The marketing promise for premium legal RAG-based models was a hallucination-free experience. The empirical reality is different. Why?
It is a structural problem, created by the way Large Language Models are created. The process includes inputting large amounts of information. This typically includes all the publicly available information on the Internet.
The next step is
She begged “Do not do that,” then “STOP OPENCLAW.” Neither worked.
That’s what happened to Summer Yue, Meta’s Director of Alignment at their superintelligence safety lab. By the time she reached her desktop to kill the process manually, the AI agent she’d created had already deleted hundreds of emails. You would expect someone with
Every year brings a new legal-technology miracle. In 2026, the most aggressively promoted one may be “AI for discovery.” If you have attended even a single conference lately, you have heard the pitch. AI will slash review costs. AI will eliminate drudgery. AI will—apparently any day now—fetch your coffee. That last claim remains unproven.
What
Let’s stop blaming the hallucinations and focus on the real problem:
Lawyers who don’t do their job because they are too busy, too lazy, or too incompetent.
The lawyer who cites a hallucinated AI case and the lawyer who cites a real case without reading it have committed the same ethical failure. Today, it’s usually
The hype machine is working overtime on Agentic AI. Don’t fall for it.
AI chatbots merely respond to prompts. They only give you information. AI agents like Claude Cowork or Openclaw go beyond this. They are built on large language models, but can take action on your behalf.
That sounds great, but there is a
The promise has become a mantra: AI will free lawyers from drudgery so they can focus on higher-value work. Thomas Martin, writing for the Thomson Reuters Institute, points to research from UC-Berkeley that complicates that story considerably. The study tracked what actually happens when knowledge workers adopt generative AI. They don’t work less. They
Monogamy is not a requirement—or even a good idea—when it comes to AI. Multiple AI perspectives help with high-stakes questions, unsettled law, or anything involving tax regulations (which remain confusing even to the IRS). When two models agree, you gain confidence. When they disagree, you gain a warning sign.
Pro Tip: Prioritize the best
When people think about malware, they often imagine someone clicking a suspicious attachment or downloading a shady file. In reality, one of the most dangerous forms of infection requires no obvious mistake at all. It’s called a drive-by download, and it remains a quiet but serious threat.
A drive-by download occurs when
The prevailing narrative surrounding Generative AI in the legal sector is one of unprecedented efficiency. The sales pitch is seductive in its simplicity: automate routine drafting and research, compress hours into minutes, and liberate attorneys for higher-value strategic thinking.
Yet, as the initial wave of adoption settles, a distinct counter-narrative is emerging