Future Of Work

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! [Original: The Batch]

Brutal career truth in the age of AI: knowledge alone is no longer enough. Pure specialists are exposed because AI can learn narrow domains faster than many people can defend them. Shallow generalists are exposed too because LLMs can already summarize, compare, and explain almost anything in seconds. So what is left when AI eats specialists and outperforms generalists? Not just more information. Better judgment. What stands out to me is this: The real career advantage is becoming a translator. Someone who can zoom in like a specialist, zoom out like a generalist, and connect technology to people, business, and outcomes. Because in the AI era, you will not win by simply knowing more. You will win by seeing better, deciding better, and being more human. What do you think will matter most for careers now: depth, breadth, or judgment? #AI #ArtificialIntelligence #FutureOfWork #Careers #Upskilling #Leadership #HumanSkills #Workplace #DigitalTransformation

🪂 How To Make Your Design System AI-Ready (https://lnkd.in/dtnpy7CM), a practical guide on how to reduce drifts, minimize mistakes, maintain context and improve the quality of AI-generated prototypes — with structured spec files, automated auditing and token layers. Put together by Hardik Pandya from Atlassian. --- 🔹 1. Design Decisions Are Infrastructure AI-generated prototypes often don't deliver consistently decent results because of tiny inconsistencies scattered all across a design system. Often it's decisions made but not documented, hard-coded values never cleaned up, or relying too much on AI making sense of mock-ups or design flows on its own. Unsurprisingly, better AI prototypes come from better data — but also from better human guidance. We shouldn’t assume that AI knows how to choose the right component, and how to design with accessibility in mind. It needs priorities, a clear path on how we make decisions, design principles, examples, do's and don'ts. In fact, we should treat design decisions as infrastructure. That means that every time we make a decision — not just a design decision, but even decision on how actually prioritize our work and how we make decisions around here — it must find a path into the spec file that is then consumed by AI. --- 🔶 2. Three Layers: Spec Files + Token Layer + Audit To ensure quality, we establish design principles, guidelines, rules in a form of “spec files”). It's structured Markdown files that include spacing rules, color choices, component usage guidelines, priorities etc. AI is going to read and reuse that spec file every time it's going to generate a prototype. Because the spec files are text files, it's much more cost-effective, but also much more accurate just because we don't rely on AI recognizing or decoding patterns from mock-ups, but gets specific guidelines instead. In fact, extending code is often a more effective way than generating code from mock-ups. Token layer lists and keeps updated all tokens used throughout the design system. AI always chooses from a closed set of named variables instead of inventing plausible values ad-hoc. An audit script catches what AI gets wrong. It scans the prototype and flags every hard-coded value and flags it if necessary. It can be a regular software doing that, with AI waiting for its feedback to come back. Finally, when a design system ships updates, a sync routine flags which spec files need updating. The goal is to make sure that AI always reads up-to-date, current specs, not the ones written against an outdated version. --- 🔺 3. Examples of AI-Ready Design Systems ⌾ Atlassian: https://lnkd.in/dVsGc3Cp ⌾ Carbon: https://lnkd.in/d4zq4WWb ⌾ CMS Design System: https://lnkd.in/dHHzV3en ⌾ Nordhealth: https://lnkd.in/d8C4j2ZA Yet again, AI can’t magically resolve technical debt or design debt — it needs guidance, decisions, priorities and principles.

India’s green economy is growing fast but LinkedIn data suggests green talent is growing even faster. The LinkedIn Hiring Rate (LHR) for green talent — defined as professionals with green skills, green job titles, or both — is now 59.7% higher than for the overall workforce. This means green-skilled professionals are significantly more likely to be hired than their peers, underscoring the growing demand for sustainability-focused roles. “The prioritisation of green talent by Indian companies is being fuelled by an interplay of policy reforms, rising consumer consciousness, and the need for deep business transformation,” says Neelima Burra, Chief Strategy, Transformation, and Marketing Officer at Luminous Power Technologies. “Government initiatives like the PM Suryaghar Yojna, National Solar Mission, and Smart City Mission, combined with the growing mandate for ESG reporting — are also pushing companies to recruit sustainability experts, carbon auditors, and ESG strategists to meet regulatory and investor expectations,” she adds further. Operational efficiency has emerged as the top skill across the top five industries increasingly hiring for green skills, as per LinkedIn data. In contrast, precision agriculture skills lead in farming, ranching, and forestry — highlighting how sector-specific green skills are evolving. “Operational efficiency offers the fastest route to tangible returns. It moves the conversation beyond regulatory compliance to net profitability, ensuring we can do more with less energy and fewer materials,” says Venu Nuguri Managing Director and CEO at Hitachi Energy. This surge in demand aligns with broader economic trends. Green jobs in India have grown over 10 times in the past five years, with Gen Z accounting for 63% of applicants, reports The Economic Times, citing a report by WeNaturalists. The projections are equally ambitious. India’s green economy will generate 7.29 million jobs by FY28 and 35 million by 2047, as the sector scales toward a $1 trillion valuation by 2030 and $15 trillion by 2070, suggests another report by The Economic Times, citing a report by NLB Services. The message is clear: green skills aren’t just good for the planet — they’re becoming essential for employability. As India accelerates its climate and economic goals, the workforce is already adapting. The question now is whether education, training, and policy can keep pace. Read the full report here: https://lnkd.in/g873CzHT #COP30 #GreenerTogether Source: The Economic Times: https://lnkd.in/d-3bShQP  The Economic Times: https://lnkd.in/dSUMFS58 

It’s an oxymoron. But the AI bank debate ended before it even began. Banking is moving from customer- to agent-centric. And agentic banking is the real shift.   Three structural shifts are taking place:   • 𝗙𝗿𝗼𝗻𝘁-𝗲𝗻𝗱: Banks have always built tools to own the customer relationship - apps, portals, distribution channels. But agents will increasingly sit between customers and banks, making recommendations, negotiating, and even executing on their behalf. The entire customer journey is turned inside out, and banks need strategies to be discoverable, interoperable, and trusted by agents.   • 𝗕𝗮𝗰𝗸-𝗲𝗻𝗱: Core banking was once the heart of the bank - where accounts, ledgers, and transactions lived. Now agents are creating a new brain layer on top, orchestrating credit, risk, compliance, and operations in real time. Static legacy rules are replaced by adaptive reasoning powered by knowledge graphs, expertise, and reinforcement learning. The bank’s intelligence is migrating out of the core and into an agentic layer.   • 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Banks have long relied on hierarchies of people, processes, and departments. With agents acting as co-pilots and co-pilots evolving into autopilots, every role in the bank will be augmented or reshaped. This is an entirely different operating model where agents collaborate with humans, redefining how work is organized, supervised, and delivered.   These aren’t theoretical shifts - they’re already being put into practice. Huawei, a major provider of financial infrastructure, has released the 𝗙𝗶𝗻𝗔𝗴𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗲𝗿: a Financial Intelligent Agent Accelerator built to help banks move from pilots to full-scale commercialization.   FinAgent Booster is part of Huawei’s Digital Finance positioning – the application scenarios below are eye-opening:   • 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗷𝗼𝘂𝗿𝗻𝗲𝘆𝘀: Accuracy, memory, and latency have long limited digital banking. Huawei employs a master-slave agent framework to interpret intent and coordinate tasks, a memory system that learns from context and past interactions, and full-chain optimization to ensure instant responses. The outcome is akin to a dedicated banker for every client.   • 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴: Agents are becoming the bank’s new decision layer. Thousands of reasoning chains and past insights are encoded into models that continuously learn and adapt.   • 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Agents are being embedded to augment every role across the organization. Intelligent contact centres route customer needs to the right resources, insight hubs act as decision-making headquarters, and role-based agents support clients, loan officers, and back-office staff.   For banks, the agentic clock is already ticking.   Opinions: my own, Graphic source: Huawei, Panagiotis Kriaris   Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

📉 Entry-level job postings have taken a knock. A significant one. That’s one of the starkest signals in the latest global research on Gen Z - and it has far-reaching implications for how we support early career talent. As this chart shows, job postings requiring 0–2 years of experience have declined by 29 percentage points since January 2024. In contrast, postings for senior roles have broadly stabilized. This creates a fundamental imbalance. At the exact moment that Gen Z is stepping into the workforce, the usual career ladder appears to be missing an early rung. This is broader than a singular talent issue. It’s an economic one. We’re dealing with persistent talent scarcity across sectors - in healthcare, logistics, IT, engineering, and more. If we fail to activate early-career workers and give them clear entry points, we risk weakening the pipeline we rely on to build our future workforce. The report draws on insights from more than 11,000 young workers and over 120 million global job postings, and the findings show a generation that’s not disengaged, but ambitious. 85% of Gen Z talent say they weigh long-term goals when considering a new job. They’re not job-hopping; they’re growth-hunting. And yet, many haven’t made the connection between upskilling and the growth they’re looking for. While they’re the most AI-empowered generation in today’s workplace - with 46% using AI to learn new skills - access to formal training still lags. This creates a new kind of digital divide. For employers, this presents challenges, but more significantly, opportunity. We need to rebuild the early-career pathway. Not by going backwards, but by reimagining the start of the working journey. That includes: ✏️ Designing entry-level roles with clear progression 🎒 Helping talent connect learning to advancement 🤖 Ensuring equal access to AI tools and training 💙 Meeting Gen Z’s call for purpose, flexibility, and equity We can’t fix talent scarcity without focusing on those just entering the workforce. And we can’t talk about the future of work without building it - from the first step of its ladder. 📘 Explore the full Gen Z report here: https://lnkd.in/ecd2HjXU

If you're feeling overwhelmed with how fast AI is evolving, you're not alone. Every day there’s a new paper, a new framework, a new agent loop, and it’s easy to feel like you’re falling behind. But the good news is that you don’t need to learn everything all at once. What you need is structure. So I put together a 10-level AI Agents Learning Roadmap that takes you from foundations to production, layering your learning in a way that’s actually doable. 💡My recommendation: spend 2–3 weeks on each level. Learn the concepts, implement small projects, and build your intuition. If you're moving faster or slower based on time or experience, that’s okay too. And when something new drops? That can be your Level 11. Don’t let “newness” derail your plan. Just start here. 👇 Here’s the roadmap: 🔖 Level 1: GenAI & Transformer Foundations Tokens, embeddings, transformers, decoding, and inference with open-weight models. 🔖 Level 2: Prompting & Language Model Behavior Prompt types (CoT, ReAct, ToT), decoding strategies, context design, and adversarial prompting. 🔖 Level 3: Retrieval-Augmented Generation (RAG) Chunking, embeddings, vector DBs, RAG pipelines, and RAG evaluation. 🔖 Level 4: LLMOps & Tools LangChain, LangGraph, Dust, CrewAI, tool use, function calling, and synthetic data. 🔖 Level 5: Agents & Agent Frameworks Agent types, memory, planning, LangChain agents, LangGraph loops, and evaluation. 🔖 Level 6: Memory, State & Orchestration Vector and symbolic memory, episodic vs persistent state, memory compression. 🔖 Level 7: Multi-Agent Systems Hub-and-spoke vs decentralized, message passing, collaborative agents, agent teams. 🔖 Level 8: Evaluation & Reinforcement Learning LLM-as-a-Judge, RLHF, RLVR, reward modeling, and self-correcting loops. 🔖 Level 9: Protocols & Safety MCP, A2A, safety alignment, guardrails, traceability, and autonomous policy updates. 🔖 Level 10: Build & Deploy FastAPI, Streamlit, GGUF, QLoRA, caching, monitoring with LangSmith, Arize, Trulens. 📌 Bookmark this. 🛠️ Build something after every level. And if you're wondering what tools to explore along the way → Start with Hugging Face (to explore LLMs and SLMs), you can use Ollama (to run SLMs on your laptop, like Phi-4, TinyLlama), or Fireworks AI (to run LLMs via endpoint, like Qwen 3, Kimi K2, DeepSeek R1), then explore LangChain & LangGraph (these two tools will teach you a lot), then you can move into learning Agentic frameworks like CrewAI, AutoGen. 💻 Pro-tip: Start with cookbooks! 〰️〰️〰️ Follow me (Aishwarya Srinivasan for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

DevOps in 2025: Winning Skills and Real Trends Two years ago, DevOps was a high-demand field. In 2025, it’s the backbone of every digital transformation—supercharged by cloud, automation, and now, AI. Here's what caught my attention 👇 📈 DevOps market is projected to expand from $13.2 billion in 2024 to an impressive $81.1 billion by 2028 📈 From specialized approach to mainstream strategy: Its adoption soared from 33% of companies in 2017 to an estimated 80% in 2024. Let me break down what's really happening out there and how you can ride this wave—whether you're just starting or gunning for that architect role. 📊 𝗪𝗵𝗶𝗰𝗵 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲 𝗝𝗼𝗯 𝗣𝗼𝘀𝘁𝗶𝗻𝗴𝘀? Based on aggregated data from 2024-2025 DevOps job postings, here’s the tech that consistently tops job requirements: 1 - Terraform 88% (+9%) 2 - Python 80% (+8%) 3 - Kubernetes 76% (+6%) 4 - AWS 72% (–3%) 5 - Jenkins 74% (+6%) 6 - Docker 68% (+3%) 7 - Azure 60% (+6%) 8 - Git/GitHub 60% (+2%) .... 19 - Golang 18% (+13%) The pattern is clear: Infrastructure as Code is king, container orchestration is everywhere, and you better know your way around multiple clouds. Golang is the surprise breakout. 🌐 𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲'𝘀 𝗛𝘂𝗻𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝗗𝗲𝘃𝗢𝗽𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀 ↳ Cloud-native expertise is “non-negotiable”: 83% of organizations now use multi-cloud approaches. If you can juggle AWS, Azure, AND Kubernetes? You're golden. ↳ Architects and senior engineers who bridge DevOps, cloud, and AI lead the next evolution. These are the people building scalable, secure, AI-ready infrastructure—roles that are multiplying fast. ↳ Platform engineering is having a moment: Everyone wants internal platforms that make their developers' lives easier. 🤖 𝗔𝗜 𝗜𝘀𝗻'𝘁 𝗞𝗶𝗹𝗹𝗶𝗻𝗴 𝗗𝗲𝘃𝗢𝗽𝘀 (𝗜𝘁'𝘀 𝗠𝗮𝗸𝗶𝗻𝗴 𝗜𝘁 𝗕𝗲𝘁𝘁𝗲𝗿) ✅ AI/ML is making DevOps smarter—think smart incident response, predictive analytics, and self-healing infrastructure that fixes itself. ⚙️ But success still comes down to knowing your foundations: DevOps, cloud architecture, and scripting. 🚦 𝗖𝗮𝗿𝗲𝗲𝗿 𝗔𝗱𝘃𝗶𝗰𝗲: 𝗖𝗵𝗼𝗼𝘀𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵, 𝗧𝗵𝗲𝗻 𝗚𝗼 𝗗𝗲𝗲𝗽 - Get dangerous with 2 automation tools (Terraform + K8s is the combo right now) - Go deep with AWS or Azure, but stay curious about the others - Python is your Swiss Army knife—learn it, love it - Don't sleep on AI tools, but master your CI/CD and container game first 🎯 𝗪𝗮𝗻𝘁 𝘁𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗰𝗮𝗿𝗲𝗲𝗿 𝗿𝗼𝗮𝗱𝗺𝗮𝗽? We’ve compiled all the proven insights—plus actual salary data, skills breakdowns, and stepwise growth plans—into the latest DevOps Career Guide. 📌 Grab it here: https://bit.ly/44TevO0 💬 What are you seeing in your corner of the DevOps world? What skills are you stacking for 2025? Sources: - Forrester: DevOps and Platform Engineering, 2025 - DevOpsCube Report, 2025 - Prepare.sh: DevOps Job Market Trends 2025

Hard Work Doesn’t Cause Burnout.  This Does. People don’t burn out because they’re weak. They burn out because they’re at war—every single day. Not with the work. But with the culture. Most high performers can handle pressure. What drains them is the invisible combat of surviving a toxic environment: • Fighting for basic recognition. • Tiptoeing around ego-driven managers. • Navigating blurry expectations. • Absorbing blame just to keep the peace. • Working long hours—not for purpose, but for permission to belong. This isn’t hustle. This is emotional survival disguised as productivity. Burnout isn’t always from too much to do. It’s from not enough safety to be human. It’s the silence you bite back. The trust you can’t give. The energy you waste decoding office politics. And here’s the truth no one puts in the job ad: "Toxic cultures break people before the deadlines ever do." So what builds resilience? Not snacks in the break room. Not "We’re a family" posters. ✅ Clarity over chaos. ✅ Trust over fear. ✅ Leaders who listen—not just talk. When people feel safe, seen, and supported— They don’t just survive. They rise. They create. They lead. Let’s stop glamorizing burnout and start talking about the real cost of toxicity. What’s one silent culture killer you think companies need to call out—loudly? ♻️ Share this with your network if it resonates. ☝️ And follow Stuart Andrews for more insights like this.

I’ll be honest. 
When I first started stepping away from the day-to-day… I used to feel a strange satisfaction when things broke in my absence. 
It made me feel important. 
Like I was the glue holding it all together. But the truth is harsher: 
Every time something breaks when you’re not there, it’s a sign you’ve failed to build a system that works without you. That’s not leadership. 
That’s being a bottleneck. 
A liability. Because when progress depends on your availability, your time, your personal input - the whole business becomes fragile. 
You become the single point of failure. Let me be clear: 
If your team needs you to approve every small move, you’re not scaling excellence - you’re scaling dependence. 
That’s ego. Not leadership. Real leadership is when: - The thinking happens without you. - The decisions happen without you. - The momentum continues without you. Not because you’re not needed. 
But because you’ve built a system that doesn’t collapse when you’re not in the room. If you step away and things grind to a halt, you haven’t built a high-performing team. You’ve built a fragile operation propped up by your control. And that’s on you. Every time something breaks in your absence, it’s feedback: - A system isn’t clear. - Accountability isn’t owned. - Trust isn’t built. It’s a signal to fix the machine, not to double down on micromanaging. Because here’s the harsh reality:
 A business that can’t run without you is a business that can’t grow beyond you. Let that sting. 💡George Stern