Opening — Why this matters now The AI industry has spent the last two years trying to turn large language models into workers. The result is a small circus of agents: coding agents, browser agents, research agents, support agents, spreadsheet agents, and agents that appear to exist mainly to summon other agents. Naturally, the next problem is not intelligence. It is management. ...
Opening — Why this matters now For years, enterprise AI strategy has been framed as a binary choice: rent intelligence from cloud APIs, or spend lavishly recreating a miniature hyperscaler in-house. Charming fiction. A new benchmark on System Dynamics AI assistants suggests a third path is maturing quickly: highly capable local inference stacks running frontier open-source models on prosumer hardware. Not everywhere. Not universally. But enough to make procurement teams nervous and GPU vendors philosophical. ...
Opening — Why this matters now Everyone wants AI to predict the future. Markets want alpha. Governments want warning signals. Executives want next quarter to behave politely. Yet most AI forecasting systems still operate like overconfident interns: one quick answer, suspicious certainty, and little memory of how they got there. A recent paper, Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs, proposes something rarer: an AI forecaster that updates its mind step by step, tracks evidence, and occasionally admits uncertainty. Revolutionary behavior, frankly. ...
Opening — Why this matters now The AI industry enjoys announcing that models now perform at medal level on Olympiad mathematics. Impressive headlines. Elegant demos. Much applause. Then MATHNET arrives with the social grace of an auditor. This new benchmark shows that while leading models can often solve difficult mathematics, they are far worse at finding related problems, recognizing structural equivalence, or reliably using retrieved examples to improve reasoning. In practical terms: your AI intern may ace the exam, then fail to locate the right binder. ...
Opening — Why this matters now Everyone wants AI agents that remember. Very few want to pay for what memory actually requires. The market has spent two years pretending larger context windows solve persistence. They do not. A 1M-token window is still amnesia with excellent short-term recall. Once the session ends, the machine forgets your preferences, confuses stale facts with current ones, and happily re-learns the same details next Tuesday. ...
Opening — Why this matters now Everyone wants an AI assistant that can answer business questions instantly. Fewer people ask the awkward follow-up: from what data, using which logic, and with what guarantees? The modern enterprise stack is not one neat database. It is a sprawl of SaaS tools, PDFs, spreadsheets, APIs, internal tables, web sources, and half-remembered user preferences. Yet many AI products still behave as if one LLM prompt and a pleasant tone can replace data infrastructure. ...
Opening — Why this matters now The enterprise AI stack has a favorite illusion: if you retrieve the right documents, you will get the right answer. It’s a comforting belief—engineer better embeddings, expand context windows, sprinkle some graph retrieval, and the system will eventually behave. Except it doesn’t. The paper “Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure” fileciteturn0file0 argues something quietly inconvenient: the bottleneck is no longer retrieval fidelity—it’s epistemic fidelity. ...
Opening — Why this matters now In AI, we’ve spent years chasing completeness. More data. More models. More outputs. More possibilities. And in optimization? The holy grail has long been the Pareto frontier — a beautifully complex surface representing every optimal trade-off between competing objectives. It looks impressive. It feels rigorous. It is, frankly, overkill. ...
Opening — Why this matters now Most enterprise RAG systems are quietly overconfident. They retrieve what looks relevant, stack it into a context window, and let the model produce an answer with unnerving certainty. The problem isn’t the model. It’s the question we’re asking the system to optimize: relevance. In messy, real-world environments—legal disputes, financial analysis, conflicting reports—relevance is not the bottleneck. Uncertainty is. ...
Opening — Why this matters now There is a quiet but decisive shift happening in the world of AI agents. For the past two years, we’ve been told that agents “learn” by remembering — storing prompts, reflections, and reasoning traces. A polite fiction. Memory, in this context, is little more than annotated hindsight. But real systems don’t scale on hindsight. They scale on reusable execution. ...