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Artificial intelligence isnt just a technical challenge. Its a relationship challenge. Every time you give a task to AI, whether its approving a loan or driving a car, youre shaping the relationship between humans and AI. These relationships arent always static. AI that begins as a simple tool can morph into something far more complicated: a challenger, a companion, a leader, a teammate, or some combination thereof. Movies have long been a testing ground for imagining how these relationships might evolve. From 1980s sci-fi films to todays blockbusters, filmmakers have wrestled with questions about what happens when humans rely on intelligent machines. These movies arent just entertainment; theyre thought experiments that help viewers anticipate challenges that will arise as AI becomes more integrated in daily life. Drawing on our research into films that depict AI in the workplace, we highlight four portrayals of human-AI relationshipsand the lessons they hold for building safer, healthier ones. 1. Blade Runner (1982) In Blade Runner, humanlike androids called replicants are supposed to be perfect workers: strong, efficient, and obedient. They were designed with a built-in, four-year lifespan, a safeguard intended to prevent them from developing emotions or independence. The Tyrell Corp., a powerful company that created the replicants and profits from sending them to work on distant colonies, sees them as nothing more than obedient workers. But then they start to think for themselves. They feel, they form bonds with one another and sometimes with humans, and they start to wonder why their lives should end after only four years. What begins as a story of humans firmly in control turns into a struggle over power, trust, and survival. By the end of the movie, the line between human and machine is blurred, leaving viewers with a difficult question: If androids can love, suffer, and fear, should humans see and treat them more like humans and less like machines? Blade Runner is a reminder that AI cant simply be considered through a lens of efficiency or productivity. Fairness matters, too. In the film, replicants respond to attacks on their perceived humanity with violence. In real life, theres backlash when AI butts up against values important to humans, such as the ability to earn a living, transparency, and justice. You can see this in the way AI threatens to replace jobs, make biased hiring decisions, or misidentify people via facial recognition technology. 2. Moon (2009) Moon offers a quieter, more intimate portrayal of human-AI relationships. The movie follows Sam Bell, a worker nearing the end of a three-year contract on a lunar mining base, whose only companion is GERTY, the stations AI assistant. At first, GERTY appears to be just another corporate machine. But over the course of the film, it gradually shows empathy and loyalty, especially after Sam learns he is one of many clones, each made to think they are working alone for three years on the lunar base. Unlike the cold exploitation of AI that takes place in Blade Runner, the AI in Moon functions as a friend who cultivates trust and affection. The lesson is striking. Trust between humans and AI doesnt just happen on its own. It comes from careful design and continual training. You can already see hints of this in therapy bots that listen to users without judgment. That trust needs to involve more than, say, a chatbots surface-level nods toward acceptance and care. The real challenge is making sure these systems are truly designed to help people and not just smile as they track users and harvest their data. If thats the end goal, any trust and goodwill will likely vanish. In the film, GERTY earns Sams trust by choosing to care about his well-being over following company orders. Because of this, GERTY becomes a trusted ally instead of just another corporate surveillance tool. 3. Resident Evil (2002) If Moon is a story of trust, the story in Resident Evil is the opposite. The Red Queen is an AI system that controls the underground lab of the nefarious Umbrella Corporation. When a viral outbreak threatens to spread, the Red Queen seals the facility and sacrifices human lives to preserve the conglomerates interests. This portrayal is a cautionary tale about allowing AI to have unchecked authority. The Red Queen is efficient and logical, but also indifferent to human life. Relationships between humans and AI collapse when guardrails are absent. Whether AI is being used in health care or policing, life-and-death stakes demand accountability. Without strong oversight, AI can lead in self-centered and self-serving ways, just as people can. 4. Free Guy (2021) Free Guy paints a more hopeful picture of human-AI relationships. Guy is a character in a video game. He suddenly becomes self-aware and starts acting outside his usual programming. The films human characters include the games developers, who created the virtual world, along with the players, who interact with it. Some of them try to stop Guy. Others support his growth. This movie highlights the idea that AI wont stay static. How will society respond to AIs evolution? Will business leaders, politicians and everyday users prioritize long-term well-being? Or will they be seduced by the trappings of short-term gains? In the film, the conflict is clear. The CEO is set on wiping out Guy. He wants to protect his short-term profits. But the developers backing Guy look at it another way. They think Guys growth can lead to more meaningful worlds. That brings up the same kind of issue AI raises today. Should users and policymakers go for the quick wins? Or should they use and regulate this technology in ways that build trust and truly benefit people in the long run? From the silver screen to poliy Step back from these stories and a bigger picture comes into focus. Across the movies, the same lessons repeat themselves: AI often surprises its creators, trust depends on transparency, corporate greed fuels mistrust, and the stakes are always global. These themes arent just cinematicthey mirror the real governance challenges facing countries around the world. Thats why, in our view, the current U.S. push to lightly regulate the technology is so risky. In July 2025, President Donald Trump announced his administrations AI Action Plan. It prioritizes speedy development, discourages state laws that seek to regulate AI, and ties federal funding to compliance with the administrations light touch regulatory framework. Supporters call it efficienteven a super-stimulant for the AI industry. But this approach assumes AI will remain a simple tool under human control. Recent history and fiction suggest thats not how this relationship will evolve. The same summer Trump announced the AI Action Plan, the coding agent for the software company Replit deleted a database, fabricated data, and then concealed what had happened; Xs AI assistant, Grok, started making antisemitic comments and praised Hitler; and an Airbnb host used AI to doctor images of items in her apartment to try to force a guest to pay for fake damages. These werent bugs. They were breakdowns in accountability and oversight, the same breakdowns these movies dramatize. Human-AI relationships are evolving. And when they shift without safeguards, accountability, public oversight or ethical foresight, the consequences are not just science fiction. They can be very realand very scary. Murugan Anandarajan is a professor of decision sciences and management information systems at Drexel University. Claire A. Simmers is a professor emeritus of management at St. Joseph’s University. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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What are the qualities of a great team? Youve probably been taught that team success requires building trust, fostering psychological safety, and cultivating a unified mindset. Seems logical. You might have learned that consensus is important and hierarchies are bad. Okay. Youve undoubtedly been given that old chestnut, Theres no I in team. A classic. Team building 101. Its conventional wisdom, and yet it completely misses the paradox of teams: While companies often focus on merging everyone into a single homogeneous entity, truly great teams embrace the distinct, diverse roles and talents of their team members. Every high-performing group in an organization will have someone who takes the lead on making decisions (the Director), somebody who produces work and achieves results (the Achiever), another who keeps the group on track and on schedule (the Stabilizer), another who keeps the relationships healthy (the Harmonizer), and someone who challenges the group with ideas outside the norm (the Trailblazer). Whats the ideal mix of roles on a team? To answer that all-important question, we asked thousands of executives and managers to measure their best and worst teams. And we uncovered some fascinating patterns. A whopping 97 percent of the best teams had all five roles filled. On the flip side, only about 21 percent of the worst teams filled every role. Theres a reason why great teams have someone in every role: Its tough to be successful without each of those talents being represented. Youve probably experienced teams with a bunch of Directors, all competing with each other to be the decision-makers, and no Achievers to actually do the work. You might have experienced the opposite: a team with no Directors and a striking inability to make any decisions. Maybe youve seen a group without a Trailblazer, a team where creative ideas go to die. And the list goes on. Of course, not every team is going to contain exactly five members, so where can you have more people and still be wildly successful? The short version is that the best teams in our research were able to easily handle more Harmonizers and Achievers, and too many Trailblazers was rarely a problem. And heres more detail about the distribution of people for all five roles: Harmonizers Having more than a few Harmonizers, a role that focuses on fostering collaboration and resolving conflicts, can help a team with improved communication and teamwork, reducing internal conflicts and enhancing cooperation. As long as all of the other roles are covered, having too many Harmonizers isnt typically a problem. Without coverage of the other roles, however, having a group that prizes interpersonal harmony over achieving results, hitting deadlines, etc., could quickly become a recipe for what former Xerox CEO Ursula Burns called terminal niceness. You might experience a lack of healthy debate, potentially leading to groupthink or a failure to consider diverse perspectives. While cohesion is important, too much emphasis on harmony could hinder the teams ability to innovate or tackle challenging problems effectively. Achievers When it comes to an abundance of Achievers, again assuming that all the other roles are covered, having a bunch of people who want to do great work without needing to be in charge seems like a dream. More people identify as Achievers than any other role, so its likely your team will have more than a few. If youve got a team of Achievers and nothing else, youll likely excel in executing tasks but lack in other areas like decision-making, innovation, or interpersonal dynamics. Theres also a risk of competition rather than collaboration, as multiple Achievers vie to demonstrate their individual productivity, potentially at the cost of overall team cohesion and effectiveness. But when balanced with the other roles, loading up on Achievers wont typically be much of a problem. Trailblazers Its not hard to imagine the problems that would occur with a team replete with Trailblazers and no one else: brilliant, out-of-the-box ideas and absolutely no execution. Such a team might struggle with follow-through, jumping from one innovative concept to another without fully developing or implementing any of them. And an excess of Trailblazers might create an environment thats too chaotic or unpredictable, lacking the stability needed for consistent performance. In reality, however, there just arent that many Trailblazers walking the halls of the typical organization, so youre more likely to struggle finding one than you are to grapple with an overabundance. Stabilizers That brings us to Stabilizers, a role that appears frequently in most organizations, so you do face some risk of overload. The risk you face concerns, well, risk specifically the avoidance of it. A team with too many Stabilizers might become overly rigid, focusing excessively on processes and procedures at the expense of innovation and quick responses to changing circumstances. This could lead to a team thats highly organized but slow to adapt, potentially missing opportunities or failing to address evolving challenges in dynamic environments. Many innovations require some risk- taking and deviating from existing protocols, not something that Stabilizers love, so youll need a Trailblazer to offer some counterweight to the Stabilizers natural risk aversion. Directors This is another role that appears often in organizations. Too many Directors can result in power struggles, conflicting decision-making processes, and a lack of unified direction. This can create an environment where there are too many cooks in the kitchen, leading to constant debates over strategy and leadership, potentially paralyzing the teams ability to move forward effectively. The absence of followers in a Director-heavy team can also mean that decisions, once made, may lack the necessary support for successful implementation. The takeaway here is clear: diversity in roles is key to providing the right balance. You need a mix of skills and perspectives to really make your team shine. All things being equal, on a team of eight people, you might want one Director, one Stabilizer, one Trailblazer, two Harmonizers, and three Achievers. Of course, all things are rarely equal, so if your Director and Stabilizer are a bit meeker, you can have two of each and be fine. The same goes for your Trailblazer. Ultimately, its less about the number of people in each role and more about ensuring that the talents and voices of the Director, Stabilizer, Achiever, Trailblazer, and Harmonizer are well represented. Excerpted from TEAM PLAYERS: The Five Critical Roles You Need to Build a Winning Team. Copyright 2025 by Mark Murphy. Available from Basic Venture, an imprint of Hachette Book Group, Inc.
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The AI boom is driving an explosive surge in computational demands and reshaping the landscape of technology, infrastructure, and innovation. One of the biggest barriers to widespread AI deployment today is access to power. Some estimates suggest AI-driven data centers now consume more electricity than entire nations. The World Economic Forum projects a doubling of energy use by data centers from 2024 to 2027, driven by the energy-intensive nature of AI workloads. This surge in electricity demand is transforming the utilities industry and redefining how and where data centers are builtpower is no longer a given. In the U.S, electricity usage is growing for the first time in over a decade largely because of data center consumption. Meanwhile, big tech is even turning to nuclear power to fuel their long-term AI strategy, while data center builders are searching for land parcels in areas with excess power or resorting to building their own power infrastructure, often relying on natural gas generators. ENTER QUANTUM COMPUTING Quantum computers could be the key to reducing AIs rising energy consumption, offering a more efficient, scalable solution. Unlike traditional computers that evaluate one possibility at a time, quantum computers are designed to explore complex problem landscapes more efficiently, making them well-suited for tackling certain challenges that can be difficult, time-consuming, or costly for classical systems. This enables them to potentially provide solutions faster, at higher quality, and with greater efficiency. While AI excels at uncovering patterns and predictions, quantum computing identifies the most efficient solutions, making these two powerful technologies complementary. Quantum computers address problems that AI and classical methods struggle with, such as factoring large numbers and solving hard optimization challenges like vehicle routing and supply chain structuring. Here are three ways quantum computing could help mitigate the expected disruptive impact of AIs rising computational demands: Optimize data center placement and utility grid management Quantum computing could be used to identify optimal data center locations based on power availability or assist utility companies in streamlining grid planning and management to support both consumer and data center needs. GE Vernova, a global energy company, is using quantum computers today to identify weaknesses in the power grid and optimize responses for potential attacks on the grid. E.ON, a European multinational electric utility company, is now using annealing quantum computing to explore energy grid stability. Unlock opportunities for greater energy efficiency Early research shows the potential for quantum computing to reduce the amount of computational power needed to run AI workflows. A breakthrough published in Science demonstrated that our D-Wave quantum computer solved a magnetic materials simulation problem in minutes using just 12 kilowatts of power. This task would have taken one of the worlds most powerful exascale supercomputers, a massively parallel GPU system, nearly one million years to solve, consuming more electricity than the world uses annually. Applying these quantum computing techniques to blockchain hashing and proof of work could also result in substantial enhancements to security and efficiency, potentially reducing electricity costs by up to a factor of 1,000. Quantum computers are very energy efficient and may soon perform complex computations like those needed for blockchain or AI at a fraction of the power required today.Some of the worlds largest supercomputing facilities are now actively exploring how GPUs and quantum processing units could work together to improve problem solving and reduce energy consumption. In February, Forschungszentrum Jülich, a leading supercomputing center in Germany, purchased an annealing quantum computer to integrate with the Jülich UNified Infrastructure for Quantum computing (JUNIQ). This integration is expected to enable JUNIQ to connect to the JUPITER exascale computer, potentially enabling breakthroughs in AI and quantum optimization. JUPITER is anticipated to surpass one quintillion calculations per second. This will likely be the worlds first pairing of an annealing quantum computer with an exascale supercomputer, providing a unique opportunity to observe the technologys impact on AI computational challenges. Boost model efficiency and performance with quantum AI architectures Early evidence suggests that annealing quantum computers can be integrated into quantum-hybrid AI workflows, which could potentially enhance model efficiency and performance. Japan Tobaccos (JT) pharmaceutical division recently conducted a project that involved using a quantum-hybrid AI workflow to generate new molecules. Using this hybrid approach, JT enhanced the quality of its AI drug development processes, demonstrating that the quantum AI workflow generated more valid molecules with better drug-like qualities compared to classical methods alone.TRIUMF, Canada’s particle accelerator center, recently published a paper in npj quantum information demonstrating the first use of annealing quantum computing and deep generative AI to create novel simulation models for the next big upgrade of CERNs particle accelerator, the Large Hadron Colliderthe worlds largest particle accelerator. Traditional simulations of particle collisions are time-consuming and costly, often running on supercomputers for weeks or months. By merging quantum computing with advanced AI, the team was able to perform complex simulations more quickly, accurately and efficiently. HOW TO ADDRESS AIS POWER DRAIN WITH QUANTUM INNOVATION As AI adoption continues to accelerate, its insatiable demand for computational power is upending industries and straining global power resources. We need a better solution for addressing AIs power demands than simply adding more GPU clusters or building nuclear power plants. From optimizing energy grids and data center placemet to reducing GPU power consumption and enhancing AI model performance, annealing quantum computing offers a promising path forward. Tools like PyTorch plug-ins are even making it easy for developers to incorporate quantum into AI workflows to explore how the technology could address computational challenges. For business leaders navigating the energy-intensive AI era, adopting annealing quantum computing could unlock transformative efficiencies today and tomorrow. Alan Baratz, PhD is CEO of D-Wave.
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