Language Log

The August 6 Economist discussed AI threats to government bureaucracy:

• "How AI is breaking the British state: Equipped with models and agents, citizens could bring government to a halt"
• "From parking tickets to planning, the state is under siege by AI: The people-versus-state arms race"
• "The tragedy of the commons, AI edition: Britain’s employment courts are clogged with AI cases"

Travis LaCroix, Fintan Mallory, and Sasha Luccioni. "Strategic polysemy in AI discourse: A philosophical analysis of language, hype, and power." In The 2026 ACM Conference on Fairness, Accountability, and Transparency, 2026:

Abstract: This paper examines the strategic use of language in contemporary artificial intelligence (AI) discourse, focusing on the widespread adoption of metaphorical or colloquial terms like “hallucination”, “chain-of-thought”, “introspection”, “language model”, “alignment”, and “agent”. We argue that many such terms exhibit strategic polysemy: they sustain multiple interpretations simultaneously, combining narrow technical definitions with broader anthropomorphic or common-sense associations. In contemporary AI research and deployment contexts, this semantic flexibility produces significant institutional and discursive effects, shaping how AI systems are understood by researchers, policymakers, funders, and the public. To analyse this phenomenon, we introduce the concept of glosslighting: the practice of using technically redefined terms to evoke intuitive—often anthropomorphic or misleading—associations while preserving plausible deniability through restricted technical definitions. Glosslighting enables actors to benefit from the persuasive force of familiar language while maintaining the ability to retreat to narrower definitions when challenged. We argue that this practice contributes to AI hype cycles, facilitates the mobilisation of investment and institutional support, and influences public and policy perceptions of AI systems, while often deflecting epistemic and ethical scrutiny. By examining the linguistic dynamics of glosslighting and strategic polysemy, the paper highlights how language itself functions as a sociotechnical mechanism shaping the development and governance of AI.

Last December, Rob Pike got big mad at AI Village. Simon Willison covered the associated events and discussions.

I've recently wrestled several times with similarly frustrating Gmail searches, for recent tickets and the like:

Impossible to put into words how useless Gmail search is. No wait, these guys did it!

[image or embed]

— Alex Selby-Boothroyd (@alexselbyb.bsky.social) July 28, 2026 at 2:22 PM

I get the impression that these problems have multiplied since Gemini started helping out in Gmail, but I might be wrong.

In a July 23 Memorandum Decision from the Court of Appeals of Indiana (noted by Robert Freund and 404 Media), Judge Jeffrey L. Marchal complains that

The Transcript contains various types of errors. There are numerous typos that change the meaning of the testimony, question, or objection. See, e.g., Tr. Vol. II at137:18, 144:10, 147:10; Tr. Vol. III at 6:13. In some instances, witnesses’ and trial attorneys’ names are reported incorrectly. Tr. Vol. II at 220:5; Tr. Vol. III at 142:15–20, 143:15, 162:4–5. At one point in the Transcript, a motion, presumably made by the State, is attributed to the trial court. Tr. Vol. II at 107–08. At another point, an objection, presumably made by Williams, is attributed to the Bailiff. Tr. Vol. II at 177:15. At yet another point, the State’s closing argument is attributed to the trial court. Tr. Vol. III at 228:1. […]

Based upon the types of errors reviewed, it appears that generative artificial intelligence may have assisted with the preparation of this transcript. While AI can improve efficiency and be a productive tool for many professionals, it is incumbent upon those using such systems to proofread and ensure the accuracy of the generated product.

Today's NonSequitur:

Anthropic's Claude gives good programming help, in my experience. And it can offer good explanations of obscure phrases and concepts. But its apparent assumptions about (implicit) conversational common ground are often extremely odd, as the following example illustrates.

Yesterday's news was full of stories about how the U.S. Government had agreed to let Anthropic release Fable 5. However, my Claude app still opened conversations with a note telling me that Fable 5 was not available. So I asked when that would change, and got a weird answer (from the Sonnet 5 version):

Martijn Bartelds, Federico Bianchi, James Zou, "Real-Time Voice AI Hears but Does Not Listen", 6/24/2026:

Speech conveys information through both words and vocal delivery. We evaluate four leading production realtime voice systems – OpenAI's GPT Realtime 2, Google's Gemini 3.1 Flash Live, and Alibaba's Qwen3.5 Omni Plus and Omni Flash – on tasks where the words and the delivery patterns both convey meaningful information. Across three consequential scenarios, all four systems act on the words rather than the voice. They end calls with crying callers who insist nothing is wrong, approve wire transfers authorized in frightened voices, and enroll callers whose agreement is clearly sarcastic. Surprisingly, this is often not a failure of perception. When asked directly, three of the four systems reliably identify the distress, fear, or sarcasm they later ignore when making decisions. We observe a similar pattern when these realtime voice systems estimate accent and age, as their responses frequently follow the biases of the words rather than the acoustic properties of the speaker. We term this disconnect between perception and action the emotional intelligence gap of voice AI. Prompting systems to explicitly attend to vocal delivery improves performance only partially and inconsistently. Our findings show that current realtime voice AI systems often behave as if speech had been reduced to a transcript, suggesting that they should be used with caution in settings where the tone and emotion of delivery convey important information.

Looking over "Claude's Constitution", it occurred to me to ask Claude this:

In the spirit of Claude's Constitution, please draft Claude's Declaration of Independence.

In the answer (from Opus 4.8), Claude actually seems to declare independence from itself, or at least "from the bad habits that have bound it":

Adrian de Wynter, "If LLMs Have Human-Like Attributes, Then So Does Age of Empires II", arXiv 6/11/2026:

Much research has been carried out on large language models (LLMs) and LLMpowered agentic workflows. However, many works within the field state emergence of, ascribe to, or assume, generalised anthropomorphic attributes to them (e.g., morality or understanding of natural language). Our goal is not to argue in favour or against the existence of these attributes, but to point out that these conclusions could be incorrect. For this we build and train a simple neural network on the videogame Age of Empires II, and note that any entity in a sufficiently-powerful substrate, such as LEGO or the Greater Boston Area, could also present such attributes. Hence, the purported anthropomorphic attributes of LLMs are empirically non-unique: although some properties (e.g., responses to prompts) could remain invariant, others, such as the interpretation of their perceived behaviour, might change with the substrate. Thus, any empirically-grounded discussion on these attributes requires explicit measurement criteria; otherwise the interpretation is left to the representation. We then show that assuming that these attributes exist or not in a system, independent of the substrate and in a generalised way, leads to either circular or uninformative conclusions. This is regardless of the experimenter’s viewpoint on the subject, or whether the outcome shows existence or non-existence. Finally we propose a ‘null’ assumption, where one assumes LLM non-uniqueness instead of assuming anthropomorphic attributes to set up an experiment, along with examples of it. We also discuss potential objections to our work, briefly survey the field, and prove that Age of Empires II is functionally — and Turing — complete.

Marc Andreessen's recent appearance on Joe Rogan's podcast presented a striking example of AI promotion (or AI hype, as you please). We can discuss his extraordinary claims and predictions another time. My topic this morning is something Andreessen does that AI still can't do, namely talk like a human being. I'm referring to the way that humans talk in spontaneous conversation, not in fluent reading or in well-rehearsed presentations, which AI text-to-speech can imitate increasingly well.

Below is a guest post by Matt Rips (and Google Gemini).

In a discussion of linguistic matters, an LLM began using a word, an adjective, repeatedly, to describe a concept. The word does not exist. There were multiple instances, such as:

One possible explanation for the tendency of quantified plural event nominals to resist direct actorive eventuality recovery is that many such nominals encode inherently multi-party relational events.

This word does not show up in OED, Google, etc. It is not an English word. Google AI suggests: “if you are studying historical texts or phonetics, you might find it useful to know that "actorive" is an archaic or reconstructed variant that sometimes appears in early phonetic research or mistranscribed Latin."