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In high-stakes meetings or chaotic team moments, the person who stays grounded often becomes the one others follow. And this outcome isnt about status or rankits biological. Human groups are wired to seek cues of stability. In uncertain situations, people scan for behavioral signals of calm, control, and composure. Those who project these signals can influence group dynamics in powerful ways, whether or not they hold formal authority. In my work on Leadership Biodynamics, a biology-based approach to executive presence, I train leaders to tune their behavioral signals intentionally. The goal is not to fake confidence, but to engage practices that create real calm in the body and broadcast it to others. This is rooted in the biology of behavior. When your nervous system signals stability, others systems start to regulate in response. Here are three tiny behaviors that can make you the calmest person in the room. 1. Slow Your Exhale One of the fastest ways to regulate your nervous system is through your breath. Specifically, focus on extending the exhale. A longer out-breath activates the parasympathetic nervous system, signaling to your body and brain that you are safe and in control. In stressful moments, most people unconsciously shorten their breath, which heightens physiological arousal. By contrast, slowing your exhale lowers heart rate variability and helps maintain executive function under pressure. Neuroscience research supports this. Controlled breathing patterns are shown to downregulate the amygdala, the brains threat detection center, and improve prefrontal cortex performance. In leadership terms, this allows you to think clearly and signal calm even when tension is high. 2. Master the Neutral Face Facial expressions are among the most contagious signals in any room. Subtle cues of tensiontightened jaw, furrowed brow, compressed lipstrigger mirror neuron responses in others, escalating stress contagion. One of the simplest yet most powerful techniques is to practice what I call a neutral face. Relax your facial muscles, release tension from the jaw and brow, and let your gaze soften. This sends nonthreatening signals that calm others nervous systems. A recent story on how fighter pilots maintain calm in high-stakes situations echoes this principle. Pilots are trained to maintain neutral, composed facial expressions because they know crew members will mirror their affect. The same applies in leadership settings. 3. Use Stillness Strategically Movement is another powerful signal. Rapid, jittery gestures broadcast anxiety. Deliberate stillness, on the other hand, projects control. In tense meetings, practice purposeful stillness. Rest your hands lightly on the table, slow your gestures, and allow silences to stand without rushing to fill them. This creates a grounding presence that helps regulate group energy. Behavioral research confirms that leaders who demonstrate controlled stillness are perceived as more composed, credible, and trustworthy. The effect is amplified when combined with calm vocal tone and centered body posture. Why Projecting Calm Matters at Work These behaviors may seem small, but their effects are anything but. In group settings, emotional states are highly contagious. The person who maintains composure can anchor the emotional tone of the entire room. This is especially critical in hybrid and remote environments, where subtle behavioral cues carry more weight. In my work with global leadership teams, I often see that those who can project calm consistently gain disproportionate influence, not through dominance but through stabilizing presence. In Biohacking Leadership, my book of science-based techniques for better leadership, I emphasize that influence is not about charisma alone. It is about biological signaling. When your own system is grounded, you help others self-regulate. That is what builds trust and followership in high-stakes moments. The bottom line is this: if you want to become the calmest person in the room, start with these three behaviors. Slow your exhale. Relax your face. Use stillness strategically. These tiny actions, grounded in the biology of behavior, can shift not only how you feel, but how others respond to you. And in leadership, that is the signal that often matters most.
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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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