Leadership

The bar for building has collapsed. Anyone with an AI assistant can produce a prototype, a draft, a working script. That is not the challenge anymore. The challenge is identifying people who can tell whether what was built is right, catch what is wrong, and make the call on what to do about it. That is a different hire than the one most managers have been trained to make. And the stakes are higher now, because your hiring decision is no longer a baseline. It is a multiplier. ...

Your team closed 47 tickets last sprint. Deployment frequency is up. Lead time is down. The sprint review looked clean. And you have no idea whether your team is getting better at what matters. Every metric on that dashboard measures motion. None of them measures whether the team made the right decision. In an era where AI generates the first draft of almost everything, code, architecture proposals, incident summaries, and product specs, that gap is no longer academic. It is the difference between a team that compounds in judgment and a team that accelerates toward the wrong destination. ...

For decades, the path was obvious. A junior engineer joined a team, got handed a bug nobody else wanted, fixed it, broke something else, fixed that too, and over a few years accumulated the scar tissue that turned into judgment. Senior engineers were not trained; they were grown, slowly, by the system itself. That system has stopped working at both ends. At the entry point, juniors aren’t being hired. AI makes a senior dramatically more productive and a junior only marginally so, which makes the rational move, for any single team, in any single quarter, to skip the junior hire and let an experienced engineer with AI do the work two juniors used to do. Industry-wide, the pipeline is being shut off before it starts. ...

Working with AI to generate code is extremely satisfying. In a matter of minutes, you get something that looks great and, in most cases, does what you wanted it to do and even more. But many times, what looks ready for production is far from being production-safe. A large-scale study conducted by two researchers at FernUniversität in Hagen analyzed 7,703 files from public GitHub repositories explicitly attributed to AI tools. Using CodeQL, the researchers identified 4,241 CWE instances across 77 different vulnerability types. While 87.9% of the analyzed AI-generated code contained no identifiable CWE-mapped vulnerabilities, the risk came from code that appeared to work fine. It compiled, it solved the visible task, but it still carried hidden assumptions, unsafe patterns, and security debt. ...

I recently argued on the JFrog blog that trusted AI requires more than model quality. It requires visibility, provenance, governance, and a real system of control around the things models consume, build, and ship. That is the foundation. This post is about what you build on top of it. Because visibility is necessary. Without it, you cannot govern anything. If you cannot see which models are running, where they came from, how they behave, and what they touch, you do not have a governance posture. You have hope dressed up as architecture. ...

I heard Satya Nadella describe this week on the All-In podcast how LinkedIn has been collapsing product management, UX, and engineering roles into a single “full-stack builder” role. The framing was optimistic: AI reduces construction costs and speeds up delivery. At a surface level, this makes sense. AI dramatically accelerates prototyping, implementation, and iteration. In many environments, the traditional separation between PM, design, and engineering really does introduce friction that slows learning and execution. ...

The Full Stack PM I have always believed in the idea of the full stack product manager. Not because one person should do everyone’s job, but because product work does not respect org charts. Products fail and succeed in the gaps between roles, and when those gaps exist, someone has to step in and own them. Many organizations like clear distinctions: technical PMs, product owners, delivery PMs, growth PMs. On paper, it looks clean. In practice, it often creates waste. Handoffs. Waiting. Endless alignment meetings. Everyone does their part, but nobody owns the outcome. ...

When Private Equity Optimizes the Wrong Thing Over the past years, a troubling pattern has become increasingly visible in Israeli tech. Companies founded and grown in Israel, often on the back of exceptional ingenuity, speed, and informal problem solving, are acquired by private equity firms and then gradually or abruptly stripped of their Israeli core. Functions are relocated, investment is reduced, leadership is replaced, and the original teams are slowly starved or shut down. ...

“Why so serious?” The line is memorable because it cuts through pretense. The Joker was not asking for chaos. He was exposing hypocrisy. People pretending this is all very important, very controlled, very rational. When in reality, it is not. Lately, scrolling LinkedIn feels like watching that scene on repeat. Stories proudly shared. Lessons supposedly learned. And beneath them, a disturbing seriousness about work and people that has lost all proportion. ...

The Answer Is Still Cookies A few days ago, I saw a Cookie Monster quote that made me stop: “Me don’t know da question, but me know da answer is cookies.” It is funny. It is childish. And it is exactly how companies behave when they insist their problems are special. Every company believes it is fundamentally different. Different industry. Different technology. Different customers. Different speed. Therefore, their problems must be new, unprecedented, uniquely complex. ...