Argument and interpretation, not announcements.
A rigorous study found experienced developers were 19% slower with AI tools while believing they were 20% faster. That gap between felt and measured productivity is the real story, and it applies well beyond coding.
Every provider sells a big model and a small one, and most people default to the biggest without asking whether their tasks need it. Research on real workloads suggests the answer is no more often than the pricing pages imply.
Every AI product now calls itself an agent, and most of them are chatbots with a new label. The technical distinction is real, it matters for what can go wrong, and it takes two minutes to understand.
The real price of an AI tool isn’t the subscription. It’s how expensive it becomes to leave once your history, workflows, and habits live inside it. Here’s where lock-in actually comes from, and how to check the exits before you need them.
Fine-tuning and prompting both aim to change how a model behaves, but they work in completely different ways. One retrains the model. The other just changes what you tell it. Here’s how to actually choose between them.
A high MMLU score sounds like proof a model genuinely understands its subject. Often it’s closer to proof the model has seen a version of the test already. Here’s what a benchmark score really tells you and where it misleads.
Meta calls Llama open source. The organization that has defined that term since 1998 disagrees. The gap between open-weight and genuinely open-source AI is a real, substantive difference, and it has real consequences for what you’re allowed to do with a model.
Hallucination isn’t a bug that better training eventually fixes. OpenAI’s own research suggests it’s a statistical inevitability. Here’s why.
Prompt injection is ranked the top security risk for AI applications by OWASP. Here’s how the attack actually works, and why no model fully prevents it.