Excited to share that my book, Decoding Tech Careers, is officially launched! I wrote it because the tech job market has fundamentally changed. For the better part of the last 20 years, tech was growing. With it comes scaling headcount to meet demand and growth expectations. Then the bubble popped. Today, layoffs dominate the headlines for three main reasons: 1. Overhiring: A 20-year build-up that accelerated during the COVID spike. 2. Interest Rates: The jump from 0% to over 5% changed how companies spend. 3. AI: Automation is now mainstream, and it’s changing what companies actually need from employees. The job market is more competitive than ever. But most professionals are still caught in a cycle of tactical execution—spending their days on manual, repeatable processes. In a world of AI, that is a career dead end. The shift everyone needs to make to win in the market is to think like a "system architect". It's a way of operating. It means taking ownership of human judgment. AI can execute a command, but it can’t decide what to build or why it matters to the business. As a system architect, you are the one who identifies the problem, decides on the solution, and builds the process from start to finish. This is an inflection point for everyone to develop new skills as it will determine your future in the industry. I started as an entry-level analyst and worked my way up to leading global eCommerce teams at a Fortune 50 company. Over that time, I've learned what separates those to move their careers forward vs those who get stuck. Drawing from my experience and from those across the network I've built over 14 years, I've distilled those learnings into this blueprint that I'm now sharing with everyone. In the book, I cover the following topics: 1. The State of the Job Market 2. How I Started in Tech 3. How AI is Reshaping Tech Careers 4. Your New Tech Career Roadmap 5. How to Build a Powerful Online Brand 6. Turning Your Network into Opportunities What you'll come to find out is that despite the short term volatility AI is creating in the job market, there are still plenty of opportunities if you know where to look and how the skill demand is shifting. The important part is that you stay ahead of the curve. Understand what AI can do and how you can use it to your advantage. If you want the roadmap that will move your career forward in the new AI world, this book is for you. Check it out here: amzn.to/42y2dZb

Up until just recently, the only magazines I’d bought were the ones you grab at the airport to keep occupied or peruse through at a Barnes & Noble (which I hear is making a come back!). And almost everything I read is either on a screen or in a full book. A magazine subscription was a format I never really committed to. That is until now. A couple weeks ago, I subscribed to @arenamag, and the software issue just arrived and I'm exciting to dig in! Arena sits at the intersection I find most interesting: technology and enterprise, written at length, on high-quality print. At a time where technology has never been more interesting than it is right now, this magazine hits the spot. Web content and social media is efficient, and books go deep on a topic. Paper in this format makes you slow down on a subject without a big time investment. If you work in tech and miss longform that isn’t optimized for social media clicks, it’s worth a look: arenamag.com

Wrote more on this in Decoding Tech Careers: amzn.to/42y2dZb

AWS just added 2 million more Nvidia GPUs for 2027–28 after the last plan still wasn’t enough. Demand is running ahead of every forecast. That hits your week as slower tools and projects waiting on someone else’s queue. Whatever size shop you’re in: 1. Protect one AI workflow that moves money or time this quarter. 2. Know who owns the wait before you promise a date. 3. Keep a backup path for the work that cannot slip. The winners decide what gets capacity first.

Jensen told the G20 that AI is infrastructure, same as water and roads. His cake is five layers: energy, chips, data centers, models, apps. Your company pays for a factory that can keep the model fed. Own it and you fund the building. Rent it and you wait in someone else’s line. @Dell’s AI Factory work with @nvidia is that middle layer: servers and storage under the chips, so the model is not sitting around waiting on its own data. Would you rather your company build that, or rent it? #iwork4dell

There are two very different paths people take when choosing AI training, and picking the wrong one wastes months. Adopters are business analysts, marketers, and managers who need to show they can use AI tools to solve business problems faster. Builders are engineers and data scientists who need to prove they can actually write the code and build the systems. The mistake happens when people cross tracks without the background to support it. A business analyst spending three months on a heavy ML engineering certificate, memorizing formulas, then getting rejected from engineering roles because there's no core software background behind it. Or an experienced engineer wasting time on a basic prompt engineering course that teaches them nothing new. The fix isn't complicated. Figure out whether you want to build AI systems or use them, then choose a credential that actually matches that goal and your current experience level.

Before you pay for any AI certification, check one thing first: does it require a real project? No portfolio requirement, no hands-on sandbox work, no code you can point to? Skip it. It won't move the needle with hiring managers. The certs that actually pay off make you build something, not just watch something. What's the toughest project-based cert you've completed? Reply and let's compare notes.

AI certification listings have grown over 20x since late 2022. That's not a typo. Professionals are jumping onboard with AI at a fast clip and online credentials are easy to come by. But volume and value are two different things. Most of these credentials teach passive knowledge: watch a few videos, answer obvious multiple-choice questions, collect a badge. None of that proves you can solve a real problem at work. Hiring managers have caught on. A generic "AI fundamentals" certificate on a resume now reads as two hours spent clicking "next," not as evidence of applied skill. The credentials that actually carry weight require a project portfolio, hands-on application, and backing from a recognized institution, whether that's a major cloud platform or a top university. If you're deciding where to spend your training budget this year, the filter is simple: does the program make you build something you can show, or just make you feel like you learned something? Recent research on the AI certification boom backs this up, and it's worth a read before you enroll in anything: reveliolabs.com/news/social/… Curious what others have seen. Has a certification actually changed a hiring decision for you, or against you?

Despite some of the short term impacts, long term story of AI in the workforce will show a different story. Organizations will, over time, restructure around the capabilities that AI offers to them. In practice, this follows a classic economic pattern: wherever AI eliminates an old operational bottleneck, human resources and capital will naturally shift to tackle the next bottleneck—the ones AI can't easily solve. This doesn't happen in one fell swoop, nor does it happen equally across all organizations or industries. Net effect is skills and job reallocation, not a permanent job vacuum. It pays to pay attention to where those skills are moving to in order to stay ahead in your career. wsj.com/tech/ai/ai-workers-t…

Job boards help you explore roles, but applications sent through them usually get ignored. Even with the recent hiring upswing, you need a warm referral to stand out. Here's a three-step referral system I cover in my book, Decoding Tech Careers: Step one: Build your Target List. Pick five to ten companies where your role exists. Map your connections across three layers: Immediate Network: Former colleagues, alumni, friends. Reach out directly with the Job ID. 2nd Degree Network: Mutual connections. Ask for a warm introduction. Outside Network: Cold contacts. Find three to five people already in your target role at each company. Always start with your immediate network, then work down. Step two: Make the Small Ask. Reach out on LinkedIn. Skip the resume for now. Ask a low-friction question. Example: Hi, I see you're a Product Manager at this company. I'm planning a transition into this role. What certifications or skills do you find most important? People love sharing their path. This gets them talking. Step three: Value Exchange. Keep the relationship active. Share relevant news articles with a short note on why it matters to their team. Once you've built trust, ask about their hiring process. They'll often offer to submit your resume internally. It's a win for them too—most tech companies pay referral bonuses. This bypasses automated parsers and gets your resume in front of the hiring manager.

Last month, tech employment jumped by 69,000 workers. Employers listed nearly 587,000 active job postings. This is happening while everyone's still worried AI will wipe out tech careers. The reason? Jevons' Paradox. Old economic concepts from the mid 1800s - not new. But it has been a good explanation for what happens with technological change. Here's how it works: when technology makes something faster and cheaper to produce, you'd expect demand to stay flat and companies to just cut costs. But that's not what happens. At least not in the long term. Instead, the lower cost drives up demand. Way up. Companies and customers consume more of that cheaper thing, which means you need more people to meet the new demand. Look at ATMs. In the 1970s, banks introduced them and everyone assumed bank tellers were done. The opposite happened. ATMs made running a branch cheaper, so banks opened thousands of new locations. They hired more staff to take on more sophisticated roles. The role shifted from counting cash to selling financial products and managing customer relationships. There was demand in the market they could now go after. We're watching the same pattern with AI. AI handles the basics—drafting emails, writing simple code, analyzing data. That drops the cost of doing work. When work gets cheaper, demand for it explodes. Companies launch more projects, build more software, and need more people to manage the expanded output. Yes, the work is changing. Some roles are losing value, and there's real short-term pain as companies adjust headcount. That's not nothing. But long-term? The opportunity is expanding. If you're job hunting, now's the time to dig into LinkedIn or Glassdoor and see what's actually opening up.

Every tech job fits into one of four categories: Planners, Builders, Operators, or Drivers. Depending on what you've done and where you want to go next, if you want to switch careers, you may not have to start from scratch. You can shift into a new role within your category using what you already know. A sales engineer (Driver) can move into account management or customer success. You already understand the customer pain points and technical objections. Now you're managing the relationship long-term instead of closing the initial deal. A product manager (Planner) can transition into strategy consulting or business operations. You've been defining roadmaps and translating business needs into execution plans. That's exactly what strategy roles need. You can also shift to a new category where domain expertise carries over to new skills. A software developer (Builder) can pivot to a FinOps cloud cost analyst (Operator). Same technical knowledge of server architecture, just applied to corporate budget forecasts and automated cost tracking. Need to make a bigger jump? Get a targeted certification that matches your actual transition plan. If you're looking at a career switch in tech, how are you thinking about getting from point A to point B?

Stop chasing the latest LLM every month. There's always a new model. Always something shinier. One LLM is king one month, and another takes the crown the next month. The noise makes it feel like you're falling behind if you're not switching constantly. You're not. Pick one. Learn how it thinks. Build it into your actual workflows. Master the prompting patterns that get you consistent results. That's where the value is. Sure, experiment with new models when you have time. Make sure you're generally aware of the capabilities out there (and whether they're significant enough to make a switch). But depth with one tool beats surface-level familiarity with ten. What's the LLM you've stuck with long enough to actually understand? Gemini has been my pick - easy integration with rest of G Suite, already built into workflows. Great outputs for what I need. Use Grok as well as supplementary LLM. #ai #llm #gemini #artificialintelligence

Middle management is about to look very different (and already starting to!). AI will flatten traditional hierarchies, requiring a significant shift in how these roles function. Expect middle managers to become "builders" again. Think coders, process designers, or product managers – getting hands-on with execution, not just overseeing others. This return to core, value-creating skills positions professionals well for an AI-driven future. It’s a big shift from pure oversight. But it truly means creating real value. Honestly, a bit of an exciting time because building has never been easier. Time to dust off those builder skills, perhaps? What's your strategy for this evolution?