Artificial Intelligence

I recently shared a short post on LinkedIn: “If an agent can do the job with an existing CLI, I do not think we need to build another MCP integration.” Let me explain the thinking behind it. Start with the Tools You Already Have Before building an integration, I would ask what the agent actually needs to do. Maybe it needs to list pull requests, inspect a deployment, or read a file from cloud storage. We already have command-line tools for these operations. An agent becoming the caller does not automatically create a need for another interface. ...

“Necati, how can I burn more tokens?” Someone asked me this recently. I had to pause. I understand wanting engineers to use AI. Give them access. Give them time to learn. Let them try approaches that might fail. But why would consuming more tokens become the objective? ...

I do not think AI cost is just a finance problem. It is mostly an architecture problem. If you are building an AI product, the cost starts with decisions your engineering team makes every day. Which model handles a request? How much information does it receive? How many times does it run? What happens when the first answer is wrong? By the time the monthly bill arrives, those decisions are already in production. You can negotiate a better price, and that may help. But you still need to understand why your system is doing that much work. ...

There are still lots and lots of organizations treating LLM usage during coding interviews as cheating. I think that needs to change. If you expect your engineers to work with AI, your hiring process should evaluate how effectively candidates work with AI. That is becoming part of the job. This is still far from standard practice. In CoderPad’s 2026 survey, 34% of surveyed hiring leaders said AI was not allowed in interviews and assessments. That is a vendor survey, so I would not treat it as a precise measurement of the entire industry. But it gives us an idea of the disagreement. ...

I was recently reading a Wall Street Journal article, and one sentence in it reminded me of Jack Welch, the CEO of GE from the 1980s to 2001, roughly 20 years at the top. I want to talk about that, because I think there is a real connection between what happened at GE and what is happening right now with AI coding agents and vibe coding. The article was titled The AI Superstars Who Say a ‘Vibe Slop’ Crisis Is Coming, and it argues that vibe coding is generating code we cannot really trust. As of now, I agree. ...

Every month, another big tech company announces layoffs. And every time, the same keyword shows up in the headlines: AI. But the real reason is much simpler than that. The Pattern Nobody Questions If you follow tech news, you notice the same story on repeat. Every layoff announcement is tied to AI. “AI is replacing jobs.” “AI made these roles redundant.” The narrative is always the same. Recently, Block – founded by Jack Dorsey, also the co-founder and former CEO of Twitter – was one of those companies. Same story, same keyword. ...

Imagine you’re a CTO at a company with a government contract. Your product runs on Claude from Anthropic. Monday morning, you get a call: Claude is banned from all government systems. Now what? Two Very Different Mornings If you built your AI layer as a plug-and-play abstraction, you’re annoyed, but you’re fine. You swap the provider, run your regression tests, push to staging, move to prod. That’s it. A day’s work. Maybe two. ...

Sam Altman recently said something that caught my attention: “Don’t learn to code.” Instead, he says, learn high agency, soft skills, and idea generation. I think he’s half right—and half dangerously wrong. Yes, high agency, soft skills, and idea generation matter. They mattered before the LLM era, and they still matter now. No argument there. But telling people to stop learning to code? That is the most dangerous advice in tech right now. ...

The Observation I recently watched a new graduate join a team. Fresh out of school. Zero real-world experience. Zero familiarity with the codebase or tech stack. That’s typical—every new hire starts there. What wasn’t typical: he went from “where is the repo?” to helping the team with non-trivial tasks in just a couple of weeks. Not months. Weeks. What Changed Twenty years ago, ramping up in a complex codebase with an unfamiliar tech stack took a minimum of a couple of months to become fully productive. That was normal. Nobody expected you to be fully functional in weeks. ...

Your company just announced a new AI initiative. You hired a Head of AI. You’re “exploring use cases.” You have 56 meetings on your calendar about it. And your engineers? They’re laughing at you. Not with you. AT you. The worst part? You have no idea. After two decades in technology and software engineering, I’ve watched this pattern repeat itself across countless organizations. Let me show you exactly why your AI strategy is failing—and what you can do about it. ...