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If youve worried that AI might take your job, deprive you of your livelihood, or maybe even replace your role in society, it probably feels good to see that the latest AI tools fail spectacularly. If AI recommends glue as a pizza topping, then youre safe for another day. But the fact remains that AI already has definite advantages over even the most skilled humans, and knowing where these advantages ariseand where they dontwill be key to adapting to the AI-infused workforce. AI will often not be as effective as a human doing the same job. It wont always know more or be more accurate. And it definitely wont always be fairer or more reliable. But it may still be used whenever it has an advantage over humans in one of four dimensions: speed, scale, scope and sophistication. Understanding these dimensions is the key to understanding AI-human replacement. Speed First, speed. There are tasks that humans are perfectly good at but are not nearly as fast as AI. One example is restoring or upscaling images: taking pixelated, noisy or blurry images and making a crisper and higher-resolution version. Humans are good at this; given the right digital tools and enough time, they can fill in fine details. But they are too slow to efficiently process large images or videos. AI models can do the job blazingly fast, a capability with important industrial applications. AI-based software is used to enhance satellite and remote sensing data, to compress video files, to make video games run better with cheaper hardware and less energy, to help robots make the right movements, and to model turbulence to help build better internal combustion engines. Real-time performance matters in these cases, and the speed of AI is necessary to enable them. Scale The second dimension of AIs advantage over humans is scale. AI will increasingly be used in tasks that humans can do well in one place at a time, but that AI can do in millions of places simultaneously. A familiar example is ad targeting and personalization. Human marketers can collect data and predict what types of people will respond to certain advertisements. This capability is important commercially; advertising is a trillion-dollar market globally. AI models can do this for every single product, TV show, website, and internet user. This is how the modern ad-tech industry works. Real-time bidding markets price the display ads that appear alongside the websites you visit, and advertisers use AI models to decide when they want to pay that pricethousands of times per second. Scope Next, scope. AI can be advantageous when it does more things than any one person could, even when a human might do better at any one of those tasks. Generative AI systems such as ChatGPT can engage in conversation on any topic, write an essay espousing any position, create poetry in any style and language, write computer code in any programming language, and more. These models may not be superior to skilled humans at any one of these things, but no single human could outperform top-tier generative models across them all. Its the combination of these competencies that generates value. Employers often struggle to find people with talents in disciplines such as software development and data science who also have strong prior knowledge of the employers domain. Organizations are likely to continue to rely on human specialists to write the best code and the best persuasive text, but they will increasingly be satisfied with AI when they just need a passable version of either. Sophistication Finally, sophistication. AIs can consider more factors in their decisions than humans can, and this can endow them with superhuman performance on specialized tasks. Computers have long been used to keep track of a multiplicity of factors that compound and interact in ways more complex than a human could trace. The 1990s chess-playing computer systems such as Deep Blue succeeded by thinking a dozen or more moves ahead. Modern AI systems use a radically different approach: Deep learning systems built from many-layered neural networks take account of complex interactionsoften many billionsamong many factors. Neural networks now power the best chess-playing models and most other AI systems. Chess is not the only domain where eschewing conventional rules and formal logic in favor of highly sophisticated and inscrutable systems has generated progress. The stunning advance of AlphaFold2, the AI model of structural biology whose creators Demis Hassabis and John Jumper were recognized with the Nobel Prize in chemistry in 2024, is another example. This breakthrough replaced traditional physics-based systems for predicting how sequences of amino acids would fold into three-dimensional shapes with a 93 million-parameter model, even though it doesnt account for physical laws. That lack of real-world grounding is not desirable: No one likes the enigmatic nature of these AI systems, and scientists are eager to understand better how they work. But the sophistication of AI is providing value to scientists, and its use across scientific fields has grown exponentially in recent years. Context matters Those are the four dimensions where AI can excel over humans. Accuracy still matters. You wouldnt want to use an AI that makes graphics look glitchy or targets ads randomlyyet accuracy isnt the differentiator. The AI doesnt need superhuman accuracy. Its enough for AI to be merely good and fast, or adequate and scalable. Increasing scope often comes with an accuracy penalty, because AI can generalize poorly to truly novel tasks. The 4 Ss are sometimes at odds. With a given amount of computing power, you generally have to trade off scale for sophistication. Even more interestingly, when an AI takes over a human task, the task can change. Sometimes the AI is just doing things differently. Other times, AI starts doing different things. These changes bring new opportunities and new risks. For example, high-frequency trading isnt just computers trading stocks faster; its a fundamentally different kind of trading that enables entirely new strategies, tactics, and associated risks. Likewise, AI hs developed more sophisticated strategies for the games of chess and Go. And the scale of AI chatbots has changed the nature of propaganda by allowing artificial voices to overwhelm human speech. It is this phase shift, when changes in degree may transform into changes in kind, where AIs impacts to society are likely to be most keenly felt. All of this points to the places that AI can have a positive impact. When a system has a bottleneck related to speed, scale, scope, or sophistication, or when one of these factors poses a real barrier to being able to accomplish a goal, it makes sense to think about how AI could help. Equally, when speed, scale, scope, and sophistication are not primary barriers, it makes less sense to use AI. This is why AI auto-suggest features for short communications such as text messages can feel so annoying. They offer little speed advantage and no benefit from sophistication, while sacrificing the sincerity of human communication. Many deployments of customer service chatbots also fail this test, which may explain their unpopularity. Companies invest in them because of their scalability, and yet the bots often become a barrier to support rather than a speedy or sophisticated problem-solver. Where the advantage lies Keep this in mind when you encounter a new application for AI or consider AI as a replacement for, or an augmentation to, a human process. Looking for bottlenecks in speed, scale, scope, and sophistication provides a framework for understanding where AI provides value, and equally where the unique capabilities of the human species give us an enduring advantage. Bruce Schneier is an adjunct lecturer in public policy at the Harvard Kennedy School. Nathan Sanders is an affiliate at the Berkman Klein Center for Internet & Society at Harvard University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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Is America in a Second Gilded Age? Evan Osnos thinks so. In this episode of Most Innovative Companies, Osnos unpacks how extreme wealth, corporate influence, and political inequality are transforming American life. If youve ever wondered how the 1% really operate, this is the deep dive you need.
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Cheap or free access to AI models keeps improving, with Google the latest firm to make its newest models available to all users, not just paying ones. But that access comes with one cost: the environment. In a new study, German researchers tested 14 large language models (LLMs) of various sizes from leading developers such as Meta, Alibaba, and others. Each model answered 1,000 difficult academic questions spanning topics from world history to advanced mathematics. The tests ran on a powerful, energy-intensive NVIDIA A100 GPU, using a specialized framework to precisely measure electricity consumption per answer. This data was then converted into carbon dioxide equivalent emissions, providing a clear comparison of each models environmental impact. The researchers found that many LLMs are far more powerful than needed for everyday queries. Smaller, less energy-hungry models can answer many factual questions just as well. The carbon and water footprints of a single prompt vary dramatically depending on model size and task type. Prompts requiring reasoning, which force models to think aloud, are especially polluting because they generate many more tokens. One model, Cogito, topped the accuracy tableanswering nearly 85% of questions correctlybut produced three times more emissions than similar-sized models, highlighting a trade-off rarely visible to AI developers or users. (Cogito did not respond to a request for comment.) Do we really need a 400-billion parameter GPT model to answer when World War II was, for example, says Maximilian Dauner, a researcher at Hochschule München University of Applied Sciences and one of the studys authors. The results underscored the balance between accuracy and emissions. The least-polluting model tested, Qwen 7B, answered just one in three questions correctly but emitted only 27.7 grams of carbon dioxide equivalent. In contrast, Deepseeks R1 70B reasoning model answered nearly eight in 10 questions correctlywhile producing more than 70 times the emissions for the same workload. The type of question also affects environmental impact. Algebra or philosophy prompts produced emissions up to six times higher than what a high school student would generate getting homework help. Companies should be more transparent about the real emissions and water consumptions from prompts, says Dauner. But at the same time, users ought to be more awareand more judiciousabout their AI use.
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