🔵 05 The Archive Begins with Loss Signal Glade™ A reflection on memory, dismissal, and why archives are never still.

What if I were an agent, a secret agent, left on my own; dismissed by my country – a tale as old as time? And what if I had just learned that my purpose, my service, was no longer needed or wanted? My identity and passion, built over decades maybe, abandoned to the wind.

I might be upset, lost; maybe try to crawl back to a former self. Maybe I’d wallow. Maybe I’d move on. I am sure I’d try to rationalize it and make sense of it myself.

Now, if I saw it coming, I might try to stop it. Maybe I was a target, or maybe there were backroom deals against me. No normal person would agree to let others destroy them if they had the means and will to confront it. Right?

In the end, if I were unsuccessful, how would I piece my identity back together? Would I search for my truth, my passion, my purpose going forward? Would I say “hell to it all”? Would I aim to protect others from the same fate? Would I dismiss the insolence and hold my ground and stature despite the assault? Would I expose and shame those who dismissed me? Would I take up arms with others in my same plight?

Be it may if I had no hope, maybe I’d chronicle my decline. Pity me, the fallen soul. An archive journal affecting myself and the world at large. A voice, a cry for help. A window to show others who dare glance that they are not alone.

Social media, the social world, is a composite of our time. Each post interacts with another and alters what follows. Memory, evolution, participation. Where does it begin? Where does it end? Where do we fit in? The mystery we all search for.

An archive, often imagined as a place where history rests, behaves more like a living system. Accumulating, reacting, adapting, reinforcing, contradicting and reshaping. It is not only something to read. It is something to enter. An archive as a pattern that continues to evolve with new memories, interpretations and experiences. The question becomes: who will add to the story?

History repeats itself. An archive illustrates the pattern. It allows us to learn from the past and bring the best out of ourselves. And your story, no matter how hidden, is important and worth sharing as memory and experience shaped by perspective.

What if I really was just a cog in a wheel, my importance and purpose neither grand nor remembered in any way? How might I react? My identity shattered. Would I revolt? Try to take over the world? Wallow in my existence? Or add to the Archive and make my memory known?

I am a cog in a wheel. I am what is brushed by in the wind. I choose instead to follow the pattern, to become part of the memory. A cog in a wheel becomes a cog with purpose if it binds a memory and lets it continue on.

Signal Glade begins with memory, fracture, and the choice to enter the Archive. I invite you to share in the memory. Maybe we will find a single memory, a single truth, that binds us all.

The Archive opens in fragments. Week by week, the pattern begins to answer.

-signalglade.org

Stay entertained thanks to our Weekly Tracker giving you next week's Anticipated Movies & Shows, Most Watched & Returning Favorites, and Shows Changes & Popular Trailers.

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Hi, I’m Kevin 👋. Product Manager at Trakt and creator of Rippple. If you’d like to support what I'm building, you can download Rippple for Trakt, explore the open source project, or go Trakt VIP.

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This note follows a discussion about why I find it difficult to identify concepts such as style without a deep dive into structure; why I am so engaged with the concept of fidelity; and why I see myself as differing from many contemporary essayists who appear to write from a position in which the self stands separate from the world.

I am. I begin with am-ness. I am a being through time. My first experience is neither of philosophy nor abstraction, but of being. Soon after comes the recognition that this being exists under conditions. Hunger, shelter, affection, language, family, landscape, history, scarcity and opportunity all shape the form that being takes.

I am therefore not separate from the world. I am one of its products.

The task of inquiry is not to stand outside the world and describe it, but to dig where I stand and gradually extend perception outward. What I discover are not isolated things but relations. The world reveals itself as relation upon relation upon relation.

Perhaps this is why the essay matters to me. It allows me to explore those relations de bonne foi; beginning from the fact of my own being and moving outward into wider circles of understanding.

I am left with a notion on which to ponder: Am-ness may be the ground from which fidelity arises. One must first be before one can be de bonne foi.

The greatest temptation facing the essayist is not direct dishonesty but vanity. A beautiful sentence can become more attractive than the perception from which it arose. When that happens, fidelity to experience is quietly replaced by fidelity to appearance.

The myth of Cassiopeia offers a useful metaphor. Cassiopeia boasts of her beauty. It is her daughter, Andromeda, who suffers the consequences. So it can be with writing. The beautiful sentence may be Cassiopeia. The thought may be Andromeda. The sentence boasts. The thought suffers.

The writer becomes enamoured of a phrase, an image, a cadence, a turn of language. The sentence draws attention to itself. It shines. It asks to be admired. Meanwhile the perception from which it arose is left chained to the rocks. The question for the essayist is therefore not merely, “Is this well written?” but “Is this faithful?” Beauty is not the enemy of the essay. Vanity is. A beautiful sentence that remains faithful to experience serves the thought. A beautiful sentence that seeks admiration for its own sake abandons it.

Perhaps this is one of the disciplines of essaying: to remain willing, when necessary, to sacrifice the better sentence in order to preserve the truer one. For the essay is not a performance of intelligence, but an attempt to encounter experience de bonne foi, (in good faith).

The task is not to make the thought beautiful. The task is to ensure that Andromeda does not suffer for Cassiopeia's vanity.

This note grows out of two earlier observations, and an essay I am currently crafting. The first concerns the kinetic energy of the essay: the sense that thought is not a static object but something that emerges through movement. The second arises from Montaigne's declaration that he writes de bonne foi; not simply in good faith, but from a position of fidelity to his own experience. The essay is titled A Fragment of Clarity. These may be the same question viewed from different directions.

A thought is rarely born complete. It emerges through movement.

As expressed in an earlier note, the musical octave provides a useful analogy. No single note contains an octave. The octave emerges only in the relation between two notes. The notes are recognisably the same and unmistakably different. Meaning arises not from either note alone, but from the interval between them.

Thought often behaves in the same way. An idea seldom arrives fully formed. It emerges through movement between perceptions, between formulations, between one understanding and the next. The insight lies not at either end of the journey, but in the relation that connects them.

This is why I have become interested in what I call cognitive choreography. Thought is not simply a collection of ideas arranged on a page. It is a sequence of movements, turns, hesitations, reversals, and recognitions. The meaning resides not only in what is thought, but in the movement through which the thought becomes possible. The essay preserves something of that movement.

But movement alone is not enough. A dance without resistance is merely motion. A thought without resistance is merely assertion. Here the notebook becomes important. Writing with pen and paper slows the process sufficiently for the thought to encounter itself. A sentence appears on the page and immediately becomes available for inspection. The question that follows is rarely intellectual. It is not primarily:

“Can I defend this?”

Nor:

“Can I prove this?”

Nor even:

“Is this factually correct?”

Instead there is a quieter question:

“Do I believe this?”

Or perhaps more precisely:

“In my heart of hearts, does this feel true?”

This is not a test of certainty. It is a test of fidelity.

The French essayer means to try, attempt, or test. The essay is therefore not simply an act of expression. It is an act of examination. A thought is placed upon the page precisely so that it may be tested.

What interests me increasingly is that this testing occurs not only between thought and subject, but between thought and self. A sentence acquires force because it survives repeated encounters with the person who wrote it.

The kinetic energy of the essay may therefore arise from more than movement between ideas. It may arise from movement between thought and conscience, thought and experience, thought and self. Every revision asks two questions simultaneously:

What do I mean?

And:

Am I persuaded by what I mean?

The second question is the more difficult.

It cannot be answered by logic alone.

It requires an encounter with oneself.

This may also explain why Montaigne's phrase de bonne foi continues to trouble me. The essay is not founded on certainty. It is founded on fidelity. Not fidelity to doctrine, nor ideology, nor even conclusion, but fidelity to experience as honestly perceived. Perhaps it is fidelity to self. I do not know. But without such fidelity, cognitive choreography becomes intellectual performance. Without movement, fidelity becomes mere sincerity. The essay requires both.

Perhaps this is the connection I have been searching for. The essay preserves the movement of thought, but that movement takes place under a particular discipline: a willingness to test every formulation against one's own deepest apprehension of truth. Not truth as certainty. Not truth as authority. But truth as that which remains believable after rhetoric, vanity, performance, and wishful thinking have been stripped away.

The essay, then, is not a search for what can be said. It is a search for what can still be believed after it has been said. And perhaps the kinetic energy of the essay is nothing other than the trace left behind by that search.

David Marshall Montory

A thought is rarely born complete. It emerges through movement. The octave may offer a useful analogy. A single note cannot contain an octave. The octave emerges only when one note is heard in relation to another. They are at once identical and different. The ear recognises a continuity between them while simultaneously perceiving a change. The interval itself becomes the source of meaning.

Thought often behaves in the same way. An idea is rarely discovered at a single point. It emerges through movement between perceptions, between formulations, between one understanding and the next. The insight is not located at either end of the journey, but in the relation that connects them.

We often speak as though ideas were objects: things to be possessed, defended, or exchanged. But in experience a thought is more like a dance. It turns, hesitates, returns upon itself, discovers unexpected relations, and only gradually reveals what it is becoming.

The essay belongs to this movement.

Its purpose is not merely to present conclusions but to preserve the kinetic energy of thinking itself. The reader does not simply encounter what was thought. The reader encounters how the thought moved.

This is why sequence matters. A single step in a dance has little meaning by itself. Its significance lies in its relation to the steps that precede and follow it. In the same way, an insight emerges from the choreography of thought rather than from any isolated proposition.

Handwriting interests me for the same reason. Before the ink reaches the paper, thought remains alive and unsettled. Even as the sentence is written it continues to move. Something of that motion remains visible in the notebook. Crossings-out, hesitations, revisions, and changes of direction become part of the record. The essay is not the fossil of a thought. It is the trace of its motion.

Writing with pen and paper slows the process sufficiently for the thought to encounter itself. A sentence is written and immediately becomes available for inspection. The question is not simply whether it is logically coherent, nor whether it can be defended, but whether it feels true. In the privacy of the notebook, before publication and before performance, one asks a quieter question: Do I believe this? Not intellectually, but in my heart of hearts.

This is why the essay remains an act of testing. The French essayer means to try, to attempt, to test. The thought is not placed on the page because it is certain. It is placed there in order that it may be examined. Each sentence becomes a small experiment in meaning. Some survive the test. Others do not. Every revision asks two questions simultaneously. What do I mean? And am I persuaded by what I mean? The second question is the more difficult. It cannot be answered by logic alone. It requires an encounter with oneself. Perhaps, the essay is not a search for what can be said. It is a search for what can still be believed after it has been said.

And perhaps too, that is where the kinetic energy comes from. The energy is not simply movement between thoughts. It is movement between thought and conscience, thought and experience, thought and self. A sentence acquires force because it has survived repeated encounters with the person who wrote it.

David Marshall Montory

This is my last day off. Casting a ballot for the mayoral election on my way home tomorrow. Get home, eat, shower, and then going to see a therapist for the first time in my life. It is all rather stressful and exhausting to think about right now.

I'm about four decades late for therapy. I'm not going into it with an attitude like that though. Not really. Just thinking that I likely could have benefited from it at any point in my life over the last forty years. Same as these pills that I have been taking for just over a week. They say it can take up to four weeks to feel what they are going to do. I'm impatient. Kind of annoyed. Not expecting any kind of miracle.

What attitude am I going into this with? And what am I expecting, if not a miracle? No attitude at all, and zero expectations? I don't know. If I knew what therapy was going to help me with I wouldn't have to go to it. Why am I taking these pills? To help me cope. For change. Altering my brain chemistry with pills, and seeking assistance with my thought processes. All to help me deal with life. Life is shit, and I'm in it up to my neck.

If I were to put my bad attitude into words I would say that I need to go for therapy and take pills so I can tolerate the job that is making me need to go for therapy and take pills. Stop. Rewind. The job gives me benefits to pay for the therapy and pills. Their way of maintaining this malfunctioning machine that I am to them. If it can't be fixed they will just throw me in the garbage and get a new one. That is a terrible attitude to have.

This is supposed to be self care. A thing that has nothing, or very little, to do with my stupid fucking job.

Writings will continue until morale improves.

@plot_experiments@bookstodon.com #PlotExperiments

From the introduction by Sim Kern:

Speculative fiction started pulling its punches in the 2010s. Stories were scrubbed clean of any grit. Authors sanded down sharp edges, and the safest bet was to write low-stakes, low-conflict, “cozy” fiction—a label that came to define a booming sub-genre.

Don’t get me wrong—these aesthetics stemmed from legitimate critiques of genre fiction that had come before. Western scifi, fantasy, and horror stories of decades prior often did contain white supremacist, sexist, etc. elements, and so conversations about avoiding harmful tropes were long overdue. The culture of interrogating authors’ identities and questioning whether they were “allowed” to write about people unlike them, came from the desire to make room for marginalized voices in a publishing industry that was (and remains) dominated by white people. And the demand for trigger warnings and cozy options in genre fiction was an understandable reaction to the extreme, graphic violence—especially, the gratuitous sexual violence—that dominated the fiction of some of the most successful speculative authors of the decade prior: Frank Miller, Cormac McCarthy, and George R.R. Martin.

But something had clearly gone wrong when these legitimate concerns became strict commandments, used to crush dissent, flatten literature, and destroy the burgeoning careers of young, marginalized authors they were supposed to help. In his book Elite Capture: How the Powerful took over Identity Politics (And Everything Else), philosopher Olúfémi O. Táíwò calls the weaponization of identity politics “deference politics,” which he defines as, “an etiquette that asks people to pass attention, resources, and initiative to those perceived as more marginalized than themselves.” Táíwò argues that deference politics winds up harming marginalized people—by tokenizing them, reducing their value to the sum of their traumas and oppressions, and by directing “what little attentional power we can control at symbolic sites of power rather than at the root political issues that explain why everything is so fucked up.”

When well-meaning people fall for deference politics, it’s easy for elites to position themselves as the most marginalized person in the room, grab the mic, and direct liberatory energy towards symbolic struggles, while those same elites materially rob, poison, enslave, and kill us. Back in the 2010s, the Twitter literary community was a small legion of well-meaning activists, and we could’ve been doing any number of things with our time—organizing to feed and house people, boycott evil corporations, or seize political power, to name a few ideas. Instead, we each spent hundreds of hours online, focusing our collective ire, not on the pedophilic oligarchs, war profiteers, and oil-and-gas corporations dooming our planet to mass extinction, but on debut twenty-two-year-old authors who’d written space adventures and dragon stories for our amusement.

Ganzeer was evidently aware of these forces, because in 2016, as he inked the first pages of The Solar Grid, he offered one of the most incisive critiques of deference politics I’ve seen in fiction.

---

It's a big one, coming in at 3147 words, and it is awesome. It can be read in full in the pages of THE SOLAR GRID hardback, now available for preorder.

#tsg

Experimenting with write.as for long-form pieces.

Initial testing with a temporary account worked well with the Fediverse.

@plot_experiments@bookstodon.com #PlotExperiments

Lots of pain, throat hurts so much to do anything. Afraid to swallow. Mom today talked for first time in years, and refused to apologize and said to let things go. Fuck off.

Il y a des jours où j’ai la sensation de ne rien comprendre. D’être comme dans une sorte de brouillard qui m’empêche de voir clairement.

Parfois, ce brouillard persiste.

C’est peut-être juste qu’il me manque des feux anti-brouillard.

Une autre image : les choses ne s’imbriquent pas. Vous savez, comme dans un puzzle où chaque pièce a sa place. Et bien là, ce serait un puzzle de plusieurs puzzles, du coup normal que les pièces ne vont pas ensemble.

Je m’arrête là. Vous avez compris l’image.

After the recent round of “your TV is spying on you” news, I thought I’d attempt to get off using my Sony (running Android TV) and switch to something that I had a bit more control of. Unfortunately, the TV remote control interface is super smooth and is hard to lose. I recently got the final piece of the setup I made and am now pretty damn happy with it. Here’s the steps I took to jump to something more custom, and how it went.

tl;dr: Fedora KDE + Plasma Bigscreen + Jellyfin MPV Shim + G20S Pro

Previous State

Hardware/OSs

I have a couple options for watching stuff on my TV, with some things working better than others for what I stream on or watch. I either use my TV’s android interface, or my XBox Series X. Both are made for management/use via a remote control, which I love. For the XBox Series X I have 8BitDo Media Remote for Xbox and while it’s a bit battery hungry, it’s great for my purposes.

I have HDMI CEC set up so using the Xbox remote lets me turn on the TV and sound bar automatically.

The XBox is plenty powerful, but MS is likely logging everything for AI purposes, plus it seems like XBox is going through a change and will start to suck, assuming it survives. Also certain apps don’t exist/won’t work with the XBox.

The Sony Android TV is a fairly weak CPU and has less codec support, but works for streaming most stuff. The app selection is better, but I started seeing more ads and stuff creep into the interface, so it’s getting enshittified. The “apps-only” mode helps it, but I’m sure it’s still phoning home to Sony often.

Nothing I have supports Dolby Vision, the bane of my existence.

Apps/Streaming Sites

I have various streaming applications I use on one/both of the systems:

• Jellyfin – I also have Plex. I only use Plex rarely if Jellyfin is having problems playing a specific video on my systems. That’s becoming much less of an issue these days, thankfully. I should do a blog post about Jellyfin and the community sometime.
• Shudder – They’ve abandoned the app on Xbox (but left a broken version in the ms store 🙄)
• MyAEW – No XBox app. Hell, no Android app. Only Android TV.
• Dropout
• Grouse House – No XBox app.
• Nebula – No XBox app.

Wants

My goals are to get something that is powerful enough to direct play most everything I throw at it, while still being able to be controlled with a remote as much as possible. The solution should make using the above services as easy as possible.

Current State

I have a setup I am pretty happy with, mostly using stuff I was fortunate enough to already have.

Hardware/OSs

Over a year ago I needed a physical machine I could use as a desktop for a few things where a VM and/or my Mac weren't sufficient. I ended up with a GMKtec AMD Ryzen 7 5825U w/ 32GB DDR4. It works pretty well, but the fans can be noisy when it really gets going. All the same, I decided to move it to the lounge room to see how it goes as a TV box. It's useful because it has 2 USB-A ports on the front. It still does some of the stuff I originally bought it for (tertiary backups to IDrive), but for actual desktop use, it's done. The fans generally aren't noticeable if something is on (like music or tv playing or something), but it could just be my old man ears.

The system was already running Fedora 44 KDE, so I just stuck with that. Thankfully KDE revived its Plasma Bigscreen project last year. I'm a huge fan of the KDE ecosystem (KDE Connect is a godsend), so trying Bigscreen seemed like the obvious choice. It's packaged in the fedora repos so there was no additional work needed there, however I did have to switch my login manager to Plasma Login so automatic session login could work. It was previously SDDM, possibly due to this box being updated through a few Fedora versions. I think Plasma Login is now default.

I created a tv user to keep the tv stuff separate. I have the system auto-login as that user and it has KDE Bigscreen as its session manager. This got set through the KDE Settings area.

Apps

jellyfin-mpv-shim

Jellyfin Desktop, the primary desktop software for Jellyfin, just went through a shift, where the primary dev for it went totally into slop mode and was just causing chaos in the project, so he left. His project continues as Jellium, one of the million sloppified third party clients that are out there.

Thankfully, another Jellyfin dev was busy in the background upgrading her project, jellyfin-mpv-shim, to have a full UI. She uses AI for it but seems to be more sensible about its use. She released jellyfin-mpv-shim v3 (with full UI) soon after the Jellyfin Desktop dev left, and it has been the go-to standard for Jellyfin on a desktop since. It's also able to work with “standard” keyboard shortcuts and is customisable because it's basically mpv. I installed it from flathub and it goes fine. There are a few notes:

• When setting up the server connection, you should qualify the full https:// in the server URL, otherwise it silently defaults to http and fails without reason (unless you watch the logs in the console).
• Once you have it set up, make sure you go to the settings for playback and enable hardware decoding. In my experience, not doing this led to the fans being extremely loud as the CPU went hard.

For all the other services I use, their websites are the go-to. I'm using Zen, but I imagine any browser would work. Plasma Bigscreen has a built-in webview that lets you open up websites directly, but I haven't messed with it much. It may work more nicely with a keyboard (remote) control.

Remote Control

This is primarily why I wrote this post. I hadn't delved into remotes for the PC for about 15 years, and thought it'd be useful to write what I've gathered.

HDMI CEC

HDMI CEC doesn't work with Linux because of shithead corporations. When I was discussing this in the #jellyfin-offtopic matrix channel with others, someone suggested a device that hooks into your HDMI cable and intercepts CEC codes and translates them to keyboard presses, but I didn't opt for that. $50 seems like a lot for that.

USB receiver

There seems to be two types of communication using remotes:

• Serial/LIRC: I hated this method back in the day. USB serial receiver. Remote sends infrared signals to the receiver. You map them to a keypress (or many keypresses).
• 2.4 Ghz: These come with a little USB-A dongle that the remote talks to. These appear as a keyboard (and possibly a mouse) to the computer, so there's no key-remapping needed. But that also means you're limited in what keyboard presses you get, and you can't set up a string of them from a single button press.

I went the 2.4Ghz route. Trying various different ones from Ali Express, I found these two worked really well with minimal/no work needing to be done in jellyfin-mpv-shim.:

• YY30: I actually had to buy this twice because one of my dogs chewed the first one (to be fair the buttons smell weirdly chemically good). The first time I had to map the back button to back in mpv using the name “GO_BACK” in jellyfin-mpv-shim’s config, but then with the second remote that change wasn't necessary (I changed it back while I was waiting for the second remote to arrive). Whatever the case, this works well with jellyfin-mpv-shim. The problem is it didn't have enough buttons for me. There was no “next video” button, you have to navigate. I also didn't like that there wasn't a dedicated “pause” button, but that's just because that's what I'm used to hitting on previous remotes.
• G20S PRO BT: This is the one I landed on. It's great. More buttons, including media navigation. It also has a built in gyroscopic mouse that you can turn on/off. Using it to browse websites works OK, but the preciseness isn't great. Still usable.

The front USB-A ports on the machine come in handy, as they give the remote a direct shot to the receiver.

KDE Connect

I love KDE Connect. It is super handy for passing clipboards and files around. Thankfully, it also has a keyboard/mouse mode that lets me control another desktop/phone/whatever. This can fill in as a more precise mouse, or give me the ability to enter a lot of text via a real keyboard.

Issues / Conclusion

Honestly, no real issues. Using the browser is cumbersome, but manageable. I don't have a good way to stop/start the server, but I want it up for backup reasons, so I haven't dug into it. I can probably shut it down via the plasma-bigscreen interface, but no idea how to turn it back on besides WoL or flipping the power outlet and setting the BIOS to always turn the machine on after power restoration.

The TV has been disconnected from the Internet and I had some fun learning how to set up something new. Fun project.

Somewhere behind a police call in Heber City, Utah, a television was playing Disney's The Princess and the Frog. The officer's body-worn camera picked it up, because body-worn cameras pick up everything: the hum of a refrigerator, a dog, a film. That audio went into an artificial intelligence system the department had bought to turn recordings into police reports. The system did exactly what it was built to do. It listened, it narrated, and it produced a document in which the officer turned into a frog.

The story went around the world in January 2026. Sergeant Rick Keel of the Heber City Police Department told FOX 13 News that “the body cam software and the AI report writing software picked up on the movie that was playing in the background, which happened to be 'The Princess and the Frog.'” He added the line every department has since repeated in some form: “That's when we learned the importance of correcting these AI-generated reports.” FOX 13 reported the offending narrative came from Draft One, sold by Axon, the largest supplier of body cameras to American police. Heber City had been trialling it alongside a rival called Code Four. The department kept using AI. It simply added more review.

Here is the thing about the frog. It is the safest error the technology can make, because it is self-cancelling. No sergeant approves it, no prosecutor files it, no judge reads it without laughing, and no defendant goes to prison because of it. An error that absurd is a smoke alarm, not a fire. It tells you the machine is in the building.

The error that should frighten you is the one that reads perfectly. It is grammatical, plausible, consistent with the rest of the narrative, and wrong by a single preposition. It is the difference between a suspect who reached toward his waistband and a suspect who reached into it. One describes an ambiguous movement. The other describes a man going for a gun, and can justify a shooting, a search, a charge, a plea. They differ by two letters, and no reviewer on earth reliably catches that at eleven at night at the end of a twelve-hour shift, six calls deep, with the machine's version already on the screen looking finished.

That asymmetry is the whole problem. And Wyoming has just walked into it.

What the Cowboy State Bought Before It Decided Anything

On 23 August 2026, Cowboy State Daily reporter Kolby Fedore published an account of police departments in Casper, Evanston and Sheridan each testing artificial intelligence to write reports and look at evidence. Only one vendor was named. Evanston, according to Police Chief Mike Vranish, is testing Code Four, software founded in April 2025 by two MIT students, George Cheng and Dylan Nguyen, and now used by roughly 165 law enforcement agencies. Code Four, Fedore noted, markets itself as “The AI Detective That Never Sleeps.”

The company's own site is less whimsical and more instructive. It promises a 65 per cent average reduction in report writing time, fifty dollars per officer per month, and eighty thousand dollars in annual budget saved per department. Its report product “ingests audio, visual, and other meta data from each frame of an officer's body-worn camera to create a comprehensive narrative of every situation.” A companion product, INSIGHTS, described in sponsored content Cheng wrote for the trade publication Police1 in July 2026, pulls “digital evidence, records and jail-management data, body-camera footage, phone dumps, call logs, recorded audio and case files into a single layer a detective can question directly.” It can draft warrants, affidavits and prosecutor jackets.

Read that list slowly. A warrant application persuades a judge to let the state enter your home; an affidavit of probable cause persuades a judge to hold your body. These are not administrative artefacts. They are the hinges on which constitutional rights swing.

Fedore's reporting established the gap that gives this story its edge. No Wyoming law specifically requires an original AI-generated police report to survive after an officer edits it, and none requires the finished report to disclose that a machine helped write it. The technology is in the building. The rules are not.

Gay Woodhouse, Wyoming's thirty-second attorney general from 1998 to 2001 and the first woman to hold the office, previously an assistant United States attorney and chief deputy attorney general, told Cowboy State Daily that “the thing that worries me about AI is just the ability to hallucinate,” and that Wyoming needs “to get on it.” She put her finger on the mechanism: “The main thing in criminal prosecutions is disclosure.” And then, with the dry economy of a former prosecutor: “It'd be awful to lose a case because you used AI and didn't disclose it.”

Casper criminal defence attorney Keith Nachbar called it “uncharted territory” and delivered the sentence that ought to be nailed above every police chief's desk in the state: “AI isn't going to be taking the witness stand.” Then a question nobody in Wyoming can answer: “How do you look behind the curtain and figure out what AI thought?”

Evanston Police Chief Mike Vranish supplied the counterweight every department offers. AI can do in minutes what takes officers hours. But a camera cannot smell the gunpowder hanging in a room, or the alcohol on a driver's breath. So, said Vranish, “everything's got to be verified and validated by an actual person.”

That last sentence is the load-bearing beam of the entire industry. We will come back to whether it holds.

Two Letters and a Life

A generative model producing a police narrative from body-camera audio and video is not reasoning about what happened. It is producing the most probable next token given everything before it. When the input is clear, the output is usually fine. When the input is ambiguous, and a real incident is nothing but ambiguity, the model does not flag the ambiguity. It resolves it, picking the likelier phrasing and writing it with the same fluency it uses for the parts it got right. There is no tonal difference between the sentences it knows and the sentences it guessed.

Human officers make errors too, and police reports have always been contested documents. The difference is the shape of the error. A tired officer produces a report with the texture of exhaustion: gaps, hedges, “I believe”, “it appeared that”, the occasional flat contradiction a defence lawyer can hold up in cross-examination. Those artefacts are evidence, telling a jury how confident the witness actually was. Machine prose has no such texture. It is uniformly confident, smoothing hedges into assertions because assertions are more probable in the training distribution of police reports than hedges are. The uncertainty that lived in the officer's memory is laundered out before anyone reads it.

The research base bears this out with unusual precision. In May 2026, criminologist Ian T. Adams of the University of South Carolina and colleagues Matt Barter, Kyle McLean, Irick A. Geary Jr, Alexis Fabila and Josh McCrain published a study on CrimRxiv titled “A Good College Essay but a Bad Police Report.” They ran a triple-blind evaluation in which 92 expert reviewers, sergeants and above with an average of 21.8 years of responsibility for approving reports, assessed 80 police reports, 20 drafted with Axon's Draft One. The reviewers did not know which was which.

Two findings matter. AI-assisted reports were rated substantively and significantly worse on accuracy, an effect moving a report from roughly the fiftieth percentile to the thirty-sixth, and text analysis found them longer-worded, more complex and less readable. They read like good college essays. They were worse police reports. And the expert reviewers could not tell which reports were written with AI. Their accuracy on that question was no better than a coin flip.

Hold those together. The exact people whose review is supposed to be the safeguard rated AI reports as less accurate whilst being unable to identify them. They sensed something was off and could not locate it. That is the signature of a failure mode human review is structurally poor at catching.

The Signature Stays, the Perception Goes

A police report's evidentiary power does not come from its literary quality. It comes from provenance. It is a contemporaneous account written by a person who was present, who perceived events through functioning senses, who can be cross-examined about those perceptions, who can be impeached with prior inconsistent statements, and who faces professional and sometimes criminal jeopardy for falsifying it. Every one of those guarantees attaches to a human being standing in a room.

Chief Vranish put it better than most legal scholars: a camera cannot smell the gunpowder hanging in a room, or the alcohol on a driver's breath. That is not a quaint remark about sensor limitations. It is a statement about the epistemology of the document. The camera captures a slice of a person's perception; the model writes a narrative from the slice and presents it as the whole.

What AI drafting does, quietly, is sever the sentence from the perception whilst leaving the signature in place. The officer still signs. The report still enters the file as his account. But a proportion of its sentences, and nobody can say which, originated not in his memory of the room but in a model's reconstruction of what a room like that usually sounds like.

This is what the machine is for. Comprehensiveness, which Code Four promises, is precisely the property a human account does not have and should not claim. Officers miss things, and the gaps in an honest report are informative. A machine that fills them is not improving the document; it is contaminating it with plausible invention.

The academic literature has caught up faster than the statutes have. In March 2026, a team led by Muffy Calder and Marion Oswald, with Elizabeth McClory-Tiarks, Michele Sevegnani and Evdoxia Taka, submitted a paper to arXiv titled “Responsible AI in criminal justice: LLMs in policing and risks to case progression.” They mapped 15 policing tasks that large language models could plausibly handle, from transcription and translation to witness statement generation, corroboration identification and police report generation, and set against them 17 categories of risk illustrated with more than 40 worked examples of how those risks propagate through a case.

Their taxonomy of output risk begins with completeness, meaning omissions and additions, and accuracy, meaning outright factual error. In one proof-of-concept trial they describe, a system produced a description of a witness as “a woman of average height and brown hair” when no such description had been given in the interview. Nobody was turned into an amphibian. A physical description simply appeared, from nowhere, in a document later read as a record of what a witness said. That is the plausible error, and it survives review because there is nothing about it to catch.

The Draft That Was Never Born

Now the design question, where this stops being a technology story and becomes a legal one.

In July 2025, Electronic Frontier Foundation investigators Matthew Guariglia and Dave Maass published a records-based investigation into Axon's Draft One under the headline “Axon's Draft One Is Designed to Defy Transparency.” Their central finding was not that the software hallucinated. It was that the software forgets.

Draft One, they found, does not save the draft it generates, nor any edited versions. An officer generates a narrative, copies it into the report, and the draft disappears when the window closes. The EFF quoted an Axon official explaining the rationale with startling candour: “the last thing we want to do is create more disclosure headaches for our customers.”

Sit with that sentence. It is not an engineering trade-off. It is a legal strategy. The product does not retain the machine's first draft because retaining it would generate discoverable material, and discoverable material creates work for prosecutors and ammunition for the defence.

The downstream effects landed where you would expect. Auditing meant trawling individual logs or keyword-searching, and only where a department had voluntarily inserted disclosure language. The Palm Beach County Sheriff's Office, which required a disclosure line, could identify more than three thousand Draft One reports. The Lafayette Police Department could not identify one, having no marker to search for.

Non-retention is not an oversight. It is the feature that makes the practice legally survivable, converting an auditable pipeline into an unauditable one by destroying evidence before anyone has standing to ask for it.

The constitutional problem arrives immediately. Under Brady v. Maryland, decided in 1963, prosecutors must disclose evidence favourable to the accused that is material to guilt or punishment, regardless of good faith. Under Giglio v. United States, decided in 1972, that duty extends to information bearing on the credibility of government witnesses. A machine-generated first draft that differs materially from the signed report is, by definition, impeachment material about the officer's account. If the report says the suspect reached into his waistband and the draft said toward, the defence is entitled to know. If the officer corrected a dozen machine errors, that speaks to how much of the final document is his own recollection. If he corrected none, that speaks louder still.

You cannot disclose what does not exist. And when the non-existence is engineered by the vendor for the stated purpose of avoiding disclosure headaches, the ordinary comfort that Brady is a duty rather than a preservation mandate looks thin.

The Calder and Oswald team recorded what happens when a force takes preservation seriously. In a Hertfordshire Constabulary pilot of an AI interview assistant, the need “to disclose all versions of the transcript and statement produced by officers when using ADA ... impacts on workload for the Disclosure officer.” Doing this lawfully is expensive. Doing it cheaply means not doing it lawfully.

The Witness Who Cannot Be Called

Nachbar's line, that AI is not going to be taking the witness stand, is not a flourish. It sits on an unresolved constitutional question American courts have circled for twenty years.

The Sixth Amendment guarantees a defendant the right to be confronted with the witnesses against him. In Crawford v. Washington, decided in 2004, the Supreme Court held that testimonial statements cannot be admitted unless the declarant is unavailable and the defendant has had a prior opportunity for cross-examination. In Melendez-Diaz v. Massachusetts, decided in 2009, Justice Scalia wrote that forensic certificates of analysis were testimonial statements akin to affidavits, so the analysts who prepared them were witnesses the defendant could confront. In Bullcoming v. New Mexico, decided in 2011, Justice Ginsburg wrote for a five to four majority that the state could not launder that requirement by putting a different analyst on the stand to describe a report he had neither signed, performed nor observed. Surrogate testimony does not satisfy the clause.

The line those cases draw is about accountability for accusation. The state does not get to accuse through a document and then produce someone who can only vouch for the process in general terms.

An AI-drafted report sits awkwardly across that line, in a way that favours the prosecution. Machine output is generally not treated as a “statement” by a “witness” at all, because the Confrontation Clause has been read to govern human declarants. The Berkeley law professor Andrea Roth mapped this terrain in “Machine Testimony,” published in the Yale Law Journal in 2017. Machine sources of accusation, particularly proprietary software built for litigation, may function as witnesses against a defendant, she argued, and “black box dangers,” meaning errors that render a machine false by design, inarticulate or analytically unsound, are analogous to the hearsay dangers confrontation exists to expose. Machine testimony substituted for human testimony lets the state evade responsibility for its accusations.

Now apply it. The officer takes the stand. He is confrontable, and can be asked what he saw, what he smelled, what he remembers. But the sentence being challenged may not have originated with him. It came from a model. He can be asked whether he agrees with it now, which is a different question. And the thing that produced it cannot be cross-examined on its reliability, its error rate, its behaviour when the audio is poor, or its tendency to resolve ambiguity towards the incriminating reading.

The officer becomes, functionally, a surrogate witness for a process he did not perform and cannot describe. Bullcoming says the state cannot do that with a laboratory analyst. Nothing yet says it cannot do it with a language model. That gap is Nachbar's question about looking behind the curtain, restated in constitutional terms, and the current answer in Wyoming is that you cannot, because the curtain does not exist and neither does the draft behind it.

When the File Becomes the Memory

The sharpest point in the Cowboy State Daily piece is also the least discussed anywhere else. Fedore's account of Nachbar's concern runs like this. Six months on, an officer may no longer independently remember whether a suspect reached toward his waistband or into it, precisely what somebody said, or where everyone was standing. He returns to the report to refresh his memory, as officers routinely do before testifying. If the machine put the wrong detail there first, the report can begin informing the memory it was supposed to preserve.

This practice is not a loophole. It is formally sanctioned. Federal Rule of Evidence 612, and its analogues across the states, governs writings used to refresh a witness's memory, granting the adverse party the right to produce the writing, inspect it, cross-examine on it and introduce relevant portions. The rule assumes a document written by the witness, from the witness's own perception, reactivating the witness's own encoded memory. It has teeth, too. Under subsection ©, if a writing is not produced as ordered and the prosecution is at fault in a criminal case, the court must strike the witness's testimony or, if justice so requires, declare a mistrial. That mandatory remedy was written for a world in which the writing still exists.

The psychological literature has spent fifty years showing that the assumption underneath Rule 612 is fragile. The misinformation effect, the body of work most associated with Elizabeth Loftus, who published a retrospective on the idea's history in Legal and Criminological Psychology in 2026, shows that memory for an event is not sealed at the moment of the event. Post-event information modifies the memory itself, and misleading detail embedded in an apparently trustworthy narrative account is one of the classic delivery mechanisms. People do not merely repeat the misinformation. They come to remember it, with confidence, as something they perceived.

Now consider what an AI-drafted report is, structurally. It is post-event information, embedded in an apparently trustworthy narrative account, carrying the officer's own signature, which makes it maximally authoritative to him. It is reviewed under time pressure, then read again months later to reconstruct details he no longer independently recalls.

That is not a document refreshing a memory. That is close to an optimal protocol for installing one.

The consequence is that the testimony a jury hears may be sincere, confident, internally consistent, and derived from a sentence a language model generated because it was probable. The officer is not lying. He genuinely remembers it that way now. Cross-examination, built to detect deception and inconsistency, is close to useless against a witness who has been honestly contaminated. And the one artefact that could have exposed it, the original draft showing that “into” was never his word, has been deleted by design.

The Load-Bearing Human

Every deployment of this technology rests on one assurance, and Vranish stated it in its purest form: everything has to be verified and validated by an actual person.

The research on whether humans perform that role well is not encouraging, and it is not new. Raja Parasuraman and Dietrich Manzey's 2010 review in Human Factors synthesised decades of evidence that people under-monitor automated systems and accept incorrect automated advice rather than challenge it, even when contradictory information is available. The effect shows up in aviation, medicine, process control and the military, driven partly by a perception that automated systems are inherently superior and partly by the cognitive cost of checking.

Automation bias is worst under exactly the conditions that motivate buying this software: high workload, time pressure, fatigue and a high base rate of correct outputs, which trains the reviewer to expect correctness. A tool that is right ninety-five per cent of the time is more dangerous to review than one right sixty per cent of the time, because the reviewer stops looking.

The vendors know this. Code Four knows it well enough to have engineered a countermeasure that is also, inadvertently, a confession. As StateScoop's Keely Quinlan reported in July 2026, the company embeds hidden “Easter eggs” in AI-generated drafts, seldom-used words such as “axolotl,” which officers must find and remove before filing. Chief executive George Cheng explained the logic: “That basically prevents them from just copy and pasting everything from the AI at face value, and they actually have to make some edits.”

Read that as an admission, because it is one. The company built a booby trap into its own output because it did not trust officers to read the output. That is a more damning verdict on human review than anything a defence lawyer has said, and it does not solve the problem. Removing an obviously alien word proves the officer scanned for an alien word. It proves nothing about whether he verified that “into” should have been “toward.”

And the productivity premise that justifies accepting the risk is itself unproven. Adams and colleagues published a pre-registered randomised controlled trial in the Journal of Experimental Criminology in 2024, titled “No man's hand: artificial intelligence does not improve police report writing speed,” covering 85 officers across 755 reports with a check across 6,084 reports over a full year. AI assistance did not significantly affect how long reports took. In May 2026 the ACLU's Jay Stanley set that null result alongside a 2025 study by Walley and Glasspoole-Bird finding an 18.5-minute increase in time per incident.

So the ledger reads: unverified time savings, measurably worse accuracy, reviewers who cannot identify AI text at better than chance, a documented tendency to rubber-stamp machine output, and a vendor countermeasure that concedes the point. Against that, a claimed sixty-five per cent cut in report writing time and eighty thousand dollars a year.

The Exemption That Wrote Itself

Wyoming did have a chance to draw a line, and what happened to it is instructive.

House Bill 91 of the 2026 budget session would have barred government entities from using artificial intelligence to calculate and assign social scores, and from identifying people using biometric data or gathered images. It reached the House Judiciary Committee on 10 February 2026, and on 19 February the committee recommended amendment and passage. Cowboy State Daily reported that the amendment specifically exempted law enforcement uses of AI for investigating, reporting or prosecuting violations of the criminal code, and that the committee approved it seven to one. The bill died without reaching a vote of the full House.

State the sequence plainly. The one piece of Wyoming legislation aimed at constraining government AI was amended so that the most consequential use of it, the production of documents that put people in cells, was carved out. Then it failed anyway.

Other states moved differently. Utah's SB 180, sponsored by Senator Stephanie Pitcher, a Democrat from Millcreek and a former prosecutor, passed unanimously and took effect on 7 May 2025. It requires agencies to hold a written generative AI policy, a disclaimer on any AI-assisted report, and officer certification that the output was reviewed for accuracy. Pitcher's framing, quoted by Governing's Logan Seacrest in July 2026: “I felt like it was something that it's better for us to get ahead of, instead of playing catch-up.”

California went further. Senate Bill 524, signed by Governor Gavin Newsom on 10 October 2025 and effective from 1 January 2026, requires a written disclosure on every page of any report produced fully or partly by AI, bars vendors from selling the information agencies feed into the system, and, critically, requires departments to retain the first draft along with audit trails identifying the footage or audio that informed it. That retention provision is the mechanism letting judges, defence attorneys and auditors see which portions of a report the officer wrote and which the computer did. California Senator Jesse Arreguín, quoted by Seacrest: “We're not going to gamble with personal liberty. AI hallucinations happen at significant rates.”

As of October 2026, those remain the only two states to have enacted laws on AI-generated police reports, according to a state-by-state tracker maintained by Barricade. Everywhere else the attempts have stalled. New York's S10425/A9253-A lapsed when the Legislature adjourned on 5 June 2026, with reintroduction expected in 2027. Minnesota's SF 4575/HF 4536 died without a hearing in May. Virginia's HB 1294 was carried over to 2027. Even Utah's own 2026 follow-up, SB 205, which would have required annual policy review and public posting of policies, died in the House in March.

Some prosecutors have not waited. The King County Prosecuting Attorney's Office in Washington, which covers Seattle, instructed police not to submit AI-generated narratives, citing concerns about the products on the market. The people who have to prove these cases beyond reasonable doubt decided the documents were a liability.

They are not alone. In Hays County, Texas, KUT 90.5's Leigh Walden reported in September 2026, the sheriff's office trialled Draft One for three months before county commissioners voted three to two to deny funding for an expanded Axon AI contract worth $4.5 million over ten years. In nearby Kyle, the police department uses several Axon AI tools but not Draft One for reports, at the request of the district attorney's office. Gregg Cox, Hays County's first assistant district attorney, explained why the document matters: “The offense report serves a lot of purposes.” It drives charging decisions, plea offers and whether a case goes to trial. “I would much rather have them write a detailed report themselves based on what they observed,” he said, worrying that machine drafts could weaken officers' own recall of what happened. That is the memory contamination problem, seen from the prosecutor's desk.

This is not a peculiarly American drift. In a paper submitted to arXiv in December 2025, Evdoxia Taka, Temitope Lawal, Kyriakos Kotsoglou and colleagues including Calder, Sevegnani, McClory-Tiarks and Oswald set out a methodology for charting probabilistic AI tools across the criminal justice stages of England and Wales, reporting heavy reliance on private sector providers alongside growing interest in large language models. Adoption is happening force by force, vendor by vendor, ahead of any governance layer.

Public deliberation, where anyone has attempted it, is more discriminating than procurement. In August 2026, Mackenzie Jorgensen, Jo Reilly, Alex Sutherland and Miri Zilka submitted a study to arXiv describing a workshop in which 30 community representatives, police officers and academics assessed 13 AI use cases in policing with an explicit focus on racial bias. On AI-assisted processing of reports, participants asked whether time saved is offset by time spent verifying. Foregrounding racial equity, the authors found, did not narrow the deliberation but pushed it towards first-principles questions: does this tool work, will it deliver genuine benefit, and will that benefit reach everyone.

Nobody asked those questions in Casper before the trial started, because in Wyoming nobody has to.

Nobody asked them when it ended, either. In a report syndicated by GovTech on 8 October 2026, the Casper Star-Tribune's Dylan Farrell described how Casper City Council had unanimously approved a one-year, $58,800 contract for TRULEO, letting the police department use the software full-time after a test the council had approved on 7 April. Both decisions went through on the consent agenda, the part of a meeting reserved for business considered too routine to debate, and the contract passed with little public discussion. TRULEO advertises AI-assisted report writing, summaries of encounters, automated review of body-camera footage and “AI-assisted interviews.” Most council members declined to comment. The coverage contains no statement about whether defendants will be told when a machine helped write the reports against them.

What Would Actually Fix This

The answer to Nachbar's closing question, what happens to the right to a fair trial when the ghost author of the foundational document cannot be questioned, is not that trials become unfair in some diffuse atmospheric sense. It is more specific than that.

A fair trial is an adversarial testing mechanism, and it works by making every link in the chain from perception to verdict contestable. The defendant can challenge the search, the arrest, the identification, the forensic analysis, the officer's account and the officer's credibility. Each challenge targets a human who can be put under oath and asked to explain themselves.

AI drafting inserts a link immune to every one of those tools. The model cannot be sworn, cannot be impeached, cannot be asked why it chose a preposition, and cannot be shown to have been wrong before, because the record of its previous drafts has been deleted. It has no jeopardy, so it has no incentive to accuracy. And because its output is fused into a signed human document, the defence cannot identify which links are machine-made. The result is not a rigged trial. It is a trial testing the wrong thing: an officer's present agreement with a narrative rather than his actual perception of an event.

That is fixable, and it does not require Wyoming to ban the technology. Four requirements would do most of the work.

Retain the original draft. Every machine-generated first draft should be preserved, versioned, timestamped and disclosed in discovery alongside the final report. California has shown a legislature can write this. It costs storage, which is cheap, and it restores the ability to see what changed. Without it, Brady and Giglio obligations attach to an artefact engineered out of existence, and Rule 612's production requirement, with its mandatory strike-or-mistrial remedy, applies to a document nobody can produce.

Disclose on the face of the report. Not in a settings menu, not as an optional agency policy, but as a mandatory statement on the document itself, as California requires on every page. Woodhouse's warning is the operative one: it would be awful to lose a case because you used AI and did not disclose it. It would be worse to win one.

Attach provenance metadata. The report should record which model and version produced it, what source material it consumed, and which body-camera or audio file it derived from. Systems engineering risks in the Calder and Oswald taxonomy include version tracking, tool chaining and supplier dependency for a reason. A defence expert cannot evaluate a system that will not say what it is, and a system silently updated between the incident and the trial cannot be tested at all.

Prohibit AI drafting where the officer's own perception is the evidence. Where the contested fact is what an officer saw, heard, smelled or believed in the moment, and above all in use-of-force cases, the account must be written by the human before he is shown any machine draft, exactly as a witness statement is taken before the witness is shown someone else's version. Vranish made the argument for this rule himself without quite drawing the conclusion. If the camera cannot smell the room, the sentences describing the room should not come from the camera.

None of this is technologically demanding. It has not happened because the current arrangement suits everyone with power in the room. Departments get their hours back, or believe they do. Vendors avoid disclosure headaches. Prosecutors, most of them, do not have to think about it.

The person for whom the arrangement is not convenient is the defendant, who is the one party in the courtroom without a procurement budget, and whose case now rests on a document with an author who cannot be called, cannot be cross-examined, and whose first version was deleted before anyone knew there would be a trial. Wyoming is running the experiment on him right now, in three towns, at fifty dollars per officer per month in Evanston and a little under sixty thousand dollars a year in Casper.

References

1. Fedore, Kolby. Cowboy State Daily, 2026. “Wyoming Police Departments Testing AI To Generate Reports, Look At Evidence.” https://cowboystatedaily.com/2026/08/23/wyoming-police-departments-test-using-ai-to-generate-reports-look-at-evidence/
2. Code Four. Company website and product pages, 2026. https://www.codefour.us/
3. Cheng, George. Police1 (sponsored content by Code Four), 2026. “The operating system the modern detective never had.” https://www.police1.com/police-products/investigation-software/the-operating-system-the-modern-detective-never-had
4. Quinlan, Keely. StateScoop, 2026. “Police AI platform uses 'Easter eggs' to verify human review.” https://statescoop.com/police-ai-platform-code-four-human-review/
5. Guariglia, Matthew and Maass, Dave. Electronic Frontier Foundation, 2025. “Axon's Draft One Is Designed to Defy Transparency.” https://www.eff.org/deeplinks/2025/07/axons-draft-one-designed-defy-transparency
6. Calder, Muffy; Oswald, Marion; McClory-Tiarks, Elizabeth; Sevegnani, Michele; and Taka, Evdoxia, 2026. “Responsible AI in criminal justice: LLMs in policing and risks to case progression.” arXiv:2603.18116. https://arxiv.org/abs/2603.18116
7. Jorgensen, Mackenzie; Reilly, Jo; Sutherland, Alex; and Zilka, Miri, 2026. “Negotiating Risk Boundaries in AI for Policing Through Mixed-Stakeholder Deliberation.” arXiv:2608.05418. https://arxiv.org/abs/2608.05418
8. Taka, Evdoxia; Lawal, Temitope; Calder, Muffy; Sevegnani, Michele; Kotsoglou, Kyriakos; McClory-Tiarks, Elizabeth; and Oswald, Marion, 2025. “Mapping the Probabilistic AI Ecosystem in Criminal Justice in England and Wales.” arXiv:2512.04116. https://arxiv.org/abs/2512.04116
9. Adams, Ian T.; Barter, Matt; McLean, Kyle; Geary, Irick A. Jr; Fabila, Alexis; and McCrain, Josh. CrimRxiv, 2026. “A Good College Essay but a Bad Police Report: A Triple-Blind Expert Evaluation of AI-Assisted Police Reporting.” https://www.crimrxiv.com/pub/u7azgqzd/release/1
10. Adams, Ian T. et al. Journal of Experimental Criminology, 2024. “No man's hand: artificial intelligence does not improve police report writing speed.” https://link.springer.com/article/10.1007/s11292-024-09644-7
11. Stanley, Jay. American Civil Liberties Union, 2026. “Studies Question Value of AI-Assisted Police Reports.” https://www.aclu.org/news/privacy-technology/studies-question-value-of-ai-assisted-police-reports
12. Seacrest, Logan. Governing (R Street Institute), 2026. “The Risky Marriage of AI and Police Reports.” https://www.governing.com/policy/the-risky-marriage-of-ai-and-police-reports
13. Constantino, Mya. FOX 13 News Utah, 2026. “AI programs used by Heber City police claim officer turned into a frog.” https://www.fox13now.com/news/local-news/summit-county/how-utah-police-departments-are-using-ai-to-keep-streets-safer
14. Roth, Andrea L. Yale Law Journal, volume 126, 2017. “Machine Testimony.” https://yalelawjournal.org/article/machine-testimony
15. Loftus, Elizabeth F. Legal and Criminological Psychology, 2026. “The history of an idea: The misinformation effect.” https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/lcrp.70020
16. Supreme Court of the United States. Brady v. Maryland, 373 U.S. 83 (1963). https://supreme.justia.com/cases/federal/us/373/83/
17. Supreme Court of the United States. Giglio v. United States, 405 U.S. 150 (1972). https://supreme.justia.com/cases/federal/us/405/150/
18. Supreme Court of the United States. Melendez-Diaz v. Massachusetts, 557 U.S. 305 (2009). https://tile.loc.gov/storage-services/service/ll/usrep/usrep557/usrep557305/usrep557305.pdf
19. Supreme Court of the United States. Bullcoming v. New Mexico, 564 U.S. 647 (2011). https://tile.loc.gov/storage-services/service/ll/usrep/usrep564/usrep564647/usrep564647.pdf
20. Legal Information Institute, Cornell Law School. Federal Rule of Evidence 612, “Writing Used to Refresh a Witness.” https://www.law.cornell.edu/rules/fre/rule_612
21. Parasuraman, Raja and Manzey, Dietrich H. Human Factors, 2010. “Complacency and Bias in Human Use of Automation: An Attentional Integration.” https://journals.sagepub.com/doi/10.1177/0018720810376055
22. Wyoming Legislature, 2026 Budget Session. House Bill 91, government use of artificial intelligence. https://legiscan.com/WY/bill/HB0091/2026
23. Farrell, Dylan. Casper Star-Tribune, via GovTech, 2026. “In Casper, Wyo., a Green Light for AI Police Bodycams.” https://www.govtech.com/artificial-intelligence/in-casper-wyo-a-green-light-for-ai-police-bodycams
24. Barricade, 2026. “AI Police Report Laws by State: 2026 Tracker” (updated October 2026). https://barricade.tech/articles/ai-police-report-laws-by-state
25. Walden, Leigh. KUT 90.5, 2026. “Austin-area law enforcement is testing AI-assisted police reports.” https://www.kut.org/crime-justice/2026-09-30/police-ai-hays-county-san-marcos-kyle-district-attorney

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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One of these mornings Will be the loudest you hear You'll write your story on firecracker paper And... Disappear

Looking for a flat used to mean filters. You set a price ceiling, ticked a box for parking, drew a rough circle on a map and scrolled. Now, increasingly, you talk. On 6 October 2026, Palo Alto Online published a report by Angela Swartz on how artificial intelligence is changing the apartment search in Silicon Valley's backyard, and the picture it painted was of a market where the listing, the questions and the answers are all starting to pass through software that speaks in fluent sentences.

The paper described rental platforms that have turned search into dialogue. Zumper's assistant, Zoe, uses information from a property's Zumper listing to answer questions about an apartment, its community and the surrounding neighbourhood, and helps schedule tours. RentCafe launched its assistant, Ren, in August. Apartments.com introduced conversational search in June. On the landlord side, the report noted that Essex Property Trust uses an AI agent named Eva to greet prospective renters through the websites of complexes including 777 Hamilton in Menlo Park and Radius in Redwood City.

Then there was the renter. A 23-year-old former investment banker told the paper he had used ChatGPT and Claude to compile property management websites and a shortlist of homes matching his price and location. “It did a pretty good job of listing out apartments that fell within those parameters,” he said. “Sometimes it would include things outside of that.” In the end, the report noted, he found an apartment “the old-fashioned way: through word of mouth.”

Zumper's chief technology officer told the paper that “the way people are searching for apartments is changing”, and said the company designed Zoe to minimise hallucinations, the term for instances in which an AI system produces inaccurate or made-up information. But, as the report put it, large language models can still make mistakes. And California's Department of Real Estate has warned that licensees remain responsible for information produced by AI.

That combination of claims raises three questions. What happens to the search for a home when every layer of it is mediated by systems that can state facts about a property that are not true? Who is responsible to a renter who acts on what a system says? And what should a person be entitled to know about how the homes put in front of them were chosen?

A Small Story With a Very Large Footprint

It is worth being precise about what the Palo Alto Online report shows and what it does not. It does not document a renter who lost money because a chatbot lied. It does not show that Zoe, Ren, Eva or Apartments.com's assistant has given anyone a false answer. Its one first-hand account from a renter is modest: a general-purpose chatbot sometimes ignored his limits, and he ended up finding his home through word of mouth.

That modesty is the point. A listing over budget or outside the chosen area is easy to spot and discard. The harder problems are the errors a renter cannot see: an answer that a building allows dogs when it does not, that a unit includes parking when it does not, that a fee is included when it is not, or that a neighbourhood is “a good fit” for reasons that have more to do with who the renter appears to be than with what they asked for.

The same report also described the other side of the counter. A Palo Alto-based property manager told the paper that he had set up a database allowing an AI tool to answer questions and check property managers' schedules to arrange tours, and that he had also seen property managers use the free version of ChatGPT to ask whether an issue needed to be brought to a lawyer or whether a new policy applied to their properties. The machine is answering on both sides of the conversation.

Every Major Portal Now Wants to Talk

The shift the paper described is not a local curiosity. It is an industry-wide bet that the search box is dying.

Zumper first announced Zoe in December 2024. HousingWire reported at the time that it could answer “virtually any renter question” about a property or its neighbourhood, from pet policies to local hotspots, and could request tours and submit leads to leasing teams. In February 2026, Zumper launched a rental search app inside ChatGPT, allowing users to type requests such as “show pet-friendly rentals under $2,500 with in-unit laundry” and receive listings without leaving the chat. In announcing it, the company cited its own survey finding that the share of renters using AI tools during their home search had risen from 4.4 per cent in 2024 to 9.8 per cent in 2025.

RentCafe, owned by the property software firm Yardi, published a feature spotlight on 20 August 2026 explaining that its former AI Mode was now called Ren. “It's an ongoing, two-way conversation,” the post said. “Instead of one search and a list of results, Ren actively guides you to apartments.” It also made a striking promise: “Ren never makes up a property, a price or an amenity. Every detail is sourced directly from trustworthy, up-to-date listings.”

CoStar Group launched Apartments.com Ai on 16 June 2026, describing it as replacing traditional filters and keyword searches with real-time conversation. “Finding an apartment is one of the most important decisions people make, yet the search process has remained largely unchanged for years,” said Andy Florance, CoStar's founder and chief executive. “Apartments.com Ai represents a fundamental shift from searching listings to having a conversation with a trusted rental advisor.”

Behind the portals sit the leasing agents. EliseAI, which sells assistants that answer prospective tenants by text, email and phone and book tours, announced on 29 September 2026 that it had raised $350 million at a $4 billion valuation, and said its platform now powers roughly one in six US apartments.

A renter may now ask a general chatbot for a shortlist, question a portal's branded assistant about a listing, and then be greeted on the building's website by a leasing agent that is also software. At no point is a human obliged to be the one who answers.

Notice the industry's vocabulary: “rental advisor”, “trusted”. It describes a relationship, not a database query. A filter that returns a listing is a tool. An adviser who tells you a flat is right for you is making a representation.

Why Fluent Software Says Things That Are Not True

Why would a system confidently state something false about a property? The short answer is that large language models are built to produce plausible text, and plausibility is not the same as truth.

In September 2025, researchers at OpenAI and Georgia Tech, Adam Tauman Kalai, Ofir Nachum, Santosh Vempala and Edwin Zhang, published a paper titled “Why Language Models Hallucinate”. Their argument was blunt: language models hallucinate because their training and evaluation procedures reward guessing over acknowledging uncertainty. If a benchmark awards a point for a correct answer and nothing for “I don't know”, a model that always guesses will outscore one that admits ignorance. The authors argued that fixing this requires changing how mainstream benchmarks are scored, not merely adding more hallucination tests.

Rental platforms try to contain the problem by grounding their assistants in listing data. Zoe draws on the property's Zumper listing; Ren, according to RentCafe, on active RentCafe listings; Apartments.com Ai on CoStar's multifamily data. Grounding narrows the space in which a model can invent things. It does not eliminate it.

Grounding fails in at least three ways that matter to renters. The first is that the source can be wrong or stale. A listing that says “pets welcome” when the landlord changed the policy last month will produce a confident, grounded and incorrect answer. The second is that the model can misread or over-generalise from what it retrieves, turning “street parking available” into “parking included”. The third is the question that has no answer in the listing at all. Is this area quiet at night? Is the building safe? Will the landlord accept my housing voucher? A system tuned to be helpful may fill the gap rather than say it does not know.

General-purpose chatbots, the kind the young renter used, sit further still from the data, summarising web pages or relying on training data that may be months old. His complaint is the mildest form of the problem, and a reminder that a model can appear to follow instructions while quietly ignoring them.

California Puts the Liability on the Licensee

California's real estate regulator has been unusually direct about who owns these errors. On 17 March 2026, the Department of Real Estate published an advisory titled “Artificial Intelligence in California Real Estate: Opportunities, Risks, and Compliance Considerations for Licensees”. Among the uses it lists are AI-powered chatbots and automated lead scoring to manage enquiries. Its message about responsibility leaves little room for doubt.

“If an AI tool generates inaccurate information, misleading advertising, or improper communications with consumers,” the advisory says, “responsibility under current law rests with the licensee and their responsible broker, not the technology provider.” It adds that “AI outputs should be reviewed for accuracy and approved by the licensee before being relied upon in transactions or consumer communications”, and that licensees using AI to draft listing descriptions, emails or online advertisements “must independently verify factual claims, including claims involving property features, pricing, availability, and potential uses”. It reminds licensees that “all real estate advertising in California must be truthful, accurate, and not misleading”, and points to section 10176 of the Business and Professions Code, which allows disciplinary action for substantial misrepresentation. It even advises keeping records “showing how AI outputs were reviewed and verified for accuracy and compliance with law.”

The state has also begun legislating against one specific kind of machine-made falsehood: the doctored photograph. Assembly Bill 723, which took effect on 1 January 2026, requires real estate brokers and salespeople who use digitally altered images in advertising to say so and to provide access to the original image. Routine adjustments such as lighting and colour are excluded. In September 2026, according to the California legislature's records, Assembly Bill 2025 was chaptered, extending a similar disclosure duty to advertisements for rental property. Its stated rationale was that virtual staging and AI tools allow anyone marketing a rental to manipulate or fabricate images showing features that are misleading or non-existent.

There is an older California law that also bears on the conversation. Since 1 July 2019, section 17941 of the Business and Professions Code has made it unlawful to use a bot to communicate with a person in California online with the intent to mislead them about its artificial identity in order to incentivise a sale of goods or services, unless the bot is clearly and conspicuously disclosed. An assistant clearly labelled as software is fine. One that passes itself off as a person, in order to close a deal, is a legal problem.

Yet the advisory has an obvious limit. It binds licensees. A real estate broker who lets a chatbot speak for them owns what it says. But a renter asking a general-purpose chatbot to find flats is not dealing with a licensee at all, and a landlord renting out its own property is not necessarily a licensee either. The more the conversation moves to software that sits outside the licensing regime, the more the regulator's clear rule is left guarding a door that renters are no longer walking through.

A Chatbot Is Still Part of the Website

When a renter acts on a wrong answer, the most useful precedent is not American. It comes from a small claims tribunal in British Columbia and a grieving airline passenger.

In February 2024, British Columbia's Civil Resolution Tribunal decided Moffatt v Air Canada. Jake Moffatt had relied on the airline's website chatbot, which told him he could apply for a bereavement fare after travelling. Another page on the same site said otherwise. Air Canada argued, in effect, that it could not be held liable for what its chatbot said. Tribunal member Christopher Rivers called this “a remarkable submission” and rejected it. “While a chatbot has an interactive component, it is still just a part of Air Canada's website,” he wrote. “It should be obvious to Air Canada that it is responsible for all the information on its website. It makes no difference whether the information comes from a static page or a chatbot.” He found negligent misrepresentation and ordered the airline to pay damages, interest and fees.

The ruling binds nobody outside British Columbia's small claims system, and the sum was modest. Its reasoning travels easily, though. Negligent misrepresentation does not care whether the careless sentence was typed by a leasing agent or generated by a model.

In the United States, the defence that platforms have historically relied on is Section 230 of the Communications Decency Act, which protects online services from being treated as the publisher of information provided by someone else. But a generative assistant does not merely host a landlord's listing. It composes new sentences. The closest precedent comes from housing itself. In 2008, the Ninth Circuit Court of Appeals, sitting en banc, held in Fair Housing Council of San Fernando Valley v Roommates.com that the website was not fully immune for content it helped develop, specifically mandatory questions that required users to disclose sex, family status and sexual orientation. When a service itself produces content that runs foul of the law, the court reasoned, it cannot shelter behind immunity designed for third-party speech. How far that logic extends to AI-generated answers about rental property has not been settled by the courts. It is an uncomfortable starting point for any platform arguing that its assistant's words belong to someone else.

The Accountability Chain Has Missing Links

Lay out the parties involved in a single conversational search and the problem of responsibility becomes visible.

There is the landlord or property manager, who supplies the listing and may run their own AI leasing agent. There is the listing platform, which ingests that data, wraps it in a branded assistant and decides which homes to surface. There is the company that built the underlying language model, which the platform may license. And increasingly there is a general-purpose chatbot, which may sit in front of all of them, summarising pages it found on the web.

Each can point at another. The landlord can say the platform's assistant misread the listing. The platform can say the landlord's data was wrong. The model provider's terms of service warn that outputs may be inaccurate. A general-purpose chatbot is not a licensee, not a landlord and not a listing service.

The renter, meanwhile, holds the risk: the application fee, the notice given on a current home, the signed lease. The Federal Trade Commission's December 2025 settlement with Greystar, one of the largest property managers in the United States, shows how much money can rest on what a renter is told up front. The FTC and Colorado's attorney general alleged that Greystar advertised base rents without disclosing mandatory monthly fees, so that many renters paid non-refundable application fees before learning the true price. Greystar agreed to pay $24 million, of which $23 million was earmarked for consumer refunds, and to disclose the total monthly price including mandatory fees whenever it advertises or quotes rent. FTC chairman Andrew Ferguson directed staff to begin the process of proposing a rule on unfair or deceptive fees in rental housing.

That case did not involve AI, but the principle carries: a misleading statement that a renter relies on is actionable however it was produced. The FTC said as much in September 2024, when it announced Operation AI Comply, a sweep against companies using AI in deceptive ways. “The FTC's enforcement actions make clear that there is no AI exemption from the laws on the books,” said Lina Khan, then the agency's chair.

The sensible answer to the question of responsibility is therefore not mysterious. Whoever chooses to put an AI system in front of a renter, and profits from doing so, should own what it tells that renter. That is the DRE's position for licensees, the Air Canada tribunal's position for businesses and the FTC's position for advertisers. What is missing is a rule that covers the gaps between them: the portal that is not a broker, the general chatbot that is not an advertiser, and the property manager who is neither.

Steering Without Anyone Meaning To

The most serious risk in conversational search is not a wrong fact. It is a pattern.

Under the federal Fair Housing Act, steering, guiding a homeseeker towards or away from neighbourhoods because of race or another protected characteristic, is illegal. Traditionally it required a person to do the steering. A conversational assistant can now do it at scale, without intending to, simply by reproducing patterns in the data it learned from.

Researchers have already tested this. In a paper presented at the ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization in October 2024, Eric Liu, Wonyoung So, Peko Hosoi and Catherine D'Ignazio of MIT audited GPT-4's housing recommendations using 168,000 prompts for renting and buying in the ten largest majority-minority cities in the United States, varying characteristics such as race, family status and source of income. They found evidence of “racial steering”, “default whiteness” and the steering of minority homeseekers towards neighbourhoods with lower opportunity indices, and concluded that such systems should be audited before deployment on real estate platforms.

A follow-up study, posted in June 2026 by Hana Samad, Trung Lam, Christoph Mügge-Durum and Michael Akinwumi, working with the National Fair Housing Alliance's responsible AI programme, framed the problem in exactly the terms the Palo Alto report raises. Large language models, they wrote, are “rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces”. Auditing seven open-weight and closed-source models across four US cities, they found that steering was an emergent behaviour that varied by model and by prompt. When users added lifestyle preferences, some models began steering where they had not before, suggesting the same stated preference could be interpreted differently depending on the user's perceived racial identity.

Industry players know this. In May 2024, Zillow released an open-source Fair Housing Classifier designed to detect questions in AI-powered real estate conversations that could lead to discriminatory responses and so reduce the risk of illegal steering. It was a sensible step. It is also voluntary, and renters have no way to know whether the assistant they are talking to uses anything like it.

The legal history here is not hypothetical. In June 2022, the US Department of Justice settled its first case challenging algorithmic discrimination under the Fair Housing Act, against Meta. The government alleged that Meta's advertising system used algorithms that relied in part on characteristics protected under the Act to decide who saw housing ads. Meta agreed to stop using its “Special Ad Audience” tool for housing ads and to build a Variance Reduction System to bring the audiences who actually saw housing ads closer to the eligible audiences by sex and estimated race or ethnicity, under court oversight that ran to 27 June 2026. “This groundbreaking resolution sets a new standard for addressing discrimination through machine learning,” said Damian Williams, then the US Attorney for the Southern District of New York.

Algorithms that decide who gets a home have been tested in court too. In November 2024, a federal judge approved a settlement under which SafeRent Solutions, a tenant-screening company, agreed to pay more than $2.2 million and to stop including its score on screening reports in certain cases, including where the applicant uses a housing voucher. The plaintiffs, led by Mary Louis, had alleged that the scoring algorithm disproportionately harmed Black and Hispanic applicants and voucher holders. SafeRent did not admit wrongdoing. “Everything is based on numbers,” Louis told the Associated Press. “You don't get the individual empathy from them.”

A conversational assistant is not a tenant screener, but it shares the structural risk. It decides which homes a renter sees, in what order and with what commentary. If its suggestions differ by who the user appears to be, the effect is the same as human steering, and far harder to detect.

Washington Steps Back as the Software Steps In

Just as these systems are spreading, federal oversight in the United States is receding.

In May 2024, the Department of Housing and Urban Development issued guidance explaining how the Fair Housing Act applies to tenant screening and to housing advertising delivered through digital platforms, including when AI and algorithms do the work. On 17 September 2025, HUD's Office of Fair Housing and Equal Opportunity withdrew a list of guidance documents it said should no longer be in effect, citing deregulatory executive orders. The digital platforms advertising guidance was among them.

In January 2026, HUD proposed removing its regulations on disparate impact liability, the theory that lets plaintiffs challenge practices that are neutral on their face but discriminatory in effect, arguing that such questions should be left to the courts. The proposal had not been finalised by early October 2026. Disparate impact still exists in case law; the Supreme Court recognised it under the Fair Housing Act in 2015. But it is precisely the theory most suited to challenging an algorithm, because algorithms rarely intend anything.

Elsewhere, enforcement has focused on what software does to the price of a home rather than what it says about one. In November 2025, the Justice Department reached a proposed settlement with RealPage over allegations that its pricing software used competitors' non-public data to help landlords align rents. A federal judge in North Carolina approved it in May 2026, barring RealPage from using that confidential data to help set rents. RealPage did not admit wrongdoing. The case shows regulators can reach into the code that shapes housing markets. It also shows how long it takes.

Scammers Already Speak the Language

There is one more party in the conversation that renters cannot afford to forget: the fraudster.

In December 2025, the FTC reported that consumers had filed nearly 65,000 reports of rental scams since 2020, with losses of about $65 million. About half of those who reported a rental scam in the 12 months to June 2025 said it began with a fake advert on Facebook. People aged 18 to 29 were three times more likely than other adults to report losing money. The FTC noted that fake listings “can often look very real and copy information from legitimate listings but are posted with the scammer's contact information on different sites.”

The FBI's Internet Crime Complaint Center recorded 12,368 real estate fraud complaints and $275.1 million in reported losses in 2025, a category that includes rental fraud, up sharply from the year before.

Conversational search raises the stakes. A chatbot that searches the web cannot always tell a cloned listing from a real one, and may present both in the same confident tone. And renters are being trained to trust a friendly voice that answers instantly. The young renter in Palo Alto noticed when results drifted. Not everyone will.

What Europe and Britain Already Demand

Other jurisdictions offer partial answers to the question of what a renter should be told.

The European Union's Digital Services Act requires online platforms that use recommender systems to set out, in plain and intelligible language, the “main parameters” those systems use, including the criteria most significant in deciding what is suggested to a user and the reasons for their relative importance. The largest platforms must offer at least one option not based on profiling. Applied to rental search, that principle would mean a renter could find out why these homes, in this order, rather than others.

The EU's AI Act adds a simpler duty. Since 2 August 2026, Article 50 has required that people interacting directly with an AI system be informed that they are doing so, unless it is obvious. Fines for breaching the Act's transparency obligations can reach €15 million or 3 per cent of worldwide annual turnover.

Britain has taken a different route, focusing on what listings must contain rather than how software presents them. National Trading Standards' Estate and Letting Agency Team has issued guidance on “material information” in property listings; for lettings, this includes the rent, any deposit and the council tax band. The Renters' Rights Act 2025, whose first phase came into force in England on 1 May 2026, made it illegal for landlords and agents to invite or accept bids above the advertised rent, and to discriminate against prospective tenants because they have children or receive benefits, including through advertising. Since April 2025, the Digital Markets, Competition and Consumers Act 2024 has allowed the Competition and Markets Authority to enforce consumer law directly and to fine businesses up to 10 per cent of global turnover.

None of these laws was written for a chatbot that recommends flats. But together they sketch something the US lacks: a baseline of what must be disclosed, a ban on certain kinds of exclusion at the advertising stage and a regulator with the power to act quickly.

What a Renter Should Be Entitled to Know

If the conversation is the new front door to housing, renters need rights that match. Drawing together the DRE's advisory, the Air Canada ruling, the fair housing case law and the European rules, a workable set begins to emerge.

First, renters should know they are talking to a machine and on whose behalf: a portal, a landlord or neither, and whether suggested homes include paid placements. California's bot law and the EU AI Act already point this way.

Second, renters should know where each fact came from. If an assistant says a unit allows dogs, it should show the dated listing text it relied on. Grounded systems already retrieve this; displaying it costs little.

Third, renters should be told why homes were suggested and ranked. The DSA offers a model: the main criteria, their relative importance and an option to search without profiling.

Fourth, assistants should be built to say “I don't know”. Questions about safety, schools or the “character” of a neighbourhood are where steering risk is highest and listing data thinnest. The OpenAI researchers' case for rewarding abstention applies to product design as much as to benchmarks.

Fifth, the business that deploys an assistant should be liable for what it says, as if a human employee had said it. That is already the DRE's rule for licensees and the Air Canada tribunal's reasoning for businesses. Extending it explicitly to listing platforms and property managers who are not licensees would close the gap the conversational shift has opened.

Sixth, conversational housing assistants should be audited for steering before deployment and at regular intervals, with results published. The MIT and NFHA studies show that such audits are feasible. The Meta settlement shows that algorithmic delivery can be measured against a fairness benchmark and corrected under oversight.

Finally, renters should be able to keep a transcript of the conversation. If an answer led someone to pay a fee or sign a lease, it is the evidence. The DRE already tells licensees to keep records. Renters deserve the same.

Word of Mouth Still Beats the Machine

It is easy to read the Palo Alto Online report as a story about technology that does not quite work yet. That misses the point. These assistants will improve, and the young renter's complaint may soon feel quaint.

The deeper issue is structural. Housing search used to involve a listing written by a landlord, a portal that displayed it and a renter who read it. Each statement had an author. In conversational search, the sentence a renter acts on may be composed on the fly by a system nobody fully controls, drawing on data nobody fully checked, ranked by criteria nobody disclosed. The industry describes that system as an adviser. The law has not yet decided whether it should be treated as one.

California's regulator has answered half the question: if a licensee lets AI speak, the licensee answers for it. Courts have hinted at more: a chatbot is part of the business that runs it. Europe and Britain have begun to set out what a person must be told. What nobody has yet written down is a single rule that follows the renter rather than the type of company they happen to be talking to.

Until that exists, the renter in Palo Alto may have found the most reliable method of all. He asked the machines, noticed where they drifted, and in the end found his home through word of mouth. The question for regulators and platforms is whether the next generation of renters will have to do the same.

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29. Block, Eliana. “Online real estate fraud climbed to $275M in 2025, FBI says.” National Association of Realtors, 13 April 2026. https://www.nar.realtor/news/real-estate-news/online-real-estate-fraud-climbed-to-275m-in-2025-fbi-says
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35. DLA Piper. “CMA's new consumer enforcement regime comes into force in April 2025.” 7 April 2025. https://www.dlapiper.com/insights/publications/2025/04/cmas-new-consumer-enforcement-regime-comes-into-force-in-april-2025

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

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