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Were taught to think of raises as the holy grail of career and financial success. Annual performance reviews. Awkward remuneration conversations. Hoping (and sometimes praying) that your hard work gets noticed. If this is your earning strategy, you’re already behind. Forget the raise. In todays workplace, shaped by AI and economic flux, the smart earners are rewriting the rules. We cant say that raises dont matter, but theyre no longer the most effective path to earning more. Here are the five savvy steps to get you closer to making more. 1. Ditch the illusion of linearity Stay longer, get promoted, earn more. In todays landscape, it doesnt always work that way. Pay progression isnt neatly tied to tenure, and loyalty alone no longer guarantees growth. So too, for following a traditional career path, the well-trodden graduate to partner/executive/C-suite path. In the modern job economy, status has become more performative than financial. Earning more doesnt follow a straight line, nor is it built on hierarchy. Its built on leverage. A linear mindset ignores lateral moves and misses cross-industry opportunities, often with a significant financial upside. With global skills shortage, never have employees with in-demand skills held such a financial strategic advantage. But be smart: this is not an endorsement for job-hopping. Tenure is still important when in conjunction with in-demand skills. This combination can make you exponentially more valuable, opening doors to equity options and lucrative bonus structures. 2. Flip your view of a raise A raise isnt the only way to take home more pay: consider additional retirement contributions, performance bonuses, opportunities for upskilling, flexible work arrangements, or additional leave entitlements. These benefits have lower immediate costs to your employer, but compound your long-term opportunity for financial gain. When no direct salary increases are on the table, employers are often relieved to meet you halfway. A collaborative, rather than adversarial, approach demonstrates strategic thinking, further reinforcing your position as a high–value employee. 3. Revalue your contribution Too many people expect that increases happen regularly and every year. They dont. And when they do ask for more, its based on the increasing cost of living. Overall, these tactics yield crumbs. Move beyond base-level expectations: you want the cake. Salary growth and negotiations follow transformation. Revisit what you have contributed in the last 12 months and quantify it into a measurable business impact. Is it cost savings, revenue generation, value creation, new efficiencies, or some kind of innovation? If you can, put a dollar amount on it. And from now on, commit to a monthly log of contribution and value creationand take it with you for next years salary discussions. Make a compelling business case based on data and the market, not opinion, helping you pitch confidently rather than passively. 4. Look for multiple income streams This is not a second job or building on your portfolio. This is a long game, being entrepreneurial with your skills within your existing professional framework. Develop expertise and relationships that generate opportunities beyond your existing role. This could be consulting or advisory work, keynote speaking engagements, expert commentary contributions, board positions, or industry committee roles. The key is leveraging your existing job to create premium–paying opportunities that further enhance rather than compete. This could work perfectly if you have negotiated upskilling, professional development and additional leave entitlements. These activities dont just create income; they build on your professional reputation, expand your network, placing you as an industry leader. Here is where it gets interesting: a higher profile strengthens your position in future salary negotiations, a virtuous cycle. 5. Position yourself as a product (because you are one) AI has automated many tasks, but it cant replace strategic visibility. Just as successful product marketing showcases attributes and unique selling points, so too must you ensure your professional contributions are recognized by the right stakeholders. Position yourself in high-profile projects and meetings. Ensure your ideas are heard and your work is clearly attributed to you, not lost in the we, us, or team. Collaborative input has its place; so do individual contributions, and not at the expense of the other. Just as products need care, attention, and servicing to keep them in top form, so do you. Ensure you are recharging, caring for your mind and body, and pursuing growth opportunities to enhance your professional worth. Rethinking your earning strategy means recognizing the more profound shifts in how we work and whats valued. Understanding these shifts allows you to approach your career and earning potential not as an ordered system, but as a platform: adaptable, strategic, and ready for whats next.
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E-Commerce
The advent of generative AI has elicited waves of frustration and worry across academia for all the reasons one might expect: Early studies are showing that artificial intelligence tools can dilute critical thinking and undermine problem-solving skills. And there are many reports that students are using chatbots to cheat on assignments. But how do students feel about AI? And how is it affecting their relationships with peers, instructors and their coursework? I am part of a group of University of Pittsburgh researchers with a shared interest in AI and undergraduate education. While there is a growing body of research exploring how generative AI is affecting higher education, there is one group that we worry is underrepresented in this literature, yet perhaps uniquely qualified to talk about the issue: our students. Our team ran a series of focus groups with 95 students across our campuses in the spring of 2025 and found that whether students and faculty are actively using AI or not, it is having significant interpersonal, emotional effects on learning and trust in the classroom. While AI products such as ChatGPT, Gemini or Claude are, of course, affecting how students learn, their emergence is also changing their relationships with their professors and with one another. Its not going to judge you Most of our focus group participants had used AI in the academic settingwhen faced with a time crunch, when they perceive something to be busy work, or when they are stuck and worry that they cant complete a task on their own. We found that most students dont start a project using AI, but many are willing to turn to it at some point. Many students described positive experiences using AI to help them study or answer questions, or give them feedback on papers. Some even described using AI instead of a professor, tutor or teaching assistant. Others found a chatbot less intimidating than attending office hours where professors might be demeaning. In the words of one interviewee: With ChatGPT you can ask as many questions as you want and its not going to judge you. But by using it, you may be judged. While some were excited about using AI, many students voiced mild feelings of guilt or shame about their AI use due to environmental or ethical concerns, or just coming across as lazy. Some even expressed a feeling of helplessness, or a sense of inevitability regarding AI in their futures. Anxiety, distrust and avoidance While many students expressed a sense that faculty members are, as one participant put it, very anti-ChatGPT, they also lamented the fact that the rules around acceptable AI use were not sufficiently clear. As one urban planning major put it: I feel uncertain of what the expectations are, with her peer chiming in, Were not on the same page with students and teachers or even individually. No one really is. Students also described feelings of distrust and frustration toward peers they saw as overly reliant on AI. Some talked about asking classmates for help, only to find that they just used ChatGPT and hadnt learned the material. Others pointed to group projects, where AI use was described as a giant red flag that made them think less of their peers. These experiences feel unfair and uncomfortable for students. They can report their classmates for academic integrity violationsand enter yet another zone in which distrust mountsor they can try to work with them, sometimes with resentment. It ends up being more work for me, a political science major said, because its not only me doing my work by myself, its me double checking yours. Distrust was a marker that we observed of both student-to-teacher relationships and student-to-student relationships. Learners shared fears of being left behind if other students in their classes used chatbots to get better grades. This resulted in emotional distance and wariness among students. Indeed, our findings reflect other reports that indicate the mere possibility that a student might have used a generative AI tool is now undercutting trust across the classroom. Students are as anxious about baseless accusations of AI use as they are about being caught using it. Students described feeling anxious, confused and distrustful, and sometimes even avoiding peers or learning interactions. As educators, this worries us. We know that academic engagementa key marker of student successcomes not only from studying the course material, but also from positive engagement with classmates and instructors alike. AI is affecting relationships Indeed, research has shown that faculty-student relationships are an important indicator of student success. Peer-to-peer relationships are essential too. If students are sidestepping important mentoring relationships with professors or meaningful learning experiences with peers due to discomfort over ambiguous or shifting norms around the use of AI technology, institutions of higher education could imagine alternative pathways for connection. Residential campuses could double down on in-person courses and connections; faculty could be incentivized to encourage students to visit during office hours. Faculty-led research, mentoring and campus events where faculty and students mix in an informal fashion could also make a difference. We hope our research can also flip the script and disrupt tropes about students who use AI as cheaters. Instead, it tells a more complex story of students being thrust into a reality they didnt ask for, with few clear guidelines and little control. As generative AI continues to pervade everyday life, and institutions of higher education continue to search for solutions, our focus groups reflect the importance of listening to students and considering novel ways to help students feel more comfortable connecting with peers and faculty. Understanding these evolving interpersonal dynamics matters because how we relate to technology is increasingly affecting how we relate to one another. Given our experiences in dialogue with them, it is clear that students are more than ready to talk about this issue and its impact on their futures. Acknowledgment: Thank you to the full team from the University of Pittsburgh Oakland, Greensburg, Bradford and Johnstown campuses, including Annette Vee, Patrick Manning, Jessica FitzPatrick, Jessica Ghilani, Catherine Kula, Patty Wharton-Michael, Jialei Jiang, Sean DiLeonardi, Birney Young, Mark DiMauro, Jeff Aziz, and Gayle Rogers. Elise Silva is the director of policy research at the Institute for Cyber Law, Policy, and Security at the University of Pittsburgh. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
In my writing and rhetoric courses, students have plenty of opinions on whether AI is intelligent: how well it can assess, analyze, evaluate, and communicate information. When I ask whether artificial intelligence can think, however, I often look upon a sea of blank faces. What is thinking, and how is it the same or different from intelligence? We might treat the two as more or less synonymous, but philosophers have marked nuances for millennia. Greek philosophers may not have known about 21st-century technology, but their ideas about intellect and thinking can help us understand whats at stake with AI today. The divided line Although the English words intellect and thinking do not have direct counterparts in ancient Greek, looking at ancient texts offers useful comparisons. In Republic, for example, Plato uses the analogy of a divided line separating higher and lower forms of understanding. Plato, who taught in the fourth century BCE, argued that each person has an intuitive capacity to recognize the truth. He called this the highest form of understanding: noesis. Noesis enables apprehension beyond reason, belief, or sensory perception. Its one form of knowing somethingbut in Platos view, its also a property of the soul. Lower down, but still above his dividing line, is dianoia, or reason, which relies on argumentation. Below the line, his lower forms of understanding are pistis, or belief, and eikasia, or imagination. Pistis is belief influenced by experience and sensory perception: input that someone can critically examine and reason about. Plato defines eikasia, meanwhile, as baseless opinion rooted in false perception. In Platos hierarchy of mental capacities, direct, intuitive understanding is at the top, and moment-to-moment physical input toward the bottom. The top of the hierarchy leads to true and absolute knowledge, while the bottom lends itself to false impressions and beliefs. But intuition, according to Plato, is part of the soul, and embodied in human form. Perceiving reality transcends the bodybut still needs one. So, while Plato does not differentiate between intelligence and thinking, I would argue that his distinctions can help us think about AI. Without being embodied, AI may not “think or understand the way humans do. Eikasiathe lowest form of comprehension, based on false perceptionsmay be similar to AIs frequent hallucinations,” when it makes up information that seems plausible but is actually inaccurate. Embodied thinking Aristotle, Platos student, sheds more light on intelligence and thinking. In On the Soul, Aristotle distinguishes active from passive intellect. Active intellect, which he called nous, is immaterial. It makes meaning from experience, but transcends bodily perception. Passive intellect is bodily, receiving sensory impressions without reasoning. We could say that these active and passive processes, put together, constitute thinking. Today, the word intelligence holds a logical quality that AIs calculations may conceivably replicate. Aristotle, however, like Plato, suggests that to think requires an embodied form and goes beyond reason alone. Aristotles views on rhetoric also show that deliberation and judgment require a body, feeling, and experience. We might think of rhetoric as persuasion, but it is actually more about observation: observing and evaluating how evidence, emotion, and character shape peoples thinking and decisions. Facts matter, but emotions and people move usand it seems questionable whether AI utilizes rhetoric in this way. Finally, Aristotles concept of phronesis sheds further light on AIs capacity to think. In Nicomachean Ethics, he defines phronesis as practical wisdom or prudence. Phronesis involves lived experience that determines not only right thought, but also how to apply those thoughts to good ends, or virtuous actions. AI may analyze large datasets to reach its conclusions, but phronesis goes beyond information to consult wisdom and moral insight. “Thinking” robots? Today, AI takes on many physical forms, from autonomous vehicles and humanoid robots to healthcare robots and drones. If intelligence requires embodiment, as Plato and Aristotle believed, it might seem as if AI is coming closer than ever to human thinking. AI does take on many forms, but theyre still not bodies; they run on code, algorithms, and datasets. Intuitive understanding, emotion, integrity, and practical wisdom seem to require an embodied, perishable form that is moved by experience. Perhaps we should ask AI itselfso I did. When I prompted ChatGPT with the simple question, Can you think?, it responded: I can process information, analyze patterns, and generate responses based on data. So, in some ways, it might seem like Im thinking. But its not thinking like a human does. I dont have consciousness, emotions, intentions, or awareness. Everything I do is based on patterns learned from huge amounts of text and a set of rules and probabilities. I can simulate certain aspects of thinking, but I dont truly think or understand in the human sense. That response aligns with Plato and Aristotles views. For now. Ryan Leack is an assistant professor of writing at USC Dornsife College of Letters, Arts and Sciences. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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