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If youre planning on buying a PC, laptop, or cell phone in the coming months, a word of advice for you before Christmas: Buy now, not later. Prices are likely set to spike in the new yeardue to a shortage of memory chips. Memory and storage for DRAM and NAND, two major types of computer memory, have seen costs rise between 30 and 40%, year-on-yearin some cases, theyre even doubling. This impacts the bill of materials (BOMs), or the cost of individual items to make, PCs, and especially low-end smartphones, where margins are thin and the proportional cost increase is more severe. The sudden spike in memory prices is part of a decades-long pattern of semiconductor supply cyclesbut this one is coming unusually fast, driven by unexpected demand from big tech companies building data centers for AI training and inference. There is an occasional cycle of supply shortages or some high demand bridges coming in once or twice a decadeit goes about 40 years back, says Runar Bjrhovde, research analyst at Canalys. He estimates that we’ve experienced seven “steep” cycles so far. What’s different now, he says, is the speed and the cause. This has developed really, really quickly. As a result, the foundries manufacturing chips and memory are prioritizing the high-performance compute chips needed for data centers, as well as higher-paying customers like Microsoft, Google, and Amazon Web Services, because scarce raw materials and limited capacity force them to funnel resources to the most profitable segments. As memory components become more limited and more expensive, manufacturers face increasing pressure to raise prices, says Anthony Scarsella, research director at IDC, which tracks cell phone shipments and sales. While some OEMs will inevitably be forced to raise prices, others will adjust their portfolio towards pricier models with higher margins to absorb some of the memory impact on BOM, he says. Next year will be a challenging time for the industry. Because of that, the average price of a smartphone will rise to around $465 next year, up from $457 in 2025 according to IDC data, even as total shipments in 2026 dip slightly because some buyers are priced out or delayed by component shortages. The biggest squeeze is expected in the low-to-mid range Android segment, where customers are most sensitive to price rises and vendors have the least room to absorb higher memory costs. On PCs, the picture is similar. A PC makes up about 15 to 18% of the bill of materials that goes in putting together a PC, says Bjrhovde of the memory and storage share. It entirely seems to be driven by the extreme data center demand that’s happening currently, says Runar Bjrhovde, research analyst at Canalys. So a lot of Nvidia investments are really pushing a lot of companies doing semiconductors forward. Bjrhovde isnt convinced that therell be ludicrous price rises, estimating that costs to end users could rise by 10% or 20%. Nevertheless, on a several-hundred-dollar device, that can make an impact. Some of that could be mitigated by smartphone companies stomaching some of the rise and reducing their margins, Bjrhovde says. That shift could also change what you actually get when you unbox a device. In some categories, especially budget phones, vendors are more likely to hold the price and trade down the quality of other parts outside of the memory, such as their camera, display, or processor. But theres only so much they can trim before the device becomes so outmoded that its not worth buying. The list of components that can be downgraded is extensive, Scarsella reckons. Most buyers may not notice the trade-offs. I think the average smartphone is upgraded every three years or so, Bjrhovde says. He suggests that at the point of upgrade, you might well have forgotten what you paid for your last device. But if youre the kind of customer who buys the latest iPhone Pro or top-end Android, youre likely to end up paying a little more, or getting slightly less storage for the same sticker price. For now, prices have held steady, with the cost increase not yet feeding through to end-user pricing. But that will soon change, warn the experts. So if youre looking to upgrade your PC or phone, bringing that purchase forward could save money. Industry executives are warning the memory crunch has only just started.
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The conversation about AI in the workplace has been dominated by the simplistic narrative that machines will inevitably replace humans. But the organizations achieving real results with AI have moved past this framing entirely. They understand that the most valuable AI implementations are not about replacement but collaboration. The relationship between workers and AI systems is evolving through distinct stages, each with its own characteristics, opportunities, and risks. Understanding where your organization sits on this spectrumand where its headedis essential for capturing AIs potential while avoiding its pitfalls. Stage 1: Tools and Automation This is where most organizations begin. At this stage, AI systems perform discrete, routine tasks while humans maintain full control and decision authority. The AI functions primarily as a productivity tool, handling well-defined tasks with clear parameters. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity? ","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514,"shareable":false,"slug":""}} Examples are everywhere: document classification systems that automatically sort incoming correspondence, chatbots that answer standard customer inquiries, scheduling assistants that optimize meeting arrangements, data entry automation that extracts information from forms. The key characteristic of this stage is that AI operates within narrow boundaries. Humans direct the overall workflow and make all substantive decisions. The AI handles the tedious parts, freeing humans for higher-value work. The primary ethical considerations at this stage involve ensuring accuracy and preventing harm from automated processes. When an AI system automatically routes customer complaints or flags applications for review, errors can affect real people. Organizations must implement quality controls and monitoring to catch mistakes before they cause damageparticularly for vulnerable populations who may be less able to navigate around system errors. Stage 2: Augmentation and Advice As organizations grow comfortable with AI systems, they typically progress to models where AI not only executes tasks but provides analysis and recommendations that inform human decision-making. At this stage, predictive analytics tools might identify emerging patterns in customer behavior, enabling more proactive business strategies. Risk assessment systems might analyze historical data to flag potential compliance issues. AI-powered diagnostics might suggest possible causes for equipment failures or patient symptoms. The critical distinction is that while AI can generate insights humans couldn’t produce alone by finding patterns in datasets too large for any person to analyze, human judgment remains the final authority for interpreting and acting on these insights. This is where new risks emerge. Over-reliance on AI recommendations becomes a real danger. Confirmation bias can creep in, with humans selectively accepting AI insights that align with their preexisting views while dismissing those that challenge their assumptions. The responsible approach at this stage requires humans to understand how the AI arrived at its recommendationswhat data it was trained on, what might have changed since training, whether there is any reason to suspect bias. It can be just as problematic when humans reject good AI advice because they don’t understand or trust it as when they blindly accept bad advice. Stage 3: Collaboration and Partnership This stage represents a more fundamental shift. Rather than a clear delineation between machine tasks and human decisions, humans and AI work as teams with complementary capabilities and shared responsibility. The relationship becomes fluid and interactive. AI systems actively adapt based on human feedback, while humans modify their approaches based on AI-generated insights. The boundary between AI work and human work blurs. Consider emergency response scenarios in which human teams work alongside AI systems during crises. The AI continuously monitors multiple data streamsweather patterns, traffic conditions, resource availability, historical response dataand suggests resource allocations. Humans accept, modify, or override these suggestions based on contextual knowledge not available to the system. The AI learns from these human interventions, improving its future recommendations. The humans develop intuitions about when to trust the AI and when to rely on their own judgment. This is where accountability becomes genuinely complicated. When outcomes result from humanAI teamwork, who bears responsibility for errors? If an AI recommends a course of action, a human approves it, and things go wrong, the question of fault is far from straightforward. Organizations operating at this stage need new governance frameworks that maintain clear lines of human accountability while enabling productive partnerships. This goes beyond the need to determine legal responsibility; it is fundamental to maintaining trust, both within the organization and with external stakeholders. Stage 4: Supervision and Governance The most advanced relationship model involves humans establishing parameters, providing oversight, and managing exceptions while AI systems handle routine operations autonomously. This represents a significant evolution from earlier stages. Humans shift from direct task execution or decision-making to a role focused on setting boundaries, monitoring performance, and intervening when necessary. An AI system might autonomously process insurance claims according to established policies, with humans reviewing only unusual cases or randomly sampled decisions to ensure quality control. A trading algorithm might execute transactions within defined parameters, with human supervisors monitoring for anomalies and adjusting constraints as market conditions change. The efficiency gains can be enormous. But so can the risks. The danger of automation complacency grows substantially at this stage. Human overseers may fail to maintain appropriate vigilance over AI systems that usually perform correctly. When you are supervising a system that makes the right call 99% of the time, it is psychologically difficult to stay alert for the 1% of cases that require intervention. Organizations must therefore implement robust oversight mechanisms that keep humans meaningfully engaged rater than performing a purely nominal supervisory role. Gamification of error identification and correction may offer a valuable path forward here, with a game layer of errors to catch sleeping overseers overlaid onto highly reliable systems that rarely err. Navigating the Progression Not every organization needs to progress through all four stages, and not every function within an organization should be at the same stage. The appropriate level of humanAI collaboration depends on the stakes involved, the maturity of the AI technology, and the organizations capacity for governance. High-stakes decisionsthose affecting peoples rights, safety, or significant financial interestsgenerally warrant more human involvement than routine administrative tasks. Novel applications of AI, where the technologys limitations are not yet well understood, require closer human oversight than established applications with proven track records. But regardless of where your organization sits on this spectrum, certain principles apply universally: Understand the AIs capabilities and limitations. At every stage, effective collaboration requires humans who grasp not just what the AI can do, but where it is likely to fail. This understanding becomes more important, not less, as AI systems take on greater autonomy. Maintain meaningful human accountability. The fundamental principle that humans must remain accountable for consequential decisions does not change as AI becomes more capable. What changes is how that accountability is structured and exercised. Design for evolution. The relationship between humans and AI systems isnt static. Organizations should build governance frameworks that can adapt as AI capabilities advance and as they develop greater understanding of how humanAI collaboration works in their specific context. Invest in the human side. The most sophisticated AI system delivers limited value if the humans working with it dont understand how to collaborate effectively. Training, cultural development, and organizational design are as important as the technology itself. The organizations that thrive in the AI era wont be those that simply deploy the most advanced systems. They will be those who master the art of human-AI collaborationunderstanding when to rely on AI capabilities, when to assert human judgment, and how to create partnerships that leverage the distinctive strengths of both.Adapted from Reimagining Government: Achieving the Promise of AI, by Faisal Hoque, Erik Nelson, Tom Davenport, et al. Post Hill Press. Forthcoming January 2026. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/creator-faisalhoque.png","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/faisal-hoque.png","eyebrow":"","headline":"Ready to thrive at the intersection of business, technology, and humanity? ","dek":"Faisal Hoques books, podcast, and his companies give leaders the frameworks and platforms to align purpose, people, process, and techturning disruption into meaningful, lasting progress.","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/faisalhoque.com","theme":{"bg":"#02263c","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#ffffff","buttonHoverBg":"#3b3f46","buttonText":"#000000"},"imageDesktopId":91420512,"imageMobileId":91420514,"shareable":false,"slug":""}}
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What can a pair of pants tell you about leadership? Much more than you think. How do you feel when you pull a pair of non-stretch jeans straight from the dryer? Theyre stiff. Way too tight. The waistband digs into your belly. Now picture trying to work an eight-hour day in them. That discomfortand sense of restrictionis exactly what it feels like to work for a micromanager. On the other end of your closet are those oversized sweatpantstheyre comfy, but theres no shape (or direction) to them at all . . . kind of like a workplace where everyone might like the manager, but no one has any idea whats actually expected or where theyre headed. Between those two extremes sits the gold standard of workplace comfort: cozy joggers. Stretchy. Supportive. They move with you, not against you. If youve ever worked for a leader who gives you the right balance of support and freedom, you know exactly how good that kind of fit feels. Because managers not only usually fall into one of these categoriestight jeans, oversized sweatpants or cozy joggersbut their teams respond accordingly. Heres what those leadership styles look like, why they matter and how to move toward the cozy joggers approach that gets the results that every style of leader is looking for. The Tight Jeans Manager: Restrictive, Rigid and Always Tugging at the Seams We all know that managerthe one who wants to sign off on every sentence, join every meeting and get updates so frequently you spend more time summarizing your work than doing it. Tight jeans managers often dont mean to restrict their teams. In fact, they usually come from a good place: they care deeply about the work and want to maintain high standards. But like those freshly washed jeans, this style leaves no room to move. How to spot a tight jeans manager: They jump in to fix work instead of guiding. They insist on approving even the smallest decisions. They monitor progress constantly. They prefer their way over any new approach. They struggle to let go of tasks they used to do themselves. And the impact on the team is real. People feel stressed and stuck. They stop speaking up or trying new things because theyre afraid of getting it wrong. Meetings turn into long status updates instead of actually solving problems. Everything slows (way) down because nothing can happen without the manager weighing in on every little thing. To loosen the metaphorical waistband, tight-jeans leaders can ask themselves: Is this about quality, or is it about control? Whats the actual risk if I step back? If I never delegate, am I prepared to own this forever? Micromanagement might feel productive in the moment, but it turns leaders into bottlenecks and employees into order-takers. Great managers recognize when theyre clinging too tightly and intentionally make space for others to stretch. The Oversized Sweatpants Manager: Comfy but Directionless If tight jeans restrict movement, oversized sweatpants eliminate shape altogether. These are the managers who pride themselves on being hands-off, but in their quest to avoid micromanaging, they end up providing almost no guidance at all. Once again, the intention is usually goodtrust your people, give them room, empower thembut empowerment without clarity quickly turns into ambiguity. How to spot an oversized sweatpants manager: They assume teams will figure it out. They dont have regular 1:1 meetings, just find me if you need me (but never seem to be available). They dont set clear expectations or deadlines. They rarely give feedbackunless something goes wrong. They avoid hard conversationsso the team ends up side-texting about it. At first, this style can feel freeing, especially for high performers. But after the initial cozy comfort wears off, people get frustrated. They arent sure what good looks like. They dont know how decisions are being made. They cant get any (much-needed) help. Even top talent needs an idea of the what, how, and why. To add structure without sliding into micromanagement, these managers can focus on: Clear expectations: What does success look like? Lightweight checkpoints: Not every project needs a meetingbut a text, Slack message, or short huddle goes a long way. Actionable feedback: Not looks good, but This direction works because . . . Its not about controlling every moveits about making sure everyone has what they need. The Cozy Joggers Manager: Flexible, Supportive, and Built for Real Work The magic of cozy joggers is their blend of stretch and structure. They hold their shape, but they dont hold you back. Theyre comfortable without being sloppy. Theyre supportive without being stiff. Cozy joggers managers operate the same way. They encourage autonomy while offering guidance. They give direction but dont dictate. Theyre not hovering, but theyre not disappearing. Theyre reliable, predictable and consistentthree qualities that transform team culture (and dont require any extra budget). Signs youre working with a cozy joggers manager: They communicate expectations clearly, including context (not just instructions). They ask questions instead of giving orders. They check in without it feeling like surveillance. They help employees grow instead of doing the work for them. They trust their teamsand their teams trust them back. Most importantly, these managers create environments where people feel both supported and capablethe real sweet spot of leadership. Managers who want to move into this style can build three simple habits: Communicate the why: Great managers explain the purpose behind the work. It builds alignment and better decision-making. Replace answers with questions: Guiding questions help employees think critically instead of relying on the manager for every answer. Build autonomy gradually: Start with more structure and intentionally pull back as confidence grows. This is leadership thats effectivebecause it builds your people up instead of adding more to their plates. You Wont Be Cozy Joggers Every DayBut You Can Always Adjust Managers are human. We all have tight-jeans days when stress makes us hold on too tight, and oversized-sweatpants weeks when were stretched thin so we cant be as present. The goal isnt to be perfectits to be aware and make the small adjustments that matter. The question every leader should ask is: What does my team need from me right nowstructure, space, or a blend of both? Effective leadership is about finding that in-between spacegiving enough support to guide without taking over, and offering enough autonomy without disappearing. Your team doesnt need perfect; they need steady, clear, human direction and room to do their best work. Because in the end, leadership is a lot like what we wear: The right fit changes everything.
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