Career Pathways in Technology

👷♂️ Picture this: a robot tiling the floor space of four tennis courts — 1,000 m² — in just one day. These robots are not prototypes. They already exist. And they’re a perfect example of where AI-driven automation shines: Repetitive. Physically punishing. Unforgiving of error. But here’s what really matters: Every square meter a robot lays is also reshaping the skills mix on site. We gain speed, precision, and fewer injuries… but we risk sidelining the tradespeople whose craft built our cities. The untold story? The winners won’t be the robots alone — but the humans who move up the value chain: > Robot maintenance & calibration > Quality assurance & oversight > AI-driven project management If we don’t invest in these upskilling pathways now, automation becomes a zero-sum game. If we do, it becomes a win-win — safer sites, faster delivery, and jobs that value human creativity and judgment over back-breaking repetition. 💡 My take: Automation shouldn’t erase workers. It should elevate them. The question is whether governments, companies, and unions act fast enough to make that real. 👉 Have you seen an effective reskilling program in construction (or any hands-on industry) that could be a model here? #AI #Automation #FutureOfWork #Construction #Upskilling

What do you know about Physical AI—besides robots? (Part 2) Physical AI isn’t just about giving AI a body—it’s far more challenging than we might expect. Unlike software AI, which can be broadly applied across industries with relatively minor adaptations, Physical AI is deeply industry-specific. Since it’s tightly linked to the physical world, every industry has unique constraints and requirements that shape how AI is built and deployed. Let’s take a look at how different it can be: (the examples I chose here are all have commercial products on the market) 1️⃣ Different Industries = Different Physical Constraints Each field has its own technical, safety, and material challenges, meaning no single AI model can handle them all: - AI in prosthetics must adapt to individual users’ biomechanics and interpret neural signals in real-time. - AI in self-driving cars must process road conditions, predict human behavior, and make split-second safety decisions. 2️⃣ Hardware & Materials Are Not Universal AI-powered prosthetics & exoskeletons → Need low-latency AI chips that process movement instantly. AI-powered glasses → Require miniaturized, ultra-low-power AI that fits in lightweight frames. AI-powered infrastructure (smart traffic, self-healing roads) → Needs durable AI-embedded materials and edge computing. 💡In software AI (like LLMs), scaling mostly requires more data and better algorithms, but Physical AI needs custom-built hardware for each industry. 3️⃣ Safety & Regulation Depend on the Industry AI prosthetics & medical devices → Require FDA or CE approval to ensure patient safety. AI-powered industrial robots → Must comply with OSHA and workplace safety standards. 💡Each industry has its own regulations and risks to manage - no single "AI safety rulebook" is available. 4️⃣ Learning & Adaptation is Industry-Specific Physical AI can’t rely on generic pre-training (unlike software AI such as LLMs)—it needs industry-specific real-world adaptation. Such as: AI-powered prosthetics → Need personalized learning to adapt to each user’s unique gait. AI-powered urban infrastructure → Requires AI that adjusts to local traffic patterns, weather, and real-time events. ... This is also why Physical AI progress happens in specific industries first, rather than scaling across all industries at once like software AI. 📍If you are a leader and looking for more in-depth Executive-level AI insights, check here (10 weeks program, expertise required) ➡️https://bit.ly/3C3GSgF So, if you were to develop a Physical AI product today, which industry would you target first? Image: CB Insights _______________ For more on AI, please check my previous posts. I share my learning journey here. Join me and let's grow together. Alex Wang #artificialintelligence #machinelearning #innovation #technology

The most consequential decisions in a career are often the ones that look irrational in the moment. A common pattern in high-growth companies is that the most impactful roles are often the least defined at the outset. The title is unclear. The scope is fluid. By traditional metrics, it can look like a step down. That is the point. Early in your career, and often well into it, people optimize for position. They evaluate title, compensation, reporting lines. They try to map a linear path forward. This is a legacy framework from a more static economy. But in periods of technological acceleration, the variables that matter shift. High-growth companies compress time and push you beyond your prior experience. They push you to develop new skills quickly and operate beyond your prior experience. One year of work can feel like five. In those conditions, the job description becomes secondary. What matters is whether you are working on important problems alongside people who raise your standard. Careers tend to follow momentum. The environments you choose shape the trajectory more than the plans you start with. The challenge is that these opportunities rarely present themselves clearly. They look incomplete. Uneven. Risky. The question is whether you can recognize directional momentum early and commit before the outcome is fully defined. #schmidtsights

𝗪𝗵𝘆 𝗟𝗶𝗻𝘂𝘅 𝗦𝗸𝗶𝗹𝗹𝘀 𝗔𝗿𝗲 𝗡𝗼𝗻-𝗡𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 𝗶𝗻 𝗜𝗧 𝗧𝗼𝗱𝗮𝘆  Linux is not just an operating system; it’s the backbone of modern IT infrastructure. Whether it’s web servers, cloud platforms, or embedded systems, Linux is everywhere. If you’re serious about building your career, learning the basics of Linux is a must.  Why Linux Matters   1. 𝗧𝗵𝗲 𝗕𝗮𝗰𝗸𝗯𝗼𝗻𝗲 𝗼𝗳 𝗜𝗧 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: Linux powers most web, cloud platforms, and enterprise servers.   2. 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀: Understanding Linux gives you a deep dive into open-source development and collaboration.   3. 𝗦𝗵𝗲𝗹𝗹 𝗠𝗮𝘀𝘁𝗲𝗿𝘆: Linux shell skills are invaluable for automation and streamlining workflows across IT domains.   4. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗳𝗼𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗮𝗻𝗱 𝗖𝗹𝗼𝘂𝗱: Most DevOps workflows and cloud services rely heavily on Linux. 5. 𝗕𝘂𝗶𝗹𝘁-𝗜𝗻 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: Its robust security makes Linux indispensable for cybersecurity roles.  Building Your Linux Expertise   1. Learn the basics: core commands, file system navigation, and permissions.   2. Practice shell scripting to automate repetitive tasks.   3. Explore popular distributions like Ubuntu, CentOS, and Fedora.   4. Set up virtual environments to get hands-on experience with Linux systems.   5. Dive into Linux security tools like SELinux, iptables, and auditing.   6. Validate your skills with LPIC, Red Hat, or CompTIA Linux+ certifications.   7. Contribute to open-source projects to gain real-world experience and build connections.  Mastery of Linux can lead to DevOps, cloud engineering, cybersecurity, and beyond opportunities. Consistency and hands-on practice are key to becoming proficient. If you’re looking to future-proof your career, Linux is the skill to invest in.  Where are you in your Linux learning journey? Share your thoughts or tips below!  

STATE OF THE PRODUCT JOB MARKET IN EARLY 2026 In spite of the headlines about layoffs and AI taking jobs, we’re actually seeing a lot of promising signs in tech hiring, and some interesting new trends: 1. PM openings are at the highest levels we’ve seen in over three years 2. AI hasn’t slowed the demand for software engineers (at least not yet) 3. AI roles in general are absolutely exploding 4. Design roles have plateaued 5. The Bay Area is increasing in importance 6. Remote work opportunities continue to decline 7. Despite ongoing layoffs, the overall number of tech jobs continues to grow Full report: https://lnkd.in/gHPzuDJa While these numbers are promising, I know a lot of people are having a hard time finding a job right now. And more openings doesn’t automatically mean people are finding jobs more quickly. For anyone in that situation, first of all, I’m sorry. Second, I’m working on ways to help. Until then, check out the end of the post above for a bunch of resources I’ve collected that’ll improve your odds of landing a gig. This analysis is based on data from TrueUp, one of my favorite collaborators and sources of data. They track job openings at tech companies and top startups around the world (over 9,000 companies) and make it easy to browse open gigs. Their data looks at roles at tech companies—the most sought-after and lucrative jobs. (It doesn’t include roles at non-tech companies and consulting agencies.) Browse open roles here: https://trueup.io/jobs

Confusing "product owner" with "product manager" is like confusing "Scrum Master" with "engineering leader." Product Owner is a role in Scrum. Product Manager is a career. This distinction matters more than most companies realize. When you only recognize Product Owner roles, you're signaling that product work is just about managing backlogs and running ceremonies. That's tactical execution, not strategic leadership. The actual career path looks like this: Associate PM → PM → Senior PM → Director → VP → CPO. And as you progress, your work fundamentally shifts. Early in your career, you might spend 70% of your time on tactical work, like writing requirements, managing stakeholders, coordinating releases. But as you advance, that flips. Senior product leaders spend 70% of their time on strategy, vision, market positioning, and organizational alignment. Companies that don't create proper career paths for product managers struggle to attract and retain top talent. Why would an experienced PM take a "Product Owner" role when they're ready to drive strategy at the director level? If you want to build a strong product organization, start by recognizing product management as the strategic function it is, not just a project coordination role. On podcast episode 252 of the Product Thinking with Melissa Perri, I explore the difference between Project and Product management, which have also created many misconceptions in the industry. Attached you can see the visual from my book “Escaping the Build Trap” from 2018. How well does this model still hold up in 2026?

When I read about Nolan Norman learning how to solder circuit boards at 18, I didn’t just see a student, I saw a spark. A spark that starts when education meets opportunity. When hands-on training meets industry need. When someone finally connects what’s possible to what’s practical. Lorain County Community College is showing how this can be done, not through slogans or press releases, but through action. High school partnerships. Paid internships. Flexible pathways to real careers. Curriculum aligned to what employers actually need. It’s not flashy. It’s just smart. And it works. At the Global Electronics Association, we know this kind of model is key to strengthening the advanced manufacturing workforce. It’s how we build trusted supply chains, starting with trusted people. https://bit.ly/4mCxqmi

Marketing careers aren’t dying because of AI. They’re splitting because of it. I was having a conversation yesterday about what happens to the generation of marketers who built their careers through digital, transformation and the convergence of marketing and technology. I think we are entering a period where that broad digital marketing profile will increasingly split into two very distinct career paths. For the last decade or so, digital marketing rewarded people who could operate across disciplines. Digital was still evolving, so companies needed people who could sit in the middle and make sense of it. That middle is becoming harder to define. 1. Marketing Systems Leaders: One path is moving deeper into data, technology and product. These marketers will increasingly need to understand marketing technology, customer data, AI, automation, product experience and the systems that sit behind acquisition, engagement and retention. Product marketing increasingly sits naturally in this space. 2. Marketing Craft Leaders: The other path is moving deeper into marketing itself:brand, positioning, consumer understanding, communications, creative strategy and the ability to build distinctiveness in a market where execution is becoming increasingly commoditised. I'm seeing that companies are becoming much more deliberate about what's the role of marketing and the marketing hires. AI is accelerating this shift. There will still be exceptional people who can bridge both worlds. In fact, I suspect they will be disproportionately valuable. But the generic middle is going to become much harder to defend. Where do you think the next generation of digital and transformation marketers will build their depth?

We’ve seen the headlines: AI is optimizing routes, cutting fuel, and predicting maintenance issues before they happen. But despite the pilots, proof points, and potential - the needle hasn’t moved fast enough. Why? Because tools are only part of the story. The real barriers are human and structural. 🔍 The complication? => Data Silos – Mountains of data, but barely connected. Without trust and interoperability, AI is starved at the source. => Split Incentives – One actor pays, another benefits. Misalignment kills momentum. => Pilot Paralysis – Many try. Few scale. Not because AI doesn’t work—but because the system around it doesn’t. => Human Friction – Tradition trumps transformation. Risk aversion slows change. => Overhyped Algorithms – It’s not the code that matters most. It’s how insights trigger real-world decisions. 🧭 The path forward? => Start Small, Scale Smart, Aim big – use what you have. Show value. Build from there. Align Incentives – Make sure everyone gains, or no one will invest. Treat Data as Infrastructure – Build it as you go, not as a prerequisite. Embed in Operations – Intelligence must drive decisions, not sit in dashboards. Lead with Intent – Change doesn’t come from tools. It comes from leadership. 💡 AI’s promise is real. But value is trapped without collaboration, alignment, and bold governance. The transformation we want won’t come from smarter tech alone. It’ll come from a smarter way of working—together. Let’s stop asking “Can AI deliver?” Let’s ask: “Are we ready to unlock it?” Katherine Cote de Kalscheuer Thomas Foege Laurids Møhl Schack Camille Egloff Daniel Cáceres Koppelhus Lasse Busch Bo Cerup-Simonsen Suresh Subudhi Ulrik Sanders Hubertus Meinecke Sylvain Duranton Mærsk Mc-Kinney Møller Center for Zero Carbon Shipping Global Maritime Forum A.P. Moller - Maersk International Maritime Organization NYK Line MSC Mediterranean Shipping Company Global Centre for Maritime Decarbonisation (GCMD) Ingrid Irigoyen Cornelius Pieper Dr. Johannes Distler Meto Trajkovski Annika Zawadzki #AIinShipping #MaritimeInnovation #DigitalTransformation #FutureOfShipping #DataDriven #SustainableShipping #MaritimeDecarbonization #Leadership #CollectiveIntelligence #SystemChange #TechWithImpact