Everyone has opinions about candidates. That's the problem.

We're supposed to ask standard questions, evaluate people against the job description, and test whether they can do the work. Instead, we dig into areas where we think they're weak, ask different questions for each person, and end up testing our biases instead of their abilities.

Everyone's worried about AI replacing engineers. The more interesting question is what happens when the cost of building software drops so dramatically that entirely new categories of software become viable.

The industry is calling this "personalized software." Custom tools built for a specific person, a specific context, a specific moment. Software that never leaves your machine. Software that would never justify a product. Software that, until recently, simply wouldn't exist.

The two-minute deploy is killing my productivity.

That sounds wrong until you think about proportions. Two minutes is nothing. But when AI-assisted development shrinks the time spent writing code, those two-minute deploys start consuming a much larger percentage of your development cycle.

I discovered this while building with a managed backend framework that requires redeployment even during local sandbox development. The frontend rebuilds in seconds. The backend takes two minutes. Suddenly, that backend deploy time is where I spend most of my dev cycle waiting.

A caveat before going further: this observation comes from a greenfield project where I'm moving quickly and iterating frequently. AI-assisted development changes the structure of work in existing projects too, but this effect is most pronounced when building something new and small, where rapid iteration is the default.

Engineering teams have always made judgment calls about risk and speed. With AI development tools becoming standard practice, that judgment call has gained a new dimension demanding more careful consideration.

I've written before about the importance of keeping work discussions in public forums: Slack channels, JIRA tickets, shared docs, anywhere that's searchable and accessible. If it's about work, other people probably need to know about it. I've recommended that teams target 60-80% of their messages in public channels to preserve institutional knowledge and make information searchable for future team members.

With AI tools becoming ubiquitous, this practice has transformed from best practice to competitive necessity.

Most people treat AI tools like a digital hoarding situation: dozens of half-finished conversations cluttering their workspace, making it impossible to find anything useful. The solution isn't better chat organization—it's a fundamental shift in how you approach AI collaboration. I delete almost every AI chat I have, and it's made me dramatically more productive. The key is a simple two-category rule: either I'm asking a specific question (delete after getting the answer) or I'm building something using artifacts as staging areas for development (save the result, delete the chat). This isn't about digital minimalism—it's about transforming AI from a conversation partner into a development tool. When you stop having endless discussions and start building tangible outputs, your AI workspace becomes as clean and purposeful as a well-managed inbox, unlocking measurable performance gains in how you work.

Every engineering team is racing to implement AI tools, but most are optimizing the wrong variable. They're tweaking prompts and comparing models while ignoring the fundamental truth: your AI output quality is entirely dependent on your input quality. When you ask a default AI model a question, you're getting the average of the internet. Those 1,000 words generated from your 50-word prompt? They're coming from random web content, not your expertise. The companies winning with AI aren't using better models. They're feeding those models better inputs through curated knowledge bases, documented processes, and structured organizational wisdom. This isn't just theory. Research shows that RAG systems pulling from quality knowledge sources increase accuracy by nearly 40% compared to models operating on training data alone. For engineering leaders, this means the competitive advantage isn't the AI tool itself. It's the quality of information you feed it. Start building your knowledge systems now, because input quality isn't just a performance optimization. It's your strategic moat in an AI-commoditized world.