Skills for Navigating Tech Industry Trends

If you want to land a $100k+ remote job offer as a software engineer in 2025, I would 100% suggest you invest your time in these technologies (based on my experience from the last 15+ years). 1/AI agents are the hottest thing right now - half of SF is building agent startups, why? Because they’re the closest thing to AI automation before AGI. - think of them as LLMs that make decisions, automate workflows, and interact with real-world apps (Gmail, WhatsApp, databases). - startups are racing to build voice agents, chatbot-based automation, and AI-driven assistants and they need engineers who know how to integrate LLMs with real-world APIs. - learn LangChain, OpenAI API, and automation frameworks to get into this space. 2/ Browser automation is the secret weapon for AI companies - Many AI companies need their models to control and interact with websites, booking flights, scraping data, handling forms. - Startups like Induced AI and Browserless are being built purely to automate browser interactions. - If you learn Selenium, Playwright, and Puppeteer, you can land jobs in AI companies that need large-scale browser automation for their systems. 3/ Vs code extensions and developer tools are printing money - AI-powered developer tools are booming, Cursor, Cody, and Devika are at billion-dollar valuations. - Understanding how VS Code works under the hood, how to build extensions, and how to index and parse large codebases efficiently is a valuable skill. - Want to future-proof your skills? Learn how to build AI-powered coding assistants or improve existing developer workflows. 4/DevOps and cybersecurity will never go out of demand - Every company hitting scale needs DevOps engineers to optimize cloud costs, monitor infrastructure, and automate CI/CD. - Good DevOps engineers are rare, and companies pay massive salaries for experts who can save them millions on AWS bills. - Cybersecurity is another evergreen skill. Even AI-written code will have security vulnerabilities. If you understand penetration testing, bug bounties, and infrastructure security, you will always be in demand. 5/ AI image and video generation will only grow -Companies like Runway, Ideogram, and Midjourney are disrupting design, media, and content generation. - Learning diffusion models, LLM-based video generation, and AI-powered media tools will put you in one of the fastest-growing industries. - This is a difficult field to break into, but if you can build AI-powered media tools, you’ll be ahead of 99% of developers. Pick a field, go deep, and build real things. AI is making engineers 10x more productive, which means companies are hiring fewer, but better engineers. Don’t just learn—show proof of work.

Right now, everyone is rushing to learn AI—deep learning, LLMs, and complex machine learning techniques. But most companies aren’t struggling with AI... They’re struggling with basic data management, analytics, and decision-making. Yet, many job seekers believe they need to master deep learning to land a data science role when the reality is much different. Before focusing on AI, it’s essential to develop strong data fundamentals: + SQL and Data Manipulation – Extracting, cleaning, and structuring data efficiently is critical. SQL remains one of the most in-demand skills in data science. + Business-Focused Data Analysis – Companies prioritize professionals who can use data to drive decisions, optimize processes, and create measurable impact. + Data Visualization and Communication – Insights have no value if they can’t be communicated effectively. Data storytelling is an underrated skill that influences decision-making. + Problem-Solving with Simple Models – Many business problems can be solved using logistic regression, decision trees, and forecasting methods rather than complex AI models. Many businesses lack structured data, clean pipelines, and the ability to make sense of the information they already have. Before implementing AI, they need: - Better customer segmentation rather than an AI-powered chatbot - Stronger demand forecasting instead of deep learning solutions - Clearer sales and operations insights before investing in predictive modeling - Organizations are looking for data-driven decision-making. The ability to translate raw data into business impact is far more valuable than knowing how to fine-tune a large language model. Most entry-level roles don’t require deep learning. The focus is on: // Understanding and working with real-world messy data // Solving business problems through analytical thinking // Presenting insights in a way that leads to action AI is only as good as the data that powers it. Strong data fundamentals will always be more valuable than chasing the latest AI trends. Those who focus on building these skills will position themselves for long-term success.

The World Economic Forum just revealed the top skills for 2030 in their Future of Jobs Report. Spoiler: the ones rising fastest are the ones we’ve been ignoring 👀 Here’s what the data from 1,000 top employers (representing 14M workers across 22 industries) tells us: 📍 Analytical thinking is the #1 skill employers value most. 📍 Career growth is shifting from credentials to capabilities. Employers care less about degrees and more about how you think, adapt, and solve real problems. 📍 Soft skills are rising faster than technical ones, including programming and design. If you want to stay relevant by 2030, these are the skills to master: 1) Analytical Thinking → Choose one business decision this week and list 3 things you're assuming to be true. Then challenge each with data before moving forward. 2) Creative Thinking → Take a current work challenge and ask, “How would a completely different team solve this?” 3) AI & Big Data Literacy → Pick one business workflow and explore how AI could reduce time, cost, or complexity. Then test it in a low-risk area. 4) Resilience & Flexibility → Write down the last thing that frustrated you. How could you respond differently next time? 5) Motivation & Self-Awareness → Track your energy for 3 days. Note when you're most focused vs. drained. Move one key task to match your peak zone. 6) Curiosity & Lifelong Learning → Set a 30-minute calendar block to explore a trend that will affect your industry but isn’t on your roadmap yet. 7) Technological Literacy → Pick one tool your team uses and explore a feature you’ve never touched. 8) Empathy & Listening → In your next 1:1, ask: “What’s something you’ve been holding back from saying?” Then listen without interrupting. 9) Leadership & Influence → In your next team meeting or Slack update, highlight a quiet win from someone who usually flies under the radar. 10) Systems Thinking → Pick one process that causes repeated friction. Map it end-to-end, and eliminate the one step that slows everything down. WEF’s data is clear: The skills rising fastest aren’t technical, they’re human. AI is already mastering the hard skills. But it still can’t lead a team, rethink a broken process, or earn trust in a room. 🤝 By 2030, those who master soft skills with strategy won’t just survive the shift, they’ll lead it 

The Future of AI Belongs to the Prepared. If you want to stay relevant in 2025 and beyond, mastering foundational AI skills is no longer optional. That’s why I created this visual: “15 AI Skills to Master in 2025”—a roadmap for developers, data engineers, and tech leaders navigating the GenAI era. Here’s what the future demands: ⫸ Prompt Engineering – Still the secret sauce to great LLM output. ⫸ AI Workflow Automation – No-code and low-code tools will drive faster innovation. ⫸ AI Agents & Agent Frameworks – LangChain, CrewAI, AutoGen… Agentic AI is the new operating model. ⫸ RAG (Retrieval-Augmented Generation) – Combine LLMs with private data sources for real-time intelligence. ⫸ Multimodal AI – Text, code, images, audio… future models speak every language. ⫸ Custom LLMs & Fine-Tuning – Build assistants fine-tuned for your use case. ⫸ LLM Evaluation & Observability – If you can’t measure it, you can’t improve it. ⫸ AI Tool Stacking – Combine APIs and agents into powerful workflows. ⫸ SaaS AI App Development – AI-native products require scalable infra and modular thinking. ⫸ Model Context Protocols (MCP) – Handle memory, context, and token budgeting across agents. ⫸ Autonomous Planning & Reasoning – ReAct, ToT, and Plan-and-Execute are no longer just research. ⫸ API Integration with LLMs – Connect the real world to your AI agents. ⫸ Custom Embeddings & Vector Search – Semantic search is foundational to personalization. ⫸ AI Governance & Safety – As AI grows, so do the risks. Guardrails are critical. ⫸ Staying Ahead with AI Trends – Read, build, share, repeat. Constant learning is non-negotiable. Whether you’re building the next intelligent platform or leveling up your career, this roadmap outlines what matters most. Use it to audit your current skillset. :-)

If you're in tech, you're sitting on a goldmine right now. While everyone's debating AI job displacement, the engineering sector is quietly becoming the biggest AI beneficiary. The World Economic Forum projects 78 million net new jobs by 2030, and IT and Engineering is leading the charge. This shift is creating entirely new job categories that didn't exist two years ago. Here are five emerging growth areas for IT and Engineering: 1. AI-native product development → AI Product Managers who understand ML lifecycles and enterprise pain points. 2. AIOps infrastructure → MLOps engineers are moving companies from AI experiments to production. Every enterprise needs these skills. 3. AI cybersecurity → Red teamers for LLMs are literally paid to break AI systems.  4. Enterprise data infrastructure → Vector database engineers managing RAG pipelines are helping AI systems access the right information at the right time. 5. Vertical AI specializations → LegalTech AI specialists, FinTech AI analysts, HR tech AI specialists—domain expertise + AI fluency is the new superpower. The numbers back this up: $632 billion in AI spending (including applications, infrastructure, and IT services) by 2028. This will lead to new AI roles in engineering, product, data, and operations to maintain these AI systems. Bottom line: The engineers who adapt fastest will have the most opportunities. In my latest newsletter, I break down exactly how to transition into each of these roles, plus the specific tools and skills that matter most. What AI role are you most curious about? #AI #Engineering #IT #FutureOfWork