamental hardware limitations when scaling models. A single modern GPU or TPU can only hold so much in memory and perform so many operations per second. As model sizes surpass hundreds of billions of parameters, no single processor can accommodate the whole network. For instance, a 175B parameter model like GPT-3 would require over a terabyte of memory just to store its weights, far exceeding the capacity of any single GPU. The solution is to distribute the model across many processors – but doing so introduces complexity in communication and synchronization. Engineering a high-performance distributed training setup is non-trivial: it involves splitting the model and data, keeping dozens or hundreds of GPUs busy in parallel, and overcoming bandwidth bottlenecks. OpenAI, for example, worked with Microsoft to co-design a supercomputer for training its latest models, linking thousands of GPU/TPU devices with specialized networking. Google’s PaLM model was trained on a cluster of 6144 TPU v4 chips, the largest such cluster ever reported. These feats require advanced software (for model parallelism, pipelining, etc.) and robust infrastructure. Even with such setups, efficient scaling is not guaranteed – some training processes don’t speed up linearly with more GPUs due to communication overheads or algorithmic bottlenecks. In short, scaling AI is not as simple as just “using more GPUs”; it demands engineering innovations to push the limits of current hardware.
Data Requirements and Efficiency: Another practical consideration is data. Bigger models typically need proportionally more data to train effectively; otherwise they risk overfitting or not reaching their potential. But we may not always have enough high-quality data for the task at hand. For example, GPT-3 was trained on essentially all of the large textual datasets available (hundreds of billions of words). Future models that are even larger may struggle to find fresh data of similar scale and quality. This has led to research on how to use data more efficiently. One intriguing result from DeepMind was the Chinchilla study (2022), which showed that many recent large models were under-trained relative to their size – they had far more parameters than the amount of data would ideally require. By training a smaller model for longer (i.e., on more data samples), Chinchilla achieved better performance than some models that were 4× larger in parameter count. In other words, just scaling up model size blindly is not optimal if you don’t also scale up the dataset. A balanced approach (more data for a moderately sized model) can outperform an indiscriminately large model. This finding has important implications: it suggests that algorithmic scaling laws have an optimal regime, and efficient scaling might mean finding the right trade-off between model size and training data. It also pushes the community to seek new data sources or clever data augmentation techniques to feed hungry models. Additionally, as models grow, so do their inference costs – running a huge model to generate outputs can be slow and expensive. This is a practical barrier to deploying scaled models widely. If an AI system is too large to run in real-time or too costly to serve at scale, it loses practical utility. This is why companies often “distill” or compress large models into smaller, faster ones for deployment, or use techniques like model pruning and quantization to make inference more efficient. As the AI Scaling Hypothesis marches forward, much effort goes into ensuring that scaling is not only about raw power but also about efficient power.
Energy Consumption and Environmental Impact: Training and deploying large AI models can consume vast amounts of electricity. This raises concerns about the environmental sustainability of the scaling race. A study by researchers from networks like CarbonTracker and others found that training a single big transformer model can emit on the order of hundreds of tons of CO2. In the case of GPT-3, one estimate is that the training process consumed 1,287 MWh of electricity and produced 502 tons of CO2 emissions – equivalent to the yearly emissions of over 100 gasoline cars. (For comparison, an average American car might emit on the order of 4.6 tons CO2 per year.) Even after training, the daily use of AI models by millions of users adds ongoing energy costs; one analysis estimated GPT-3’s daily carbon footprint could be 50 pounds of CO2 (if serving many queries). These numbers vary depending on whether renewable energy powers the data centers, but the bottom line is that larger AI models have a larger carbon footprint. This has led to calls for more transparency in reporting energy use and for improving the energy efficiency of models. Techniques like better hardware utilization, algorithmic optimizations, and switching to greener energy sources are being explored to mitigate the environmental impact. Additionally, some researchers advocate for focusing on algorithmic improvements that achieve the same performance with less compute (“green AI”), rather than purely brute-force scaling.
Accessibility and the Research Divide: The high costs and infrastructure needs of large-scale AI have also created an accessibility gap in the AI community. Training frontier models is increasingly something that only big tech companies (and a few well-funded academia or government labs) can attempt. A recent study quantified this trend: industry now dominates AI research at the cutting edge, in part because academic groups cannot afford the necessary compute and data resources. Today, the largest AI models in a given year almost always come from industry labs – 96% of the time, according to an analysis published in Science. Moreover, the typical model coming out of industry is vastly larger than those from academia; by one estimate, industry models have on average 29× more parameters than academic models in recent years. This imbalance raises concerns. If only a handful of corporate players can scale to the state-of-the-art, they might influence the research agenda towards problems that align with their business goals, possibly at the expense of curiosity-driven research or public-interest projects. It also means that knowledge about how to train and handle these large models often remains proprietary. To counter this, there have been collaborative efforts to democratize access to large models. For example, the open research collective EleutherAI and others have reproduced large language models (like GPT-Neo/GPT-J) and released them openly. More recently, Meta AI released OPT-175B, a 175 billion-parameter model, to the research community (with certain access restrictions), and the BigScience project (a worldwide collaboration of researchers) developed and open-sourced BLOOM, a 176-billion-parameter multilingual model. These initiatives aim to make scaled AI models accessible for study and use by a broader range of people, not just the tech giants. Still, the compute and energy requirements pose a real barrier – even if the model weights are available, not everyone has a spare supercomputer to run them. This has spurred interest in techniques to make large models more accessible through efficiency: e.g., better pre-training algorithms that require less compute, or ways to quickly adapt large models to tasks with minimal overhead (so smaller organizations can leverage large pre-trained models without retraining from scratch).
In summary, the path of scaling AI, while promising, comes with intertwined technical, economic, and ethical challenges. Compute and data are the fuel of scaling, but they are costly and finite; hardware can be scaled out, but not without ingenuity; and the pursuit of scale raises questions about sustainability and equitable access. As we push the frontiers of model size and performance, the AI community must innovate not only within the models (to make them more capable) but also around the models – developing new strategies to train them efficiently, share them responsibly, and apply them in ways that benefit society at large.
Successful Case Studies
Despite the challenges, a focus on scaling has led to some of the most remarkable AI achievements to date. In this section, we highlight several case studies of AI models that have been effectively scaled and the strategies behind their success. These examples illustrate how scaling theories have been put into practice, yielding systems that are not just bigger, but demonstrably more capable. We’ll look at OpenAI’s GPT-4, DeepMind’s AlphaFold, and Google’s PaLM – three very different AI models at the cutting edge – and draw lessons from each.
GPT-4: Scaling Language Intelligence
OpenAI’s GPT-4, introduced in 2023, is a milestone in scaled language models. It is the latest in the GPT series and a direct descendant of GPT-3, but far more advanced. While many technical details of GPT-4 (such as its exact size) were not publicly disclosed, OpenAI describes it as a “large multimodal model” that exhibits “human-level performance on various professional and academic benchmarks”. Indeed, one headline result was GPT-4’s performance on the Uniform Bar Exam (a test for lawyers): GPT-3.5 (ChatGPT’s earlier model) scored in approximately the bottom 10% of test-takers, whereas GPT-4’s score put it around the top 10%. In other words, in a single year the GPT model went from failing the bar to passing with a score better than many human lawyers. This dramatic improvement underscores the power of scaling combined with refinement. GPT-4’s prowess isn’t limited to law exams; it has also demonstrated high-level performance in tasks like biology Olympiads, math problems, and more, approaching or surpassing the level of human experts in several domains.
How was GPT-4 made so capable? OpenAI applied massive compute resources and new training strategies. They collaborated with Microsoft Azure to build a dedicated AI supercomputer for training, essentially building the machinery to scale before running the final training run. In fact, they treated the preceding model (GPT-3.5) as a “test run” on this infrastructure to work out bugs and ensure that scaling up would be stable. This points to a key strategy: iterate and optimize the training pipeline at a slightly smaller scale, then make the leap to the full scale model. By the time they trained GPT-4, the process was reportedly their “first large model whose training performance we were able to accurately predict ahead of time” – a testament to understanding and controlling the scaling behavior. Beyond pure scaling, OpenAI also spent considerable effort on alignment and fine-tuning: they used human feedback and an adversarial testing program over months to refine GPT-4’s behavior, focusing on factual accuracy and adherence to ethical guardrails. This indicates that as models scale, guiding their behavior becomes as important as creating their raw capabilities – otherwise we risk simply creating a very powerful but uncontrolled system. The takeaway from GPT-4 is that successful scaling is not just about adding GPUs; it required careful engineering (custom hardware setups), monitoring (to keep the giant training run on track), and post-training alignment. The result is a model that leverages the benefits of scale – broad knowledge and emergent abilities – while being much more reliable and useful than its predecessors.
DeepMind’s AlphaFold: Scaling for Scientific Discovery
Not all scaling successes are about ever-bigger parameter counts; sometimes it’s about scaling compute and data to crack a tough scientific problem. AlphaFold 2, developed by DeepMind and announced in 2020, is a prime example of qualitative breakthrough achieved through heavy AI computation. AlphaFold 2 is an AI system that predicts the 3D structure of proteins from their amino acid sequence – a problem known as protein folding that scientists had been trying to solve for 50 years. AlphaFold 2’s impact was staggering: it can predict structures with atomic-level accuracy, often rivaling results from expensive lab techniques like X-ray crystallography. It was hailed as a revolutionary advancement in biology, essentially providing a “solution” to protein folding for many cases. The model’s predictions were so good that they vastly outperformed all other methods in the CASP14 competition (a benchmark for protein structure prediction), and AlphaFold has since been used to release the structures of hundreds of thousands, then millions, of proteins into a public database for scientists worldwide.
From a scaling perspective, AlphaFold 2’s achievement was less about an enormous network (in fact, the model has only ~97 million parameters, which is modest by modern AI standards) and more about leveraging huge amounts of data, compute, and a complex model architecture effectively. The system was trained on large databases of known protein structures and genomic data. Training it was a gargantuan task: even with “only” 97M parameters, the training involved processing vast numbers of evolutionary sequence alignments and intermediate computations. The volume of intermediate data (like the pairwise interactions in the protein chain) is enormous, creating a heavy load on memory and compute. DeepMind’s team had to use specialized hardware (Google TPU v3 clusters) and optimize the software pipeline to handle this. In fact, the initial AlphaFold 2 training was so computationally intensive and I/O heavy that scaling it further (to speed it up) was not straightforward – adding more GPUs didn’t help much due to communication bottlenecks. It took follow-up research (like NVIDIA’s ScaleFold project in 2024) to identify and fix these bottlenecks, successfully distributing AlphaFold training across 2,080 GPUs and bringing what used to take a week down to 10 hours. This illustrates that scaling in practice requires end-to-end optimization: everything from the neural network design, to how data is fed in, to how tasks are parallelized needs to be tuned.
The strategies behind AlphaFold’s success include: incorporating domain knowledge into the model’s architecture (the AlphaFold network uses an innovative module called the Evoformer that efficiently captures protein-specific relationships), using ensemble models and multiple stages to refine predictions, and harnessing massive compute to search the solution space. The lesson here is that scaling can also mean scaling in problem complexity. By throwing sufficient computational might and creative modeling at a well-defined scientific problem, AI can achieve what was once thought impossible. AlphaFold did not rely on a trillion parameters; it relied on enough compute to let the model learn the physics and biology from data. Its success has inspired a wave of AI-for-science efforts, using similar heavy-compute techniques to tackle problems in drug discovery, weather prediction, and materials science. The broader point: scaling isn’t just for language models – it’s a general paradigm that, when applied thoughtfully, can lead to breakthroughs in many fields.
Google’s PaLM: Pushing the Limits of Language Model Scale
While OpenAI and DeepMind grabbed headlines with GPT-4 and AlphaFold, Google Research has also been a major player in scaling AI. One of its signature projects is PaLM (Pathways Language Model), unveiled in 2022. PaLM is a 540-billion parameter transformer language model – at its debut, one of the largest dense language models ever trained. It was trained using Google’s Pathways system, which allowed the training to be spread across 6,144 TPU v4 chips simultaneously. This massive scale-out was a technical accomplishment in its own right (reportedly the largest TPU pod created to date). The result of this scaling was a model that demonstrated extraordinary performance on a wide array of tasks. PaLM was evaluated on 29 different NLP benchmarks (covering things like language understanding, question answering, summarization, translation, and more) and it set new state-of-the-art results on 28 of them. In fact, on a challenging collection of tasks called BIG-bench – which includes logic puzzles and commonsense reasoning tests – PaLM’s average performance was at the level of an average human, a notable milestone for AI. It also showed prowess in more creative tasks, such as explaining jokes (a task requiring nuance and world knowledge), which smaller models struggled with. Furthermore, Google researchers found that PaLM’s capabilities could be boosted even further with techniques like chain-of-thought prompting (where the model is guided to break down its reasoning step by step), which indicates that these large models not only carry knowledge but can be steered to use it more effectively.
The strategy behind PaLM’s success was straightforward in principle: scale up everything – model size, data, and compute – following the scaling law playbook. But in practice, executing this at the 540B-parameter scale required innovations. Google’s Pathways framework was developed to efficiently route data and model computations across thousands of chips, keeping them all utilized. The training dataset for PaLM was also enormous and diverse, comprising multiple languages and domains (which helped the model generalize and even perform tasks in languages it wasn’t explicitly trained on). Training such a model took significant time on one of the world’s most powerful AI supercomputers. The PaLM team emphasized that scaling was combined with “novel architectural choices and training schemes” to reach the best results. For example, they likely experimented with different transformer variants and optimization tricks to make the training converge well at this scale. The success of PaLM demonstrated that the scaling hypothesis holds true even at previously untested extremes – if you go bigger, you do get better performance, and sometimes very impressive new abilities. One lesson here is that multi-discipline collaboration helps: building PaLM required software engineers, researchers, and hardware teams working together. It’s as much an engineering triumph as a scientific one. Another lesson is the importance of evaluation: the Google team benchmarked PaLM on an extensive suite of tasks, which helped showcase its strengths (and remaining weaknesses) and provided evidence that scaling delivered more than just “parroting” ability – it produced an AI that can reason and understand in ways that smaller models could not.
Other Notable Examples: The above three are flagship examples, but many other notable AI models underscore the impact of scaling. OpenAI’s DALL-E 2 and other image-generating models (like Google’s Imagen) showed that scaling model size and training data (in those cases, pairing images with text descriptions) can produce AI that generates remarkably detailed and creative images from prompts – essentially learning high-level visual concepts. In reinforcement learning, AlphaGo Zero and AlphaZero from DeepMind demonstrated that scaling up computation for self-play (training for millions of games) can yield superhuman game-playing agents without any human examples; each iteration (AlphaGo to AlphaGo Zero to AlphaZero) used more compute and training to achieve a leap in performance and generality. More recently, companies like Meta AI have introduced models such as LLaMA that, while not the largest in absolute size, were trained on large-scale diverse data and have been scaled down (via fine-tuning) to run even on smaller devices – showing a pathway to make scaled models more efficient for deployment.
Lessons for the Future: From these case studies, a few common threads emerge about what it takes to successfully scale AI models:
Integrate Innovation with Scale: Simply throwing more compute at the problem is rarely enough. The teams behind these models introduced new ideas – be it GPT-4’s alignment methods, AlphaFold’s novel architecture, or PaLM’s training techniques – to get the most out of scaling. Future efforts will likely require algorithmic ingenuity alongside raw scaling to tackle things like reasoning, causality, or learning from small data.
Infrastructure and Collaboration: Scaling projects are big efforts that often need custom infrastructure (e.g., the TPU pods, Azure supercomputer) and large teams. A lesson for organizations is that if you aim to train a frontier model, you must plan for a significant engineering investment. Collaboration between research and engineering divisions (and sometimes between organizations) becomes crucial. OpenAI partnering with Microsoft for Azure, or the cross-team effort in DeepMind for AlphaFold (bringing together protein experts and AI researchers), exemplifies this.
Efficient Scaling: The Chinchilla result and others suggest the next phase is about better scaling, not just more scaling. Techniques like model compression, smarter training (choosing the right model size for a given compute budget), and hybrid models (like Mixture-of-Experts which effectively have many parameters but activate subsets for efficiency) will play a role. For instance, there are research directions into models that can grow dynamically or use sparsity to cut down computation without losing capability.
Safety and Ethics: As models scale, their impact (positive or negative) scales too. The GPT-4 case showed the importance of aligning powerful models with human values and norms. Future scaled models will need even more robust guardrails, testing, and transparency. The community is increasingly aware that “with great power comes great responsibility” – and a scaled model is great power. OpenAI open-sourcing a framework like OpenAI Evals (for evaluating model performance and catching issues) is one attempt to handle this
insidehpc.com. We can expect that any future GPT-5 or comparable model will involve even more extensive safe-usage planning.
Broadening Access: Lastly, the success cases so far have been in the hands of a few big players. A key lesson and challenge is how to broaden participation in scaling breakthroughs. Efforts like open-sourcing large models (OPT, BLOOM, etc.) and developing more cost-effective training recipes are steps in the right direction, ensuring that academia and smaller companies can also experiment and contribute. This will be important to keep the field healthy and innovative, avoiding a scenario where only “the rich get richer” in terms of AI capability.
Scaling AI models has already transformed what machines can do – from passing professional exams and decoding the language of proteins, to beating world champions in games and creating art. The theory of scaling suggests that even more surprising feats lie ahead as we continue to increase model capacity. However, practical constraints mean that we must be smart about how we scale. The future will likely see a mix of bigger models and better (more efficient, more targeted) scaling techniques. As we forge ahead, it’s important to remember that scaling is a means to an end, not an end in itself. Ultimately, the goal is not just to build enormous AI systems, but to unlock new capabilities and applications that benefit humanity – whether that’s through a conversational AI that truly understands, or a scientific AI that helps solve climate change. The journey of scaling AI is thus a story of both imagination and caution: dreaming how far this approach can take us, while staying mindful of the challenges and responsibilities that come with such powerful technology. In the words of one AI editorialist, “Scaling might produce the most exciting results and capabilities today, but that does not mean the machine learning community should have a narrow-minded focus on it” joyk.com . There is plenty of room for creativity and alternative approaches alongside scaling. By balancing scale with efficiency, innovation with ethics, and ambition with inclusivity, the AI field can ensure that the scaling revolution continues to be a positive force in the years to come.
References:
Bashir, D. & Kurenkov, A. (2022). The AI Scaling Hypothesis. Last Week in AI (Editorial) – “The past decade of progress in AI can largely be summed up by one word: scale...”
Sutton, R. (2019). The Bitter Lesson. – On the tendency of scalable approaches to outperform built-in knowledge over time.
Kaplan, J. et al. (2020). Scaling Laws for Neural Language Models. – Demonstrated smooth power-law improvements in language model performance as model/data/compute scale up.
Brown, T. et al. (2020). Language Models are Few-Shot Learners (GPT-3 paper). – Noted the emergent few-shot learning ability of GPT-3 (175B), 10× larger than GPT-2.
Zhu, F. et al. (2024). ScaleFold: Reducing AlphaFold Initial Training Time to 10 Hours. – Noted AlphaFold2’s 97M parameters and challenges in scaling its training across GPUs.
PlanBEco (2023). AI’s carbon footprint... – Reported GPT-3 training consumed ~1287 MWh, emitting ~502 tCO2 (comparable to 112 cars/year).
Thompson, N. et al. (2022). Science. – Study showing 96% of largest AI models come from industry, with industry models 29× larger than academia’s on average.
OpenAI (2023). GPT-4 Technical Report and announcements. – Noted GPT-4’s performance (top 10% on bar exam vs GPT-3.5’s bottom 10%) and the Azure supercomputer used for training.
DeepMind (2021). AlphaFold 2, Nature paper. – Demonstrated breakthrough in protein structure prediction, with AlphaFold achieving atomic accuracy.
Chowdhery, A. et al. (2022). PaLM: Scaling Language Modeling with Pathways. – Introduced PaLM 540B model, trained on 6144 TPU v4 chips, surpassing prior NLP benchmarks and even human average on some tasks.