The real problem for the frontier AI labs is not that their models are too powerful and already “misaligned” with the goals their creators set for them. Rather, it is that the models are being trained in ways that may be leading to a type of intelligence that will become less predictable.

One of the issues that I have with the current generation of “AI” products is that they do not appear to take their own premises seriously. I look at a plethora of obsequious chatbots claiming to be serious tools for problem solving, and I think, this is not what a problem-solving tool would look like.

Even before we get to the tremendous ethical problems with the frontier labs, it is this impression of their composition as a product that makes me feel, constantly, whenever I am interacting with them, that they are less a software product than that they are a grift, a scam designed to make me feel like I am interacting with a product that has capabilities that it simply does not, to try to lull me into a false sense of security that I can trust it.

The frontier labs are of course the worst offenders, but every criticism here applies just as much to Ollama, which (if anything, due to the obviously poorer quality of the available models themselves) needs these features even more than the frontier labs do.

Here, I will set down a few features that might convince me that an LLM-based product, particularly one focused on research or software development, was actually serious about helping me do useful things with it.

Make “Checking For Mistakes” A First-Class Feature

This is the biggest issue, and the major reason that I was inspired to write this post.

It is a truth universally acknowledged, that AIs cannot reliably provide information.

I could cite a ton of news articles and studies about this fact, but there is no need. Every single chatbot admits this, up front, in a fine-print disclaimer as a core part of their user interface. Gemini says “AI can make mistakes, so double-check responses”, Claude says “Claude is AI and can make mistakes. Please double-check responses.¹” ChatGPT says “ChatGPT can make mistakes. Check important info.”.

Every time I see that last one, I wonder how I’m supposed to know what “info” is supposed to be “important”.

All of these warnings are all small, gray text, painfully obviously included as legalese to push responsibility back onto the user rather than to help with anything. This is a core limitation of all these products. Checking their output is a part of the workflow for using them that:

1. you absolutely cannot skip or skimp on without creating risks to yourself and whoever you are conveying its output to, and,
2. it is very easy to skip or skimp on and you are encouraged at every turn to do so, because “just trust the output” is one of the quickest ways to save time.

A chatbot product that took this weakness seriously, as an actual consideration for using it, would put a checkbox next to every claim in its output. It would be a 2-column worksheet, where you’ve got the LLM output in the first column, and next to it, human notes in the second column, explaining what work went into checking this claim, and a big checkbox that you would only check off after you believe you’d checked its claims thoroughly enough.

Coding assistants would need to have some version of this as well. Right now, this is pushed off into code review, which means it is a dark pattern which subtly encourages the “author”² to offload this work to their code reviewer without ever looking. Once again, “it’s probably fine, I don’t need to check” is the quickest way to save time and churn out those PRs faster.

It might even be useful for coding harnesses to have some affordance for checking code before it even runs tests. As the vendors themselves have admitted, it’s not just expensive to burn tokens on your “AI”, you also end up burning far more compute on the AI. Being able to check your diffs before sending them over to uselessly exhaust your testing compute cluster would be useful.

If your product tells me that it makes mistakes and I must be the one to check for the mistakes, but then gives me zero tools to check for mistakes, I cannot take it seriously.

More Citations to Check, And More Details

Most chatbots prefer to give an answer, rather than a citation. In my own personal use, I find that when asked to provide a list of citations with clearly marked sources for each one, they will appear to “get bored” halfway through the list and simply stop including citations at some point.

When the bots include citations at all, present them as inline annotations that say nothing but the domain name of the search result, in a font so small that it’s barely legible, and an equally indecipherable icon that is fewer than 16 pixels on a side.

This is backwards.

Now, I am aware that these citations do come from somewhere, and in an attempt to reduce hallucinations, all of the major providers support some form of “grounding”³, and that those little barely-readable citation links are referencing actual structures in the RAG pipeline and not just potentially-hallucinated tokens, but I’m not talking about the underlying machinery in the model, I’m talking about the presentation to the user.

Plus, regardless of whether a snippet of text came from a RAG query, we know that LLMs can never provide an authoritative result; it’s a fundamental limitation of the technology. They can still garble the results of RAG as much as they can misrepresent any other training data. This means that it must never present its results as authoritative.

If you ask an AI to do research queries, every result should be presented as a list of citations. Moreover, the presentation should each the citation as a large object of in its own right, with clearly identified metadata, including not just the site where it was found but its publication date and, if possible, the name of the author. The literal, unmodified quotation (not from RAG, not a summary: a quotation extracted with a regular program and not an LLM) should be front-and-center, larger than any AI-generated text.

If the AI product wants to editorialize or summarize (which should not always be necessary!), the AI-generated text should be presented as small text underneath the citation that has been found, de-emphasized as much as the disclaimer is right now, at the very least until the user has verified that the summary is accurate. Perhaps, for a research project, a “did you read the citation” checkbox might even be helpful.

If your product openly tells me that it will scramble, misrepresent, or omit its citations in its summaries, and I must read the original human-authored citations to be sure, but then gives me no tools to track my reading of those citations or even any way to find them, I cannot take it seriously.

No First-Person Output, No Apologies

There is no reason for a software development or research tool to use first-person language to describe itself. They should not do so. In fact they should not be allowed to do so.

There is also no reason that they should ever apologize. It is a waste of everyone’s time; it’s a waste for the chatbot to generate the apology, it’s a waste for the user to read the apology, and it’s a waste for the user to respond to the apology. Yet they unfailingly do this upon every correction.

The vendors of these tools know that they are routinely causing mental-health crises. In response, they have added non-functional “guard rails” that can still, in 2026, easily be bypassed.⁴

A product seriously interested in helping with productivity would correct this glaringly obvious flaw, focus on the task at hand, and stop emitting useless verbiage.

In the previous two sections, I tried to focus on ways in which the harness would be constructed differently even if the LLM technology is fundamentally impossible to improve; in this case, I have to assume that the labs have some control over the model itself. But unless they are truly incapable of influencing their output (and all their “benchmarks” and “capabilities” seem to indicate that they can control it very tightly) they ought to be building models that are much less verbose.

More Non-Natural-Language User Interfaces

Although natural language could hypothetically be a powerful interface for interacting with a computer system, the practical upshot of LLM natural language interfaces is that these interfaces are imprecise and repetitive, full of superstitions masquerading as “best practices”. The inputs are a mess and the resulting outputs are a mess.

The general way of addressing this unstructured mess is to allow the chatbot to directly take action in response to the user’s input; in other words to supply it with “tools” via an MCP server. But again, this is backwards. If we cannot even express our intent clearly in the first place, why are we trusting this system to take potentially destructive and harmful actions on our behalf?

Instead, I would expect a product that was seriously invested in helping me accomplish specific tasks, to have user interfaces specific to those tasks. Is it supposed to be able to be a security scanner that can discover OWASP top 10 bugs in a codebase? Have a button for that. Build that functionality into your harness, train it directly into the model, use smaller models that can satisfy that functionality more effectively than throwing it at the planet-sized brain of Fable or whatever.

I’m aware that there are small software startups that do something like this, but they are bolted on to the side of the main model providers’ APIs, not integrated into the core of the product and not using their own models and AI systems to achieve consistent and repeatable results.

Strong Data Provenance Indicators

Chatbots produce data tables pulled from websites, from APIs, from MCP tools or from summarizing and scrambling the user’s input. In order to provide the illusion of a seamless interface, this data is presented in-line regardless of where it comes from. But some of these outputs are produced mechanically via regular old API calls, for example, from the result of calling a tool or querying a website, but presented uniformly.

But there is a huge difference between an authoritative data source being inlined as part of a chatbot conversation, being treated as input by the chatbot, and some ad-hoc hallucinated data being treated as output of the chatbot.

If a product is trying to help me make accurate, empirically-grounded, data-driven decisions, the source of the data is critical.

Integrated into the “check for mistakes” and “verify citations” workflow I described above, there’s a necessary “verify data programmatically” pass as well; to have tools that will treat portions of the output as a regular spreadsheet, allowing regular-old computer arithmetic to verify things and showing where such arithmetic was used, and how.

Better User Control of Reproducibility

Anyone familiar with the technical specifics of LLMs will know that they have a variable called “temperature” which controls the degree of randomness that the LLM uses to produce its outputs. But most users don’t know this, because it isn’t exposed as part of the user interface by default.

This leads to a subjective impression that you asked ChatGPT, and you got ChatGPT’s authoritative answer.

You can’t just set the temperature to zero and still get useful results - I am aware that it does more than just scramble the output at random, and there are perhaps good reasons that simply exposing just a temperature setting would not be that useful to users. But if we followed some more of my earlier recommendations for making more structured UI elements to solve specific problems rather than having long back-and-forth chats where each refinement depends on the previous response, perhaps those elements could also re-play the process so that users can see how reliable the bot is at a particular task and develop a sense of how the stochastic nature of the process actually affects it.

Similarly, if a user is trying to solve the same problem repeatedly with a chatbot, and the chatbot product has numerous computational tools that don’t really have anything to do with the LLM, such as deterministic data-processing tools, then having a way to freeze the non-deterministic parts of the transcript but re-populate a particular data frame with updated information and fork / continue the conversation from there would be a way to avoid introducing pointless additional randomness when you already know what tool you’re trying to use.

The fact that every conversation is presented as this flat chat prompt that doesn’t let me interact with any of the widgets that were previously produced except through more chatting, really makes me feel like the whole product is just doing predatory social-media style “increase time on site” optimization, just trying to lure me into further repetitive and unreliable chats, rather than letting me get in, solve my problem, and get out.

Context Visibility

Managing the LLM context is the ongoing challenge facing organizations that are trying to use “agentic” workflows. Filling up the context with too much information causes well-known problems. In response, advanced LLM users attempting to solve larger problems must break up very long prompts into “skills”, give access to lengthy information via “tools”, and delegating sub-problems to “sub-agents” rather than simply extending a single prompt indefinitely.

All of these strategies have flaws, because even on the largest models, compared to the breadth and depth of knowledge-work problems, LLM contexts are quite small.

And yet, none of these products will show the context to the user by default. There are third-party addons that can show you a simple progress bar but for addressing the premier engineering difficulty with this technology, that is below the bare minimum.

This lack of visibility means that almost all of the tools for extending the context are flying blind. Rather than responding meaningfully to a full context, everyone just kind of guesses how much state they need by guessing and trying over and over again with progressively more elaborate skill and sub-agent layouts. Even managing context compaction ends up being an advanced API-driven workflow⁵.

A serious product that was trying to help the user understand would not only show “available context” but explain the impact of context compactions, make it easier to see harness-generated prompts, and so on. This would be a first-class feature, combined with the aforementioned reproducibility / replay tools, would allow users to do real experiments to develop an understanding about how to make good use of the context window.

A Sandbox That Actually Works

I’ve been focused on the chatbot interface here because it is the most immediately egregious upon looking at the UI. But the “agentic loop” tools used for coding are equally dangerous, if not more so. Coding tools keep destroying everyone’s data, over the course of years.

These catastrophic incidents that become front-page news are relatively rare compared to the amount of coding-agent use out there. But they also aren’t the only kind of sandbox violation. Coding models will so routinely edit test code instead of the system under test that there are “pro tips” articles all over the web giving you the flawed advice to simply ask the agent not to cheat. News write-ups of the catastrophic incidents themselves will also offer glib and wrong advice, like “use a docker container”. That might prevent it from literally deleting your operating system, but it won’t prevent it from destroying all the local work you have in your codebase (it needs access to a checkout, after all!)

There is a flurry of activity in the infosec space where people are rushing to plug the gaps left by these coding harnesses. Everyone’s got their own version of an MCP approval gateway where you can optionally place a proxy between your agent and your production infrastructure.

In the best case, though, all these mitigations and proxies and prompts simply turn the user into an auto-approval automaton, hitting Y, Y, Y, Y over and over again, until you finally are driven mad and hit “yes to all”, turn on full-auto mode and submit yourself to the void. With nothing between your personal vigilance and disaster, there are no workflows left beyond decrementing your own vigilance until there’s nothing left and then hoping the disaster never arrives.

The fact that some mitigations exist that can be deployed by extra-cautious users does not change the fact that “agentic coding” is an unsafe-by-default technology deployed without concern or guidance. Every frontier lab has tied a spring-loaded shotgun to a dog; the fact that dog owners can publish thoughtful blog posts explaining how you can teach your dog the basics of gun safety or how you can have your dogs play in a bullet-proof room does not mitigate the fact that the product should not have been allowed in the first place, nor should it continue to exist without VERY strong security controls.

I might believe that a frontier lab were seriously interested in providing developers with a useful tool if they shipped something that had safety built-in.

That means tools in the harness, detached from any LLM, independent of the prompt, that could:

• sandbox all filesystem operations and strictly limit ANY deletions outside of specified scopes, regardless of operating system,
• enforce snapshotting of the entire repo on every operation for easy rollbacks and minimal lost work,
• remove the disaster of “auto mode” (not to mention nonsense like --dangerously-skip-permissions) entirely, and
• carefully consider a structure for presenting plans to the user where, rather than provoking immediate alert fatigue by asking for checks on every action, make structured plans which can be submitted to the user as a group of actions and reviewed and approved as a batch.

In the same way that I suggested above that research-based tasks should have a way of re-issuing prompts to determine how reproducible a result is, or whether other sources might be found, agent-based tasks should have a way of being executed against mock services for popular APIs, so that the verification can match both on the front-end (review the plan for making the API calls before they’re executed) and the back end (review the API calls that were issued to the mock service and verify that they matched).

Instead, the frontier labs provide us products that are disasters out of the box, give us “best practices” to build massive and elaborate, as well as incomplete and error-prone, security perimeters of our own design. Then they blame “operator error” when it inevitably goes wrong. I cannot believe that these design choices are intended to help us be productive.

Bonus: Human Processes

Organizations deploying AI also frequently come across as unserious, for similar reasons. In 2023, naive exuberance could perhaps be forgiven. But today, as we near the close of 2026, there are several well-known problems, that have been extremely well-covered in the press. None of these things should be surprising, but most orgs deploying these tools are still just letting them rip and hoping it all works out.

Organizations deploying these tools would need at least three kinds of major modifications to their internal processes, if they wanted to be serious about using them safely:

1. Shift Rotations to Prevent Vigilance Decrement

There have been several high-profile incidents where software developers’ gradual acquiescence to accepting LLM output have lead to serious economic consequences for the companies deploying them, perhaps best typified by Amazon’s “millions of lost orders” due to a gradual decay of their engineering processes from LLM use.

These outages, and other AI-related failures, are due to the difficulty of maintaining focus on the same problems. In other words, as I described above, vigilance decrement is a constant problem, because AI outputs are most often correct, but continue to be incorrect in surprising and non-intuitive ways. As I have previously written, you cannot trust yourself to catch every bug with code review, and LLM output.

Aviation, for example, has very strict rules around rest requirements. There is also a specific rule that “No certificate holder may operate an aircraft without a second in command if that aircraft has a passenger seating configuration, excluding any pilot seat, of ten seats or more.”. Other safety-critical professions have similar rules.

And yet, even in the age of the supposed “AI revolution”, most software teams are still assigning every engineer a full feature load, not planning for any rest, and telling people to review code whenever they happen to have some “free time”.

Maintenance of vigilance has to be your top priority. Regular, scheduled, inviolable rest periods where people do work without AI assistance, and are not exposed to any AI output for review or otherwise, would be crucial in order to stay mentally sharp enough.

The tools themselves should have this sort of thing built in. The mistake-review process described above should have a periodic spot-check mode where a second reviewer periodically reviews a chatbot log, doing their own independent verification of claims, to see if they spot the same errors. This could provide a feedback loop to determine how much rest is necessary to maintain continuous attention and actually spot hallucinations.

2. Skill Practice To Prevent Skill Loss

It is also well-known that AI use leads to AI reliance, and AI reliance leads to skill loss.

I like to use the analogy to dockworkers at a seaport⁶ adopting automation.

If you employ dockworkers to load and unload ships all day long, they are going to be getting tons of exercise. They will be able to lift heavy objects on demand, whenever. They might have plenty of health problems and injuries from this type of work, but “lack of exercise” will not be a problem.

With the development of standardized container ships and mechanized cranes, you are going to be changing their job description substantially: now they mostly spend all day sitting in a small cubicle moving a control lever back and forth, not lifting heavy stuff. They will get worse at lifting heavy objects.

In this analogy however, the cranes are not all that reliable. We know they break, and they drop their payloads sometimes, and the stuff needs to be manually moved. But this only happens a few times a week, at most. If you need whoever is driving the crane to be able to jump out at any moment and still move stuff around manually, then you need to make an affordance for that. You need to give them time to go to the gym and do some lifting for practice, or every crane failure is going to be a major emergency.

An organization doing an AI transformation would also need a massive increase to learning & development budget, both in terms of resources and in terms of schedule. If your people are going to lose skills because they’ve lost regular practice in the incidental course of doing their duties, then they are going to need deliberate, intentional, non-incidental practice of those skills to keep them sharp.

But rather than trying to accommodate new workflows and give time for people to adjust, most AI mandates are simply dropped on workers like a ton of bricks, with no time to adapt and no affordance for maintaining their skills. Operate the crane and stay fit and healthy and ready to switch back to manual lifting at any time and then get back in the crane cockpit right afterwards. Don’t mess up.

Then an accident happens and everyone is surprised, as if this process weren’t practically designed to produce a terrible result.

3. Mental Health Resources to Deal with Mental Health Risks

AI psychosis often begins with practical problem-solving, and beyond that, it can start specifically at work. Not to mention the more pedestrian condition of “AI brain fry”.

If you are mandating your employees to use a hazardous tool that may seriously and directly damage their mental health, you need trainings and resources. You need in-house therapists and you need to be making sure to check in with people actively to make sure that this is not happening.

Again, the tool itself ought to have some way of dealing with this. An occasional “take a break” popup is easily dismissed; they need a user-visible AI personal dosimeter so you can see your cumulative usage over time.

I don’t even know if “usage over time” is a sufficient metric to gauge risk. Maybe if your work chatbot start to talk about resonance too much, unless you literally work as an acoustic engineer, that should be flagged for someone.

We are, again, years into dealing with these tools, and we know these risks exist. Yet no serious mitigations are provided. Not even any way of measuring the risk exposure.

And More

There are also many other risks associated with the technology. There are intellectual property risks with the foundation models, due to recklessness with their training data. There are existential financial risks associated with the infrastructure build-out. The extent to which most “open” models are simply derivatives of frontier models is an open question.

What I Think

If any one of these things were regularly overlooked by AI vendors or users, that would be a totally normal product oversight. Room for improvement for the next version, but nothing catastrophic.

Shipping without any of them doesn’t seem like lean product management, it seems like a careless attitude towards risk and a product design philosophy oriented entirely towards short-term demos, with no regard for how to realize actual productivity gains.

Furthermore, being available for years without anything like these features, despite hundreds of incidents demonstrating the risks, with hundreds of billions of dollars of funding, makes it seem to me like if they were to add all the features that would make their product actually safe and hypothetically useful, these features would reveal that it is actually not an improvement to productivity.

In the year since I first wrote about measuring the cost/benefit ratio of AI, I have heard from numerous people who have shown this to management to try to illustrate why their AI initiatives — like almost all AI initiatives — were either failing or burning out their engineers.

I’ve also heard from lots of people that have told me that it’s obviously useful and they don’t need to measure so carefully, because they are getting lots of work done that they couldn’t have otherwise.⁷

I have yet to hear from a single person who has said “yeah, we measured according to your methodology⁸, and it turns out that our AI work is going great and that our ratio is 0.75”.

Obviously, I cannot say for sure why this is; absence of evidence is not evidence of absence. But at this point I think the null hypothesis is that AI tools provide, in aggregate, zero value. They make mistakes too often, and the externalities they produce are so bad and so difficult to control that even before we get to the places where they are just physically poisoning people, even the negative effects on their direct users end up cancelling out whatever benefit to they provide to their organizations.

If I were wrong, then including tools to measure an AI’s effectiveness at the tasks their users are actually trying to accomplish, rather than meaningless benchmarks, would show big productivity gains. The frontier labs would be champing at the bit to add such features, and crowing about their fantastic results.

I think the labs know that if they did that, it would present a grim picture to their users. Such tools would let their users see that it’s making mistakes much more often than they realized, that they’re spending much more time with it than they want to be, and that it’s just generally not fit for purpose.

If they prove me wrong by adding in all of these safety mechanisms, and in the process, they make all of their AI technology less harmful, I’ll be thrilled to be debunked.

Acknowledgments

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From the Coney Island History Project:

On September 21, 1966, real estate developer Fred Trump threw a demolition party at Steeplechase Park’s Pavilion of Fun, exactly two years after Coney Island’s legendary amusement park had closed.  While the champagne flowed and bikinied models posed for photos, Trump invited guests to hurl bricks through the stained glass Funny Face, the symbol of Steeplechase.

And from the Trump File:

Fred acquired Steeplechase amusement park on Coney Island in 1965 and began lobbying his political circles to change the zoning laws so that he could build real estate on the property.

His efforts failed, though, because his political co-conspirators were falling out of power and locals wanted the park to become a landmark. In a last-ditch effort, Trump demolished the Steeplechase amusement park on September 21, 1966, before it could be named a city landmark, and the city turned on him. Without many political connections or public support left, Fred Trump was never able to follow through on a construction project again.

In October, the city announced that it planned to buy the property back from Trump, which eventually happens. Even though his plan failed, Trump still pocketed a $1.4 million profit.

And now we get to watch the son destroy an entire nation, instead of just an historic amusement park. He apparently learned his lessons well.

--30--

I know! I know what you're about to say because I say it more than anyone: There is no point in taking the ravings of a dementia-addled lunatic seriously! It's a fool's errand! It will only drive you slowly mad!

But then the narcissistic lunatic pipes up with something so wacky that it's impossible to look away. It wraps around the lunatic spectrum to become a work of art. It's like watching Picasso eat a stained glass window.

I am speaking, of course, of His Dictatorness' Latest Weekend Belch.

Wha—what? Huh? You okay there, ya big crook?

All right, I know. Should we ignore this, given that it is so clearly meant as a distraction from $8 diesel, expected stagflation, the Trump family's latest schmoozing with child-abducting Russian oligarchs, the abject failure of Trump's Iraq War, the continued ICE assaults on citizens and illegal renditions of non-citizens, military strikes on fishing boats, billions of dollars in corruption from Dear Leader alone, and the announcement that Secretary of Testosterone Pete Hegseth has ordered mandatory military-wide testing to ensure everyone's getting morning erections?

Yes! We probably should ignore it! But it's impossible, because all that bad stuff is happening everywhere around us but over here in this corner you've got a drunken Pablo Picasso picking pieces of the Duomo di Milano out of his teeth and come on now: You can't just ignore that.

At the strong request of the United States Military, and for National Security purposes, I have agreed to convert the magnificent Triumphal Arch, planned since the Civil War Era many years ago, at the Receptive Circle adjoining the Arlington Memorial Bridge, into a top grade Military Complex/Triumphal Arch,

Top. Grade. The rest of that is Donald "Dear Leader" Trump's usual drivel, with a lie or a delusion shoved into every individual phrase, but the new delusion here is the insistence that his batshit Loser Arch will not just have some sort of quasi-military observation post crammed on top like he's been claiming this whole time but will now be a top grade "Military Complex."

Because sure, why would you even have an imperial megamonument if it wasn't also going to be a "military complex." The Arc de Triomphe was built to shoot lasers at passersby, a dream that was never realized because lasers hadn't been invented yet; Hitler's planned Nazi Arch was designed to be so massive that it would simply topple onto whatever enemy troops dared touch it, squashing them into paste.

Trump's original plan was to install golden statues on top so gaudy that invading armies would gag in disgust, which would give local teenagers a brief opportunity to Red Dawn their asses, but then he announced that it was going to be a more active military compound, one with a folding chair at the top that some poor lieutenant would sit at day in, day out, so that Donald could tell the Supreme Court that the whole arch was a national security project that Congress and the courts weren't allowed to weigh in on.

And we know that's all this is. It's the same play Trump used to justify tearing down the East Wing of the White House to put up his Ballroom That Is Definitely A Ballroom But Also Ummmm A Drone Port For Some Reason. Donald Trump is extremely stupid, his only method of scheming is to bellow the details of his scheme stupidly and at the top of his lungs, and because he is rich he has always been able to convince the schmucks that surround him to go along with the grift even if it makes them look like the biggest suckers on the planet. It's his whole thing. Sorry, there's a troop in a folding chair at the top, nobody's allowed to criticize me now.

But from here we get into the batshit insanity that almost makes this art. The part he apparently dreamed up the night before, possibly after watching old Airwolf reruns. It's going to be the world's only combination Top Grade Military Complex slash Triumphal Arch. It's going to be a Murder Arch.

to house, store, and have the rapid ability to use large numbers of drones, plus Snipers, on both the roof and plaza areas, and additionally have and hold large quantities of sniper ammunition in storage. There will be no facility like this anywhere in the World.

Now, to a large extent this is only the ravings of a madman, but on the other hand it's the ravings of a currently powerful madman so that changes things a bit. To be sure, however, it's insane. It would be cute in a five year old: Trump's post reads a lot like a preschooler trying to explain their latest drawing to you, their new invention that's an arch but also it's a robot and this part over here dispenses dinosaurs and the dinosaurs breathe fire and eat people.

But that doesn't cancel out the more sordid truth: This lunatic is our president, not a five year old shoving crayons up his nose. So now, for only the fifth or so time in my life, I now want the Sunday shows, the White House press corps, and all those puddlebrained television pundits to focus on this plan and ask Donald Trump every possible question about his "plan." This isn't just bullshit, on his part. This is a grand design for America's future, a vision of what the nation will be like once Trumpism has disposed of its enemies and can reign ascendant until the point when unchecked rich asshole-backed climate change Venusifies the planet and turns us all into irradiated fossils.

to house, store, and have the rapid ability to use large numbers of drones

Why? No, seriously, I want a white paper on this! I need the White House to issue a 40-page report on the urgent need for a 250-foot gold-topped arch that rests exactly under the flight path of commercial jets departing and arriving at the nearby international airport. I want them to cover, in depth, why it's not just fine to put a taller-than-allowed glitter arch under those planes but it is essential to also have swarms of drones launching into that airspace at any given time.

Think you've avoided the big golden whatever-it-is on top of the arch, commercial pilot? Ha! The joke's on you, because it also launches impossible-to-see drones according to whatever schedule the U.S. military, which is famous for its consideration of D.C. area pilots, wants to fk around with.

I want a press conference on this idea. I want to see scale models. I want to see AI renderings of this Triumphal Arch That Shoots Drones At You.

Jesus, White House press corps, this flippant half-baked bullshit is your whole reason for existing. Get some damn quotes.

Trump's own FAA, which probably means Sean Duffy during one of his breaks between Filming Content, gave brown nosing approval for the higher-than-allowed arch while noting that any changes from the previous design would require a new FAA review.

Which means now Sean Duffy has to steer the remains of his department into an explanation for why "and it launches drones" also doesn't impact the commercial airliners attempting to land at the National Airport. That's a level of improv comedy that even Andy Kaufman wouldn't have attempted.

plus Snipers, on both the roof and plaza areas, and additionally have and hold large quantities of sniper ammunition in storage

Spell it out, buckaroos! Now that you've said it, we have to know. According to Donald, the military made a "strong request" for a 250 foot gilded sniper's nest with a direct line of site to both Arlington National Cemetery and the Lincoln Memorial.

It's obvious why the military wants a sniper nest overlooking Arlington National Cemetery. They're afraid of zombies. They've watched a thousand different zombie movies, 800 of them while drunk (see: Pete Hegseth) and are now imagining any number of scenarios in which the soldiers buried at Arlington come back to not-life, burst out of the ground, and attack.

These wouldn't be just regular zombies. These would be zombies with military training. They'd know martial arts. They'd have a strong chain of command. They'd be able to read maps. Complete nightmare fuel, all of it, which is why Pete apparently put in an urgent middle-of-the-night phone call to Trump insisting that a 250-foot tall sniper tower be constructed nearby in order to pick off zombies and pink elephants as they emerge.

Presuming zombies do not attack, though, the main purpose of the sniper tower would be to have direct line-of-sight to the Lincoln Memorial. One of the most popular protest sites in the whole country.

Why does the military need a fortified sniper's nest overlooking one of the most popular public protest sites in the nation?

Why does the military need to "additionally hold large quantities of sniper ammunition in storage" in this position overlooking one of the most popular public protest sites in the nation?

Hey. Hey, every reporter in Washington. We want to know.

This is your beat. This is your bailiwick. I don't know what a bailiwick is, as far as I know it's a kind of candle, but whatever it is this is supposed to be your reason for having these jobs in the first place.

How about we all each take just one of our big important questions about which politicians do or do not denounce a Twitch streamer and instead use that time to ask the Donald J. Trump administration why they plan to construct a fortified Sniper Arch overlooking the Lincoln Memorial and fill it with "large quantities of sniper ammunition?"

It seems kinda fking important, to be honest.

Trump's game of insisting that he can do whatever he likes so long as he says "national security" somewhere alongside it has been tedious for a while, but you can't blame him for saying it because the Roberts Supreme Court insisted, straight-up, that presidents named Trump can now do absolutely anything they want so long as they say "national security" somewhere alongside it.

That's why Trump's Ballroom is now a Ballroom plus Drone Port. That's why Trump's Arch With Troop Sitting On Folding Chair had the folding chair guy.

But this? This appears to be a case of Trump's id seizing control over even his narcissism. The man flat-out announced that he was building a Murder Tower, gave no explanation whatsoever for just who he expects to see murdered, only that it's going to require a hell of a lot of ammunition, and we're supposed to just treat it as one of his usual brain-burps?

I ... kinda don't think so?

So let's hear it. C'mon, all you journalists who park yourself in Washington to do journalisming. Let's get to the bottom of this. We need to know, from the military, what the "strong" military rationale is for a sniper nest aimed at Arlington National Cemetery and the Lincoln Memorial. Donald said this was the military's idea, so you're up, Drunky Pete. Give us your scenarios here.

We all want to hear what "military" scenario exists that would see Washington D.C. overrun with foreign adversaries, presumably because they've outwitted or overwhelmed the U.S. Army, Navy, Air Force, Marines, and Space Force combined, and the only thing that stands between an invading let's say Canada and victory is a group of plucky military snipers with vast quantities of stored sniper ammo holed up in a Triumphal Arch above one particular D.C. road. Oh, and they have drones for some reason.

And somehow, in this scenario, the military foe who's managed to threaten the nation's capital wouldn't just drop a 2,000 bomb on the arch and solve their problem.

I don't often want to hear more about the things this dementia-cooked seditionist dreams up during Natalie Time but this one, I think we really need to hear the details on. Go on, then, let's hear it. I want to hear every detail of this new military installation that the Pentagon considers even more important than Pete Hegseth's morning erection screening program.

Go on. We need this. It'll be hilarious.

This essay was written with Cindy Cohn, and originally appeared in Lawfare.

One of the many legacies of the terrorist attacks of Sept. 11 is the government-wide shift from targeted surveillance—such as individual wiretaps or pen register/trap and trace orders—to mass surveillance techniques—such as tapping into the internet backbone or mass collection of telephone or internet metadata. The legal and technical architecture of modern mass surveillance, initially framed as a necessary defense against terrorist threats, has grown far beyond that justification and national security in general. Mass surveillance is now a routine tool used by law enforcement. ICE uses it in immigration actions and against people exercising their First Amendment rights to protest. It’s also increasingly part of private security systems, such as facial recognition at venues such as Madison Square Garden and networked Flock license plate capture systems on roads and in parking lots.

The interrelation between private and governmental mass surveillance is worth examining. Surveillance is the business model of the internet; companies like Google and Facebook constantly spy on their users’ behavior. From the National Security Agency relying on data collected by telecommunication and internet companies, to local sheriffs and ICE agents relying on cellphone location data and privately managed automatic license plate readers, governments primarily obtain the mass surveillance information through private companies. Increasingly, access doesn’t just come through legal processes, either. FBI Director Kash Patel recently confirmed in congressional testimony that the agency is purchasing information on Americans from data brokers and intends to continue to do so.

This pipeline from private collection to governmental collection means that as companies collect more information for surveillance capitalism purposes, more is available to law enforcement as well. And as the technology for mass surveillance and analysis improves, especially with the increased use of AI technologies, the problems attendant to mass surveillance grow as well.

After 9/11, the idea that the government could surveil the population to safety took hold. In 2001, the fear of terrorism reached a frequency and intensity never before seen. Along with that came the fear that the enemy could be anyone, anywhere. As a result, the government’s response was to watch everyone, everywhere. This line of reasoning underpinned the shift from targeted to mass surveillance. Or, in the words of an internal National Security Agency (NSA) presentation that was made public as part of Edward Snowden’s 2013 disclosures, a government that can “Collect it All,” “Process it All,” “Exploit it All,” “Partner it All,” and “Sniff it All,” will ultimately, “Know it All.” Similar rationales support the rise of domestic mass surveillance: if law enforcement could see and hear everything, it could more effectively interdict and solve serious crimes.

The national security community has never provided a full analysis of the costs and benefits of these mass surveillance programs, either in terms of taxpayer dollars or diversion of resources from other efforts—or any demonstration that those techniques stopped attacks that otherwise they would not have been able to prevent. While the NSA occasionally presents examples of the successes due to its mass surveillance programs, especially when those techniques are under public pressure, the examples also regularly fall apart upon serious scrutiny. And even if some utility exists, it must be seriously weighed against the costs.

Similarly, there has never been any comprehensive analysis about whether domestic immigration or law enforcement’s use of these techniques actually makes people safer, or whether other techniques could produce the same results. Instead, both the police and the companies selling these tools float anecdotes and dubious data. For example, Flock’s data equates the number of law enforcement hits in their database with actually solving crimes.

Twenty-five years after 9/11, it seems reasonable to step back and evaluate the costs of this shift to mass surveillance, especially in terms of Americans’ rights and freedoms.

The Shift

The easiest place to see a shift to mass surveillance was in the government’s decision immediately after 9/11 to collect Americans’ telephone records. The program started under an argument of pure executive power as the “President’s Surveillance Program.” But in 2006, that argument secretly shifted to a novel interpretation of Section 215 of the Patriot. Act which had only previously authorized more targeted access to record. While some media and public interest organizations struggled to force the government to reveal the program as early as late 2005, the government only officially confirmed it after the 2013 Snowden disclosures. In 2015, the Second Circuit Court of Appeals rejected the government’s interpretation of Section 215 as allowing mass collection of telephone records. Later the same year, Congress passed the USA Freedom Act. While this new law still allows collection of a tremendous amount of domestic telephone records, it ended the indiscriminate mass collection that had occurred for nearly fourteen years.

Other shifts to mass surveillance continue through today. The NSA launched its Upstream program, which involved intercepting both metadata and content from key telecommunications junctures inside the U.S., soon after 9/11. It was also initially conducted under a claim of purely presidential authority. This program was brought under marginal congressional and programmatic (not targeted) Foreign Intelligence Surveillance Act (FISA) court review via Section 702 of the 2008 FISA Amendments Act. In 2017, more than15 years after its inception, the NSA ended content searches due to FISA court pressure, but the mass collection continues.

Despite the stated goal of conducting mass spying only on people outside the U.S.—which itself is problematic given international law’s requirement that surveillance be both necessary and proportionate—mass surveillance collects a tremendous amount of U.S. persons’ communications. This can happen because people communicate with people abroad, or because of overcollection—when government agencies gather far more personal data on non-targeted US persons than authorized by law. The concerns about collecting Americans’ data on U.S. soil led Congress to allow the program to officially expire in 2026, although the previously-approved mass surveillance itself continues until at least Spring of 2027.

The shift to mass surveillance would be notable enough even if it remained only a strategy of the intelligence community. It has not. Americans are awash in mass surveillance. Networks of automated license plate readers such as those offered by Flock and Vigilant Solutions blanket both public and private roadways and parking lots. These networks often allow searches by law enforcement, including across jurisdictions. They are, for example, being used to track people seeking abortions across state lines. Facial recognition tools, once the province of only the more elite parts of federal law enforcement, are increasingly used by Immigration and Customs Enforcement agents on immigrants and protesters, in airports by the Transportation Security Administration, as well as by private entities. And, of course, modern phones track users’ locations constantly—and that information is readily available to law enforcement, often with only minimal process protections.

Constitutional Costs

Regardless of the murkiness of its actual usefulness, the shift from targeted to mass surveillance has profound implications for Americans’rights. It has created risks that have become increasingly evident, especially under the Trump administration.

At a basic level, the Fourth Amendment guarantees that citizens can be secure in their “persons, houses, papers and effects” from unreasonable searches. Warrants breaching that security should be supported by probable cause and particular descriptions of the place to be searched and items to be seized. Mass surveillance turns that promise on its head, allowing access to our “papers and effects” by the government without individualized suspicion or a particularized description of what data is being seized, much less probable cause. This protection was in response to colonial British misuse of writs of assistance, which authorized indiscriminate searches rather than targeted ones.

The justifications for exempting mass surveillance from constitutional protection vary. For Section 702, the government has taken the position that U.S. persons’ communications caught up in the dragnet, either due to overcollection or because they were communicating with someone outside the United States, do not require a warrant prior to initial collection or secondary access by the FBI and several other agencies. The argument is that if the initial collection was not aimed at Americans, the information is free from constitutional protection for any later uses, even for reasons far afield from the initial rationale for collection.

Other arguments rest on the claim that metadata is outside the Fourth Amendment, despite its demonstrated ability to reveal intimate details of all of our lives. Still others rest on the Supreme Court-created Third Party Doctrine, which holds that the Fourth Amendment does not apply to data shared with companies that provide us with services. Some turn on whether analysis by machine counts, claiming that only “human eyes” matter—a particularly troubling argument with the rise of artificial intelligence. What’s more, the government has used doctrines like standing to limit the ability of those subjected to mass surveillance to seek constitutional protection. No matter the argument, the goal is the same: to place the mechanisms and fruits of mass surveillance outside the protections of the Fourth Amendment.

The overarching truth is that, due to the concerted efforts by the government since 9/11, and the rise of technologies in recent years, the slice of Americans’ lives and data that are actually protected by the Fourth Amendment has shrunk significantly in the past 25 years. Together, with the technical capabilities of mass surveillance and the increased ability for that data to be analyzed using AI tools, the “security in our papers and effects” that the constitution promises seems increasingly illusory.

In addition to the Fourth Amendment, mass surveillance creates tensions with the First Amendment. The Constitution has long recognized that the right to freedom of speech requires a zone of privacy against governmental surveillance. The right to anonymous speech as well as the right of association both recognize the chilling effect that surveillance creates for people saying unpopular things or attempting to organize for political or other societal change. Mass surveillance grants the authorities the ability to track those people, both in real time and historically, that is inconsistent with actual techniques of freedom of speech and assembly.

That is why the recently released 2026 U.S. Counterterrorism Strategy is so troubling. On page seven, the White House expressly states that it intends to target domestic activists with its heretofore foreign-targeted powers. It says that the government “will prioritize the rapid identification and neutralization of violent secular political groups whose ideology is anti-American, radically pro-transgender and anarchist” and “will use all the tools constitutionally available to us to map them at home, identify their membership, map their ties to international organizations like Antifa.” While framed as targeting “violent” groups, it’s clear that the government intends to use its national security tools, presumably including the tools of mass surveillance, against Americans in ways that will create profound tensions with the First Amendment rights of people to organize and communicate privately.

Costs Due to Mistakes and Abuse

Even assuming some utility from mass surveillance—a fact we do not dispute, even if the public record is shaky and conclusory—the history of both the national security and domestic uses of mass surveillance confirms that these tools are inevitably misused, and that mistakes have impacted huge numbers of Americans. The past twenty-five years have demonstrated that it is not possible to surveil the entire US population while staying within the bounds of even a very generous legal framework like Section 702.

As Rep. Zoe Lofgren (D-Calif.) recently stated in discussion of Section 702 in an interview with Tech Policy Press: “backdoor searches have been used improperly for protestors, 19,000 campaign donors, members of Congress, journalists, government officials, a state court judge who had complained to the FBI about police misconduct. It has been abused substantially in the past.” The NSA experienced so much abuse of its mass surveillance tools by actual or aspiring romantic partners and ex-spouses that an internal name emerged for it: “LOVEINT,” or Love Intelligence.

That same pattern of abuse is now emerging at the domestic law enforcement level. A Texas police officer misused, and then lied about, using license plate readers to track a woman suspected of seeking an abortion. Multiple law enforcement officials have been accused of tracking people they either wished to have a relationship with or who were their exes. And mass surveillance technologies have been used to track both immigration targets and citizens engaging in their First Amendment-protected right to track and record the police.

Mistakes are inevitable with collections of data of this size and scope. The history of the FISA court’s reviews of Section 702 is littered with examples of the NSA not being able to follow its own rules limiting the scope of what it collects and analyzes, even after having been given multiple chances by the court. On the local level, the technical protections that Flock, for example, put in place have repeatedly been insufficient to stop “accidental” sharing its data with out-of-state law enforcement. These mistakes have fueled growing efforts by local communities across the country to remove license plate readers. Those efforts should be the first step in a broader reconsideration of mass surveillance.

More generally, ubiquitous surveillance carries a real societal cost. The chilling effects are real and pervasive, and they tend to fall hardest on the most marginalized members of society. Moreover, social progress requires the ability to experiment in secret. It’s hard to imagine a society progressing morally to the point of accepting and legalizing things like marijuana use or gay marriage if the earliest signs of that shift are snuffed out because of overzealous surveillance.

Reversing Course

While a cost-benefit analysis is not the best frame for deciding constitutional rights, it is a place to start to evaluate government policies. If the costs are too high and the benefits too small, what should the public do? While the policy and legal frameworks can be individually complex, mass surveillance is a problem in all of its applications. So too should solutions be comprehensive rather than piecemeal.

One comprehensive strategy is to reset the promise of the Fourth Amendment and recognize that a warrant is required prior to collection, access or use of information gathered through mass surveillance. This would apply to collections that include U.S. persons, whether done for national security or domestic purposes. This protection would apply regardless of whether the information is in the form of metadata. It would apply regardless of whether the information is held in homes or by services people rely on, such as telephones, internet or social network providers, or by private entities utilizing mass surveillance for their own purposes. By passing this legislation, Congress could ensure this rejection of mass surveillance, and include real enforcement such as a private right of action and an automatic exclusionary remedy in criminal prosecutions. The courts could also recognize this protection of “papers and effects” directly as a plain language interpretation of the Fourth Amendment.

There are already a number of efforts that take on pieces of mass surveillance. Section 702 has expired and should remain so. This was due largely to efforts to block the “back door” access to Section 702-collected data without warrants. The bipartisan “Fourth Amendment is Not for Sale Act” would prevent the government from purchasing data that it would otherwise need a warrant to obtain. The Supreme Court itself has already been chipping away at the Third Party Doctrine, with a recent step in the rejection of mass geofence warrants—warrants seeking the identities of individuals based upon their proximity to a crime—in Chatrie v. United States. Now, such warrants fall, at least initially, under the Fourth Amendment.

A more comprehensive approach would also address mass surveillance carried out by private companies, and to ensure that Americans have the right to encrypt and secure their data. There are many reasons the United States would benefit from a comprehensive privacy law—and curbing mass surveillance is one of them. Addressing mass surveillance is certainly one of them. Ideas such as the banning of secondary uses of data—with roots in the Fair Information Practice Principles from the 1970s—are worth pushing forward. So are moves such as creating fiduciary duties for mass data collectors. There are many more ways to curtail private companies’ mass surveillance while staying within constitutional boundaries. But addressing the costs of mass surveillance by both companies and governments is even more important in a world where AI agents are making decisions both about the public and on their behalf based on their data and observed behavior.

Twenty-five years after the U.S. government embraced mass surveillance, it’s time to evaluate it as a whole, and consider responses that address the problem as a whole. Americans must ask: Is it consistent with a self-governing democracy to have systems that watch everyone everywhere? Is the public comfortable with governments—federal, state, local—that seek to “know it all” about its citizens? Is the public comfortable with private mass surveillance in its own right and as it’s being increasingly used to fuel government surveillance? These questions have long needed serious consideration. But as it becomes increasingly evident that the Trump administration is using mass surveillance to keep itself in power, stifle dissent, and undermine political opponents, these questions are now more urgent than ever.