Shiva Mahajan

Evan Spiegel is pulling resources out of Snap's engineering org and moving them to distribution. More than two-thirds of Snap's new code is written by AI now. A service that used to need a team takes "half a person's time." So when the CEO looks at where the advantage went, he doesn't point at the product. He points at distribution, and he's moving the budget to match. Read that as a CMO. A public-company CEO just made the case for marketing better than most marketing teams make it for themselves. The part that should keep you up: distribution is the new moat, and it's also getting eaten. Rented reach isn't a moat. It's a bill that climbs with demand. The defensible asset is the distribution a platform can't tax away from you. This week in Behind the CMO: - Why AI retires the product advantage, not the marketing one - The one question that separates an owned moat from a rented cost - How to take this into the 2026 budget conversation Read it free, more in the comments section.

Earlier this year I made two changes at Google that have been both humbling and energizing: ➡️ Moving from Core Networking to Google Distributed Cloud (GDC) ➡️ Transitioning from manager (10 years) back to individual contributor In Core Networking, I had the privilege of working on the backbone of Google’s global infrastructure. Now at GDC, I’m learning every day from teammates and customers as we bring Gemini to the Edge — announced at Google Cloud Next. What excites me is not just the technology (though getting hands-on with Kubernetes, VM stacks, and real-world applications is a great learning experience), but the broader industry trend: • Workloads and data are moving out of centralized clouds • Customers want greater control, data protection, and cost efficiency • AI at the edge, private, and sovereign clouds is becoming a reality I’m grateful to Google and our leaders for enabling career mobility like this — it makes it possible to cross-pollinate ideas across teams, roles, and technologies. 🌍 The future of cloud is distributed. I’m curious: how do you see your industry approaching this shift? #GoogleCloud #GoogleDistributedCloud #Gemini #EdgeComputing #AI #Kubernetes #CareerGrowth

93

4 Comments

From Mid-Scale Hustle to Big-Scale Systems — My Leadership Journey When I worked at a mid-sized company — McAfee (formerly Intel Security), life as an engineering leader looked very different. I owned the entire tech stack end-to-end for the Real Protect SDK, which powered both enterprise and consumer security products. Decisions were quick. If I had an idea, we could experiment and roll it out within days. The visibility was immediate — the team saw the impact directly, and customers felt it instantly. But there were trade-offs: scale was limited, resources were stretched, and reliability sometimes gave way to speed. I wore multiple hats — tech lead, architect, and often the “on-call fireman.” Fast forward to today at Amazon. The canvas is massive — whether rolling out Prime membership features or building a multi-tenant portal to enable supply chain services for 1P & 3P sellers. Systems serve millions of users globally, reliability is non-negotiable, and playbooks ensure consistency across teams. I learn from some of the brightest minds, and every design decision forces me to think about scale, latency, and resilience. But here too, there are trade-offs: processes are heavier, decisions take longer, and ownership is often of just one slice of a much bigger puzzle. ⚡ What helped me move faster in this environment: Clarity of priorities → tie every discussion back to business + customer outcomes Cross-team relationships → build trust before you need alignment Document → Decide → Drive → cut endless meetings with written context & trade-offs Bias for small wins → ship incremental value instead of waiting for “big bang” launches Empowering the team → unblock quickly and let them own execution 💡 My takeaway: Mid-scale sharpened my ownership, versatility, and bias for action. Big-scale honed my systems thinking, discipline, and focus on reliability. The best leaders blend both — carrying startup agility into enterprise scale. 👉 If you’ve made the shift between mid and big companies, what was your biggest “surprise learning”? #EngineeringLeadership #TechLeadership #CareerGrowth #EngineeringManagement #ScalingTech #BigTech #StartUpCulture #LeadershipJourney

23

1 Comment

Indeed. A small number of big tech companies have grossly overinvested in LLMs and overextended themselves -- even laying off tens of thousands of critically needed human employees and enshittifying their primary products and services to reallocate investment into it -- shooting Nvidia's profits and shares to the moon -- all in an insane attempt to "be first" for something that has no forseeable compelling ROI. It's a collective delusion and hype bubble. This is markedly different from the strategic "operate in the red to build marketshare" strategy of the few survivors of the dot-com bust, because at least those survivors were able to articulate (and realize) a logical ROI after reaching a tipping point of critical mass. This is more like the 98% of dot-com startups that burned bright fueled by VC right until they burned themselves out because they never actually had any believable plan for monetizing anything they were building. The lack of ROI will come rudely crashing down on these companies over the next several quarters as they run out of tricks to cook the books and hide the losses. Shareholders will get spooked and back out, share prices will crash, and big tech companies that have bet the farm on LLMs will suddenly find themselves without the necessary employees, capital, quality products/services, or reputational sway needed to save themselves. And when they collapse, Nvidia will collapse with them. And then maybe, finally, we can get back to the business of making things of actual value to humans.

19