Career Path Exploration

It looks like we finally have the data to confirm that the AI revolution has an unintended casualty: entry-level careers.... New Harvard research tracking 285,000 firms found that companies adopting AI have slashed junior hiring by 22% since early 2023, while senior roles keep growing. This isn't about layoffs - it's about fewer opportunities for entry level roles and those just beginning their careers. Wholesale and retail got hit hardest, with AI adopters hiring 40% fewer juniors per quarter. The likely culprit? AI easily automates routine communication, customer service, and documentation - exactly the tasks new grads typically handle. What does this mean? Business leaders need to think beyond short-term savings. Today's entry-level drought could create tomorrow's talent shortage. Recent grads should focus on skills that complement AI rather than compete with it. And HR teams face a balancing act between efficiency and maintaining career pipelines. The silver lining? Promotion rates for existing junior employees actually increased at AI-adopting firms. The companies that figure out how to use AI while nurturing junior talent will have a major advantage. Are we seeing a temporary adjustment or a fundamental shift in how careers begin? You can read the research here: https://lnkd.in/gbYecbc3 #AI #HiringTrends #CareerDevelopment #TalentAcquisition #FutureOfWork

If you’re AI-curious but can’t decide where to start, this one’s for you 👇 The AI space is vast. Buzzwords fly. Roles overlap. And it’s easy to get stuck wondering: 👉 Should I become a Data Scientist, ML Engineer, or Product Manager? Instead of chasing titles, map your strengths and figure out where you fit best in the AI lifecycle. 📌 I put together this infographic + a blog post to help you find your lane, with 10 clear roles you can actually train for (even without a PhD or a Stanford badge). 🚀 The 10 Career Paths in AI, Simplified: ➡️ AI/ML Researcher or Scientist – creating new algorithms, publishing papers, pushing the frontier ➡️ Applied ML Scientist / Data Scientist – solving real-world problems with models and experimentation ➡️ ML Engineer / MLOps / Software Engineer (ML) – taking models to production and scaling them ➡️ Data Engineer – building the infrastructure to move and manage data ➡️ Software Engineer – writing core product code with ML components ➡️ Data Analyst – analyzing data to drive insights and business impact ➡️ BI Analyst – working with KPIs, reporting, and decision frameworks ➡️ AI Consultant – advising teams and clients on adopting AI responsibly ➡️ AI Product or Program Manager – aligning AI capabilities with user needs and business goals ➡️ Hybrid Roles – wearing multiple hats across technical and strategic functions 🧭 How to choose the right one for you: → Start with your natural strengths: coding, communication, business thinking, or data sense → Identify the part of the AI lifecycle you enjoy most: research - build - deploy - iterate → Stack the right skills intentionally: • Coders: Python, PyTorch, prompt design, eval frameworks • Data Infra: SQL, Spark, Airflow, Lakehouse, vector DBs • Insights: Analytics, causal reasoning, dashboard tools • Translators: AI roadmap building, governance, storytelling → Focus on shipping evidence of work: demo apps, notebooks, open-source PRs, or experiments → Develop a T-shaped skill profile – go deep in one role, but stay conversational across others 💡 A few truths to keep in mind: → You don’t need to be a “10x coder” to work in AI → Problem-solving > job titles → Projects > perfect resumes → Cross-functional skills are a force multiplier – clear writing, ethical reasoning, and stakeholder empathy go a long way → There’s no “entry-level” in AI – just entry-level impact 📖 Curious to explore deeper? Check out the full blog, and save the infographic to use as a compass for your AI journey: https://lnkd.in/daQNHPyg

Without going too far out on a limb, I believe almost everyone would like two things from their jobs and careers: success and happiness. They want to do well financially, receive recognition for their accomplishments, enjoy their work as much as one can, and become happier as a person as a result. These are reasonable goals, but they can be a lot to ask—so many people, especially ambitious, hard-working leaders, simplify them in a logical way: They first seek success and then assume that success will lead to happiness. But this reasoning is flawed. Chasing success has costs that can end up lowering happiness, as many a desiccated, lonely workaholic can tell you. This is not to say that you have to choose between success and happiness. You can obtain both. But you have to reverse the order of operations: Instead of trying first to get success and hoping it leads to happiness, start by working on your happiness, which will enhance your success. Success and happiness are generally positively correlated, as many workforce studies have shown. From this correlation, many assume causation—from success to happiness. During my years as an executive, I found that people strongly believe that pay increases—especially big ones—will have a large and long-lasting effect on their job satisfaction. The data tells us a different story, however: Large wage increases have only a small and transitory effect on well-being. One study, for example, showed that if your job satisfaction is a 6 out of 10—not bad—then even if your boss doubles your pay, it will get you to about 6.5, and then it will fall back to about 6.2. Maybe getting a raise isn’t the best strategy to help you love your job. Much stronger and more positive results emerge, however, when researchers reverse the order, looking not at success’s effects on happiness, but happiness’s effects on success. Scholars in 2005 surveyed hundreds of studies—including experiments to establish causality—and concluded that happiness leads to success in many realms of life, including marriage, friendship, health, income, and work performance. Whether you are an employee or employer, it is a better investment to increase happiness at work and in life, rather than simply trying to increase measures of success.

Linear careers are breaking. Here's how to build something stronger: By 2027, AI automation will erode the value of more than 60% of specialized skills. Specialization used to mean security. Now, it’s a significant risk. In an increasingly complex world, intentionally broadening your skill base is the only defense. Starting as an entrepreneur, I was forced into discomfort: → Sales felt awkward → Design felt foreign → Management felt forced → Writing felt painful → Investing felt risky I wasn't an expert at any. In this credibility valley, most quit - because being average feels terrible. But, like compound interest, the longer you persist and intentionally add skills, something powerful emerges: Each new skill multiplies the others’ value. Eventually, these skills took me from starting a single business to running multiple companies and operating a fund. Here's the deeper truth I've learned: Specialists master depth. But risk tunnel vision. Generalists see patterns. But risk superficial understanding. The timing paradox: → Generalists thrive in chaos → Specialists dominate stability Linear paths may feel safer, but today they're more vulnerable than ever. The best defense isn't just adding skills, it's adding them intentionally. A simple compound skill advantage playbook: → Select adjacent skills before they’re obviously valuable → View discomfort as critical feedback, not as failure → Prioritize skill integration over isolated mastery → Constantly practice unlearning to adapt faster The uncomfortable truth: you'll never be the best at any single skill. You'll become irreplaceable through their combination. Your choice: Stay specialized and fragile, or become multidisciplinary and antifragile. What skill combination are you building? Share in the comments below.

Too many people see that "Open to Work" sign as an easy target to make a quick buck. From paid communities, to MLM-style courses, to folks with minimal experience branding themselves career coaches and charging thousands for their generic advice, folks are often exploiting people's desperation. But there ARE free resources out there: Pay Forward Coaching offers a free 1:1 coaching session as well as a free community with experienced coaches (and no upsell) EarnBetter offers a free AI resume tool and job board. Landed! is my newsletter for jobseekers where I share advice for navigating both job searches and the workplace (and I also share jobs on this page!) Lyft offers free rides to interviews and your first few weeks on the job. CareerOneStop is a free resource with tools for job search, and they have a lot of resources in particular for veterans, justice-impacted individuals, etc. Dress for Success Worldwide, Bottomless Closet, and Career Gear are just a handful of the many organizations that will help you get dressed for that interview or new job, and many offer additional supports to job seekers. I'm dropping a list of these resources in comments, and if you know of other totally free resources (with no upsell!), feel free to share them as well!

The notion that a "perfect" career in life sciences follows the rigid path of B.Sc. - M.Sc. - Ph.D. - Postdoc - Academic Professor is not just outdated – it's limiting your potential. As life science professionals, we possess a unique set of skills that extend far beyond the laboratory. Our analytical thinking, problem-solving abilities, and deep understanding of complex systems are invaluable across numerous industries. Consider this: You can drive innovation in biotech without running experiments daily. You can shape science policy without writing academic papers. You can lead product development in pharma without being tied to a bench. Are you truly leaving science behind by choosing these paths? Absolutely not. You're still: Interpreting scientific data, just in different contexts Communicating complex ideas, just to varied audiences Solving critical problems, just on a broader scale It's time to recognize that your worth isn't defined by your proximity to a pipette. Your value lies in your ability to apply scientific thinking to real-world challenges. Embrace the multitude of career options available to you. Your life science background is not a constraint – it's a launchpad for a fulfilling career aligned with your personal aspirations. Remember: You're not abandoning science by exploring diverse career paths. You're expanding its reach and impact. ... #LifeScienceCareers #careers #gethired #sciencejobs #beyondacademia #buildyourcareer

🚀 AI isn’t just replacing jobs—it’s creating an entirely new workforce. While we often debate which roles AI might eliminate, we overlook a bigger reality: the rise of AI-powered careers. According to the latest World Economic Forum (WEF) report, the job landscape is set for a massive shift by 2030: 🔹 170 million new jobs will emerge 🔹 92 million jobs will disappear And what’s driving this transformation? Artificial Intelligence. The fastest-growing roles today are being shaped directly by AI adoption, including: AI & Machine Learning Specialists Big Data Analysts Fintech Engineers AI-Augmented UX Designers Information Security Analysts Process Automation Experts 👀 Many of these roles didn’t exist at scale just a few years ago. And here’s the twist—it’s not just about tech! AI is creating demand for entirely new positions across industries: ✅ AI Ethics Leads to navigate responsible AI use ✅ AI Product Strategists to align AI with business goals ✅ Prompt Engineers to refine AI-generated content ✅ Decision Engineers to design workflows where humans and AI collaborate Organizations aren’t just hiring data scientists anymore. They need: ⚡ AI risk & governance specialists ⚡ AI product managers ⚡ AI ethicists This shift proves one thing: AI isn’t just a tool—it’s a mindset. The future belongs to leaders who can think with AI—strategically, creatively, and responsibly. That’s exactly what we’re cultivating in our Executive AI course—designed for those who want to lead the AI revolution, not chase it. 📢 Stay ahead. Stay adaptive. The AI-powered workforce is already here. Let’s grow together. Follow for more insights! 👇 Sarveshwaran Rajagopal #Leadership #AI #Technology #Innovation #FutureOfWork

“Let me burst your bubble first: there’s no standard career track anymore. Your father’s career playbook - engineering degree, join a good company, get promoted every 3-4 years, retire as General Manager — is as relevant today as a Nokia 3310. The professionals who are thriving today are the ones who understand how the landscape has changed. They’re ready to explore unconventional paths and make calls based on opportunity, not tradition.” I came across these lines in an article by The LHR Group, and they couldn’t have been truer. It reminded me of my own journey. My career path is far from linear. I started out as a lawyer, then did an MBA in Analytics, and today I’m working as a Product Manager. For the longest time, my father told me to do a traditional MBA after BCom because it was considered a safe option. But I explored different paths and then did a Tech MBA. It didn’t make sense to him until now — when he sees the advent of AI in almost everything. The truth is: traditional career paths are dead. The most exciting opportunities today aren’t on a straight road — they’re at the intersections. Law taught me how to think critically and structure arguments. Analytics sharpened my ability to make data-driven decisions. And product brings those skills together to solve real problems for users. I’ve learned that switching paths isn’t always a setback — it makes me more adaptable. Every pivot added a new layer to how I structure problems, decode them, and build things that matter. So if your journey feels messy, nonlinear, or “all over the place,” you’re probably on the right track. The world accepts non-linear résumés more than you think - it rewards people who can adapt, connect dots, and reinvent themselves. Would love to hear - has your career taken a few non-linear turns too?

AI’s impact on the workforce is no longer theoretical. New data from Anthropic provides one of the clearest pictures yet of how AI is actually being used in professional roles today. By analysing millions of real-world interactions with Claude AI, the study moves beyond speculation and reveals where AI is embedded in work, where its adoption remains low, and whether it is augmenting or automating professional tasks. Some key findings: 🔹 AI is now performing 25% or more of the tasks in 36% of occupations. 🔹 57% of AI use is augmentation, meaning workers use AI as a collaborator, refining and improving their work. 🔹 43% of AI use is automation, where AI completes tasks with little human involvement—raising questions about long-term shifts in work. 🔹 AI’s adoption is highest in mid-to-high-wage professions, particularly in software engineering, content creation, and data analysis. 🔹 Industries requiring physical labour or complex interpersonal skills see much lower AI usage—for now. This data brings important implications for education and workforce development. Rather than broad assumptions about AI’s role in work, institutions now have a clearer sense of where AI is being used, where it isn’t, and how qualifications may need to adapt. So, what does this mean for workforce preparation? The findings suggest that AI fluency will be essential in some fields, while in others, the focus must remain on human-led expertise—critical thinking, ethical reasoning, and leadership. The full article unpacks these insights further, exploring what this data means for jobs, education, and the future of work. 🔹 #AI 🔹 #FutureOfWork 🔹 #AIinEducation 🔹 #WorkforceDevelopment 🔹 #EdTech

AI is fundamentally reshaping our workforce, but the impacts are nuanced. The latest report, “Potential Labor Market Impacts of Artificial Intelligence: An Empirical Analysis,” by The White House Council of Economic Advisers, provides critical insights for leaders that will impact everyone's future.. 📊 Key Findings: ✅ 𝐆𝐫𝐨𝐰𝐭𝐡 𝐢𝐧 𝐇𝐢𝐠𝐡-𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲, 𝐀𝐈-𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐑𝐨𝐥𝐞𝐬 Roles requiring advanced AI skills have increased by 30% over the last five years. Positions such as AI ethics officers and data scientists are on the rise, indicating a shift toward more complex, creative work. Occupations that integrate AI effectively are growing twice as fast as average, suggesting AI's role in complementing human skills rather than replacing them. ❌ 𝐇𝐢𝐠𝐡 𝐑𝐢𝐬𝐤 𝐨𝐟 𝐉𝐨𝐛 𝐃𝐢𝐬𝐩𝐥𝐚𝐜𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐋𝐨𝐰-𝐒𝐤𝐢𝐥𝐥 𝐑𝐨𝐥𝐞𝐬 40% of current jobs are at risk due to high AI exposure but low skill requirements, particularly in administrative and routine manual tasks. These jobs are declining at a rate of 2% annually. Sectors like customer service and data entry are vulnerable, raising concerns about job security and economic stability in these fields. 📍 Regional Disparities: ✅ 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐢𝐧 𝐓𝐞𝐜𝐡 𝐇𝐮𝐛𝐬 Tech-centric regions like Silicon Valley show a high concentration of new, AI-driven job creation, reflecting significant economic opportunities for those regions. Urban centers with strong tech clusters are emerging as key players in AI employment, driving innovation and growth. ❌ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐟𝐨𝐫 𝐑𝐮𝐫𝐚𝐥 𝐚𝐧𝐝 𝐒𝐦𝐚𝐥𝐥𝐞𝐫 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐢𝐞𝐬 Rural areas and smaller towns are facing increased risks of job losses due to AI, without comparable opportunities for new AI-driven roles. This geographic imbalance could exacerbate regional economic disparities. 👉 Here are my questions for Leaders: 1️⃣ Are we ready to leverage AI’s potential while minimizing risks? How are we preparing our teams for a future where AI enhances human capability? 2️⃣ What is our reskilling strategy? With 40% of jobs potentially vulnerable, how are we investing in upskilling our workforce to transition into growth-oriented roles? 3️⃣ How can we balance geographic and economic disparities? Are we focusing enough on regional strategies to ensure inclusive growth? As leaders, our role is to harness AI's potential to foster a resilient, inclusive, and dynamic workforce. Are we ready to lead this change and shape the future of work?