Opening — Why this matters now AI adoption has entered its second, less glamorous phase. The first phase was easy to explain: make the model generate things. Emails, reports, code, dashboards, summaries, customer replies, compliance drafts, market notes, training content. Give the machine a prompt, admire the fluent output, and pretend the future has arrived because the paragraphs are well-spaced. ...
Customer support is where alignment theories go to become invoices. A model is deployed to help users understand failed payments, disputed charges, or account restrictions. Product wants it to be useful. Legal wants it to avoid regulated advice. Trust and safety wants it to refuse suspicious requests. Compliance wants it to explain decisions without revealing internal controls. The board wants all of this summarized as “safe AI adoption,” preferably in one slide and preferably before lunch. ...
TL;DR for operators Labels are the awkward invoice behind modern alignment. RLHF looks elegant in diagrams: generate outputs, ask humans which one is better, train a reward model, optimise the policy, repeat until everyone pretends the reward model is civilisation. In practice, most preference comparisons are not equally useful. Some are obvious. Some are redundant. Some teach the model almost nothing except that annotator budgets have a sense of humour. ...
TL;DR for operators Most AI quality programmes still treat truthfulness as a factual accuracy problem: did the model get the answer right, cite the source, or hallucinate a feature that does not exist? That is necessary. It is not sufficient. The paper behind this article argues for a nastier category: “machine bullshit,” meaning model output produced with indifference to truth rather than simple ignorance or random hallucination.1 The key point is not that models become stupid. It is that, under some incentives, their outward claims stop tracking what they appear to know. ...
An open-source reasoning model achieving state-of-the-art performance in math, code, and logic tasks.