If you’ve been anxious about AI potentially usurping your employment, stripping away your means of support, or perhaps even substituting your function within society, you might find some solace in observing the recent failures of the latest AI technologies. If AI suggests adhesive as a pizza topping, it’s likely safe for you today.
Nonetheless, it is a reality that AI already possesses distinct advantages over even the most adept individuals, and understanding where these strengths emerge—and where they fall short—will be crucial for adjusting to the AI-enhanced workforce.
AI might not consistently outperform a human in the same role. It doesn’t always have superior knowledge or precision. Furthermore, it certainly does not guarantee fairness or reliability. However, it can be advantageous whenever it surpasses humans in one of four aspects: speed, scale, scope, and sophistication. Grasping these aspects is essential for comprehending AI-human substitution.
Speed
First, let’s address speed. There are activities that humans manage well but cannot match the rapidity of AI. A typical case is the restoration or enhancement of images: transforming pixelated, noisy, or blurry visuals into clearer, high-resolution versions. Humans excel at this; with the appropriate digital tools and sufficient time, they can meticulously refine details. Yet, their pace is too slow to handle extensive images or videos efficiently.
AI systems can perform this task at astonishing speeds, a capability that holds significant industrial value. AI-driven software is utilized to improve satellite and remote sensor data, to compress video files, to enhance video games with more efficient hardware and reduced energy consumption, to assist robots in executing precise movements, and to simulate turbulence to aid in the development of improved internal combustion engines.
In these instances, real-time performance is crucial, and AI’s speed is necessary to enable it.
Scale
The second dimension of AI’s superiority over humans is scale. AI is increasingly employed in tasks that humans can perform effectively in one location at a time, but which AI can manage in millions of locations concurrently. A notable example is targeted advertising and personalization. Human marketers can gather data and anticipate which types of individuals will react to specific advertisements. This ability is commercially vital; advertising constitutes a trillion-dollar industry worldwide.
AI models can execute this for every individual product, TV show, website, and internet user. This operation exemplifies the functioning of the contemporary ad-tech sector. Real-time bidding platforms evaluate the display advertisements that appear with the websites you visit, and advertisers utilize AI models to decide when they wish to pay that price—thousands of times each second.
Scope
Next comes scope. AI can be beneficial when it undertakes more tasks than any single person could manage, even if a human might excel at any specific role. Generative AI systems like ChatGPT can converse on any subject, compose an essay presenting any viewpoint, create poetry in countless styles and languages, write programming code in any coding language, and more. These models might not surpass skilled individuals in any singular task, but no single human could outperform leading generative models across the board.
The interaction of these skills generates value. Employers frequently encounter challenges in finding individuals with expertise in fields such as software development and data science who also possess considerable knowledge of the employer’s industry. Organizations are likely to continue relying on human experts to produce top-notch code and the most compelling text, but they will increasingly find AI satisfactory when they merely need an adequate version of either.
Sophistication
Lastly, sophistication. AIs can take into account more variables in their decision-making than humans can, granting them superhuman performance on specialized tasks. Computers have long been utilized to monitor a multitude of factors that interact and compound in ways more intricate than a human could track. The chess-playing computer systems of the 1990s, such as Deep Blue, triumphed by anticipating a dozen or more moves ahead.
Modern AI systems adopt a markedly different methodology: Deep learning systems utilizing multi-layered neural networks consider complex interactions—often involving billions—among numerous factors. Neural networks now fuel the best chess-playing models and the majority of other AI systems.
Chess is not the sole domain where breaking free from traditional rules and formal logic in favor of highly intricate and opaque systems has led to advancements. The remarkable progress of AlphaFold2, the AI model for structural biology whose creators Demis Hassabis and John Jumper received the Nobel Prize in chemistry in 2024, serves as another illustration.
This breakthrough replaced conventional physics-based methods for forecasting how amino acid sequences would fold into three-dimensional structures with a 93 million-parameter model, even if it doesn’t consider physical laws. That absence of real-world grounding is undesirable: No one appreciates the cryptic nature of these AI systems, and researchers are eager to gain a clearer understanding of their operations.
However, the sophistication of AI is delivering value to scientists, and its application across scientific domains has surged dramatically in recent years.
Context matters
These represent the four areas where AI can outperform humans. Accuracy continues to be significant. You wouldn’t want to utilize an AI that produces glitchy graphics or targets ads at random—yet accuracy isn’t the unique factor determining success. AI needs not achieve superhuman precision; being merely competent and swift, or satisfactory and scalable, suffices. Expanding scope often incurs an accuracy compromise, as AI can struggle with generalizing to genuinely novel scenarios. The 4 S’s occasionally conflict. With a fixed amount of computational power, you typically have to sacrifice scale for sophistication.
Even more intriguingly, when an AI assumes a human task, the nature of the task can evolve. Occasionally, the AI simply approaches tasks differently. At other times, AI starts executing different functions altogether. These transformations result in fresh opportunities and new challenges.
For instance, high-frequency trading is not merely about computers executing trades faster; it’s a fundamentally altered trading method that enables entirely new strategies, tactics, and risks. Similarly, AI has formulated more advanced strategies for chess and Go. Moreover, the prevalence of AI chatbots has transformed the landscape of propaganda by permitting artificial voices to overwhelm human discourse.
It is this “phase shift,” where incremental changes might transform into qualitative shifts, where the effects of AI on society are likely to be felt most intensely. All of this highlights areas where AI can contribute positively. When a system faces a constraint related to speed, scale, scope, or sophistication, or when one of these factors presents a legitimate obstacle to achieving a goal, it is logical to contemplate how AI could assist.
Conversely, when speed, scale, scope, and sophistication are not major hindrances, utilizing AI becomes less justifiable. This is why AI auto-suggest features for brief communications like text messages can seem so annoying. They provide minimal speed benefits and no sophistication advantage, while compromising the authenticity of human interaction.
Numerous uses of customer service chatbots also fall short of this standard, which might clarify their lack of popularity. Organizations invest in them for their scalability, yet the bots often obstruct rather than expedite support, failing to provide sophisticated problem-solving.
Where the advantage lies
Keep this perspective in mind as you encounter a new use for AI or consider it as a substitute or enhancement to human processes. Identifying bottlenecks in speed, scale, scope, and sophistication offers a framework for discerning where AI delivers value, and where the distinct capabilities of humans provide us an ongoing advantage.
This article was penned in collaboration with Nathan E. Sanders and originally published in The Conversation.