Frankly speaking ...

A Reader Pushes Back Why Lean? argued that formal methods are the industry's answer to a world where testing can no longer keep up with the cost of failure. A reader, commenting on that post , pointed out something the post had left out entirely: sometimes bugs survive not because testing failed to catch them, but because nobody was ever going to fix them. That comment, and the conversation it started, turned into more than a reply. It was the second half of the argument. The Bugs We Choose Not to Fix The reader's point was blunt and specific: a company they had just left refused to fix known bugs because the market and investors wanted to see year-on-year growth, and growth comes from shipping new features, not from fixing what already exists. Bugs get "fixed" only when a client trips over one in production. In effect, the user base becomes an informal, unpaid QA layer — crowdsourced testing whether they like it or not. This is a different failure mode from ...

Since 1956 one of the central goals of AI is achieving Artificial General Intelligence (AGI). Recently we have seen amazing advances in AI, but there are still important challenges to solve before we get there. In this article, I draw on the AAAI 2025 Presidential Panel report to outline what the AI community sees as the critical gaps that must be resolved before AGI can be achieved. AI is in a strange place right now. New benchmark results can make it seem as if the field is nearly solved. Yet these systems still fail basic common-sense tasks that humans manage with little effort. That gap is why true AGI still feels a long way off. In this article, AGI means an AI that can perform as well as a human across a wide variety of tasks, not just produce fluent text. The big problem is what researchers call the "Reasoning Paradox." Today's Large Language Models (LLMs) are very good at producing language that sounds like reasoning, but that is not the same as reliable for...