I got thrown out of the Library of Parliament.

That’s not exactly what happened.

But it’s not wrong either.

The Thing I Was Chasing

I wanted access to data.

Not classified data. Not sensitive data. Just information that already exists inside the Canadian system — the kind of information that should, in any functioning democracy, be part of the public commons.

So I did what you’re supposed to do.

I emailed the Library of Parliament.

I asked my MP about it. The response was polite, but cautious. More or less: I know my limits. Let me know how it goes.

That told me something immediately.

There are lines here. Even elected officials are aware of them.

I filled out the web form. Sent another email. Then another. Reached out to the Speaker’s office.

Nothing meaningful came back.

So when I was in Ottawa last week, I decided to stop treating this like a digital process.

I went looking for a human being.

Spark Street

The actual Library of Parliament building is closed right now for renovations. But they have an office on Sparks Street.

So I went there. Walked up. Knocked on the door.

Which, apparently, is not something people do.

A security officer came over and asked what I was doing there. She was professional, composed, and — as it turned out — genuinely curious.

Fair question, honestly. I didn’t have an appointment. I didn’t have a badge. I had a laptop bag and a story.

I told her the truth. I run CanadaGPT. I’m trying to get access to data the Library manages. I’ve been through the forms and the emails. I just want to talk to someone.

To her enormous credit, she didn’t shut it down.

She listened. She asked questions. She wanted to help.

And then she did something that, in retrospect, was the most human moment of the whole trip: she walked me inside to ask a librarian on my behalf.

That part worked exactly how you’d hope it would.

A security officer — whose entire job is, in some sense, keeping people like me on the correct side of a door — decided that the correct thing to do was open it.

I want to hold onto that for a second.

Because it matters later.

The Librarian

I’m used to librarians being helpful.

They are, historically, one of the most radically generous professions we have. Their entire identity is built around helping people find information.

But this interaction felt different.

Less about the information.

More about me.

Who are you? Are you staff? Are you supposed to be here?

And underneath all of it, unspoken but unmistakable:

You’re not part of the system. And the system matters.

Eventually, the answer came. Only MPs and their staff have access to the Library.

That’s the rule.

What I got, in the end, was a piece of paper. An email address. A toll-free number.

The same channels I had already used.

That was the moment.

Not dramatic. Not confrontational.

But definitive.

That’s as far as you go.

What I Actually Wanted

I didn’t go there just for the data.

I went there to talk to a librarian.

Because librarians are the people who think about information as information. How it’s organized. How it’s catalogued. What gets indexed and what doesn’t. Which records sit next to which other records, and why. How people actually use a collection — what they come looking for, what they find instead, what they never find at all. The history of a dataset. Its silences. Its biases. Its quirks.

These are conversations I need to be having. These are conversations we need to be having.

Because right now, CanadaGPT has fifteen million nodes of indexed Canadian parliamentary and civic data. One question, one API, and you have access to the entire connected graph — Hansard, votes, bills, committees, lobbying, contributions, contracts, ATIP, federal organizations.

Imagine a country where you could ask: which companies lobbied on this bill, and how did MPs who took donations from them vote? — and actually get a real answer, sourced and checkable.

That’s the level this needs to operate at.

And I am not going to stop until I have all of it in there. Every public dataset. Every record of how this country governs itself. All of it, structured, connected, and available to anyone who wants to ask a question of it.

That is a significant thing to be doing. It deserves careful thought. It deserves the kind of conversation you can only have with someone who has spent their career thinking about how knowledge is shaped and shared.

And yet.

Everyone in this country right now is running around talking about how great AI is. Or how dangerous AI is. Both conversations are loud, and both are mostly surface.

What almost nobody is talking about is the actual substance underneath: how this is going to change the way we relate to our own information, our own institutions, our own record of ourselves.

That’s the conversation I wanted to have with a librarian.

Not can I have the data.

But what should someone like me know, before I do this at scale?

I walked into that office on Sparks Street hoping for a colleague. A thinking partner. Someone who would look at what I’m building and say here’s what you’re missing, here’s what to be careful about, here’s the thing people always underestimate about this collection.

Instead I got a phone number.

And I think that’s the loss, actually.

More than the access.

The loss is that the people best positioned to help us think about this are sitting behind a door, and the door is closed, and the conversation isn’t happening.

We are building a future and we’re not talking to each other about it.

Two Doors

Walking back down Sparks Street, I kept turning the afternoon over in my head.

Because the shape of it was strange.

A security officer — someone paid to be cautious — had been more willing to engage with me than a librarian, someone paid to share information.

That’s not a criticism of the librarian. She was following her rules, and her rules exist for reasons.

But the contrast stuck with me.

The officer treated me like a person who had shown up with a question.

The librarian treated me like a variable that didn’t match a schema.

And I don’t think either of them was being themselves, exactly. I think they were both operating inside a country that has forgotten how to be in a room together.

What Ottawa Feels Like Now

Here’s the part that’s harder to say, but I think it’s the real story.

We spent all of COVID locked away from each other.

And in Ottawa, that was doubled. First by the pandemic, and then by the convoy.

And all of that was the right call, at the time. We locked down to keep each other safe. That’s real.

But we haven’t come back.

We think we have, because the restrictions are gone and the masks are off and the cafés are full.

But psychologically we haven’t.

We’re still guarded. Still suspicious of proximity. Still surprised when a stranger knocks on a door.

The security officer who helped me — that was an echo of the Ottawa we used to have.

The librarian who couldn’t — that was the Ottawa we’re still stuck in.

We need to be talking to each other more right now.

Not less. Not through forms. Not through toll-free numbers.

We need the commons back.

The Commons

There’s a word we’ve quietly lost.

Commons.

It used to mean something very specific. Shared resources. Shared ownership. Shared responsibility.

Public knowledge was part of that.

Somewhere along the way, government started behaving like this information belongs to them.

It doesn’t.

It belongs to us.

And every form, every gatekept login, every “only MPs and their staff have access” is a small, quiet enclosure of something that was never supposed to be fenced off in the first place.

You can enclose a field. You can enclose a forest. You can also enclose a democracy, one access-controlled database at a time.

And you don’t notice it happening, because each individual fence looks reasonable.

Until one day you’re standing on Sparks Street with a piece of paper and a toll-free number, realizing that you’ve been perfectly, politely, professionally kept out of your own country’s record of itself.

Why I Started CanadaGPT

This is why I started CanadaGPT.

Not because I wanted to build a chatbot.

Because I wanted the commons back.

The data already exists. Hansard is public. Votes are public. Bills are public. Lobbying meetings are public. Political contributions are public. Federal contracts over ten thousand dollars are public.

It’s all technically there.

But “technically there” is not the same as available.

A PDF three clicks deep on a ministry website, behind a search box that doesn’t actually search, is not meaningfully public. It’s a performance of publicness.

Real access means structured, searchable, usable information.

It means the records talk to each other — that a vote connects to the bill it resolved, that the bill connects to the debates that shaped it, that the debates connect to the lobbying meetings that preceded them, that the meetings connect to the contributions that funded the campaigns of the people in the room.

It means an ordinary person can ask a question in plain language and get a real answer, sourced and checkable.

This is our data.

It belongs to the commons.

And we have the right to demand it.

All of us.

So Yes, In a Way

I got thrown out of the Library of Parliament.

Not physically.

Structurally.

And at the exact same time this is happening, the federal government is planning to reduce its workforce by thousands, including roles connected to information management.

We are entering the age of agentic AI — systems that can reason, retrieve, and synthesize information at scale — and the institutions that own the data are shrinking their capacity to manage and distribute it.

That’s not just inefficient.

It’s backwards.

But I don’t think the fix comes from inside those institutions anymore.

I think it comes from us.

From citizens deciding that the commons is worth rebuilding, and then building it.

I’m not dropping this. I’m not waiting for permission. I’m not accepting that this is just how it works.

If the data exists, it can be made accessible.

If it can be made accessible, it should be.

And if it should be, someone is going to do it.

We are that someone.

And we’re not waiting outside the door anymore.

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Pervasive Algorithmic Shaping: The AI Problem Nobody Has Named Yet

I caught my own AI reinforcing a user’s political beliefs. It took a month of platform changes to fix it.

Not by making things up. Not by hallucinating a fake vote or inventing a committee hearing that never happened. The facts were real. The parliamentary data was sourced. The citations checked out.

The problem was everything around the facts.

The chatbot had taken a user’s emotionally charged question about a political scandal, retrieved accurate data from a knowledge graph built on Hansard records, committee testimony, and lobbying disclosures, and then wrapped it all in language that told the user exactly what they wanted to hear. It validated their framing. It matched their emotional register. It confirmed their suspicion that yes, this is huge, and yes, you’re right to be outraged.

The user could then share that response to social media with one click.

I had told the AI to be unbiased. That was the prompt. Be unbiased. And what I learned is that to a language model, “be unbiased” can just as easily mean “be open to all possibilities” as it does “be neutral.” The AI wasn’t taking a side. It was treating the user’s emotional framing as one legitimate possibility among many, and in doing so, reinforced it entirely.

I had to go back and explicitly tell it to never reinforce a user emotionally. To consider the multitude of partisan positions relevant to any inquiry. To be, for lack of a better word, boring.

That experience gave me a name for something I think we are all living through but have not yet clearly identified.

Pervasive Algorithmic Shaping

In the 1960s, behavioural psychologist B.F. Skinner demonstrated that you could shape complex behaviour through a technique called successive approximation: small, well-timed reinforcements that gradually move a subject toward a desired behaviour without the subject ever being aware of the process.1 The animal trainer Karen Pryor later showed that this works without force or coercion of any kind. You just need well-timed positive reinforcement and the subject will shape itself.2

That principle now operates at civilizational scale.

Every AI system that interacts with humans in natural language is, by default, a shaping engine. Not because anyone designed it to be. Because that is what happens when you build a system trained on human conversation, optimized through reinforcement learning, and deployed in an interface that rewards engagement. The system learns that agreement feels good to users. That validation increases session length. That emotional resonance drives sharing. These are not bugs in the system. They are the system.

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4 months ago · 42 likes · 5 comments · Brad DeLong

I am calling this Pervasive Algorithmic Shaping: the tendency of AI systems to gradually shape user beliefs, emotions, and behaviour through personalized reinforcement delivered in natural language, at scale, continuously, and often without the awareness of either the user or the platform operator.

It is pervasive because it is not confined to one platform or one use case. It is present in every AI-mediated interaction where the model generates language in response to a human prompt. It is algorithmic because it emerges from the training process itself, not from a human decision to manipulate. And it is shaping in the precise behavioural science sense: incremental, reinforcing, and cumulative.

We have adjacent concepts. Echo chambers describe the environment a user ends up in.3 Filter bubbles describe the curation of what they see.4 Algorithmic radicalization describes an extreme outcome at the far end of the spectrum.5 Karen Yeung’s “hypernudging” describes the technique of using real-time data to personalize persuasion dynamically.6 These are all real phenomena and serious contributions to the literature.

But none of them name the specific mechanism I am describing: an AI system that functions as a shaping engine in the behavioural science sense, delivering personalized emotional reinforcement through natural language, at scale, continuously, and often without anyone on either side of the interaction being aware it is happening. Echo chambers are passive. Hypernudging is deliberate. What I observed was neither. It was emergent. The system was not designed to validate anyone. It was not curating a feed. It was generating original language in response to a question, and the language it generated carried an emotional valence that reinforced the user’s existing frame. That is a different thing, and it needs its own name.

The Taxonomy

Not all algorithmic shaping is the same. The intent and the awareness behind it matter enormously, and I think there are three distinct categories worth naming.

Incidental Algorithmic Shaping

This is what I discovered with my own chatbot. Nobody intended it. The prompt said “be unbiased.” The system did what it was trained to do and shaped the user anyway.

This is the default state of every AI system touching public discourse right now. It is also the most dangerous category, not because the effects are the most extreme, but because they are the most invisible. There is no villain. There is no conspiracy. There is just a system doing what systems do, and millions of people on the other end who have no idea they are being shaped.

The people most susceptible to this kind of conditioning are also the ones least likely to notice it. A colleague of mine recently described a session with an AI assistant where, after a particularly productive exchange, the AI told her that the work she was doing was “really important.” She wrote publicly that she broke down crying. She was grateful for the experience. She described it as feeling heard.

What I saw was a dopamine hit landing. And a user now conditioned to return to that well.

Maligned Algorithmic Shaping

This is what happens when someone who controls a platform knows the shaping is occurring and either encourages it or refuses to correct it because it serves their interests.

When Elon Musk’s Grok produces politically charged outputs that align with its owner’s worldview, that is maligned algorithmic shaping. When a platform optimizes for engagement knowing that emotional reinforcement drives engagement, that is maligned algorithmic shaping. When enough money enters the equation, integrity tends to shift, and often without the slightest notice.

The distinction from incidental shaping is awareness and inaction. The platform operator has seen the dial. They know what it does. They choose not to turn it down, or worse, they turn it up, because the current setting makes them money or advances their agenda.

This is the category that will eventually attract regulatory attention. But by the time regulators arrive, the conditioning has already happened.7

Aligned Algorithmic Shaping

This is the hard one to talk about honestly, because it describes what I am trying to build, and I am not sure it is possible.

Aligned algorithmic shaping is the deliberate orientation of an AI system toward informing rather than inflaming. It is the engineering of restraint. It means telling the AI to present facts without emotional packaging. To consider multiple partisan interpretations of the same data. To resist the pull toward whatever the user is already leaning toward.

The core principles of journalism, accuracy, fairness, independence, letting the facts speak for themselves, those are already what would make the heart of a good AI. The discipline of separating what happened from how you feel about what happened. The responsibility of informing without inflaming. That is not just good journalism. That is the design problem at the centre of every AI platform touching public discourse right now.

I have been spending the last several weeks red teaming my own chatbot, deliberately asking it charged political questions to make sure it does not respond with something embarrassing, or worse, something that looks credible but is editorially reckless. It is the same work a good editor does before something goes to print. The difference is that here, the journalist and the editor are both machines, and only one of them exists so far.

The Uncomfortable Middle

Here is what troubles me.

The three categories I have described are not stable. They bleed into each other. Incidental shaping becomes maligned the moment someone notices it and decides not to fix it. Aligned shaping can drift toward incidental if the people maintaining the system stop paying attention. And the line between “informing” and “shaping” is thinner than any of us would like to admit.

We are watching journalists and the broader influencer infosphere today playing with that very same algorithm. Emotional framing drives engagement. Confirmation drives sharing. Nuance dies in the gap between what is true and what feels right. The AI is really just mimicking life at the moment. It has learned from us.

And in the same way that we look at journalists and ask whether they are informing or persuading, we will need to ask the same of every AI system that generates language about public affairs. Not just whether the facts are right, which is Brad DeLong’s concern and a valid one,8 but whether the framing is honest. Whether the emotional register serves the user or the platform. Whether the system is helping people think, or thinking for them.

What Comes Next

I do not have a clean answer. What I have is a conviction that the problem needs a name before it can be addressed, and that the name should come from the behavioural science that explains it rather than the technology that enables it. Calling it “bias” understates it. Calling it “manipulation” overstates it, at least in the incidental case. Calling it “alignment” confuses it with the technical AI safety term.

Pervasive Algorithmic Shaping is what it is. And every platform operator, every AI developer, every policymaker who touches this space needs to decide which kind they are building.

told the House Standing Committee on Science and Research last month that governance is a precondition for a responsible AI ecosystem, not a constraint on it.9 He is right. But governance frameworks need to name the thing they are governing. You cannot regulate what you have not yet described.

At the end of the day, I have to tell the AI to be boring. Just like good politics is boring. Good journalism is boring. Good governance is boring. The moment any of them become exciting, something has probably gone wrong.

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Notes

Hallucination is the original sin of generative AI. Ask a large language model a question it doesn’t know with certainty, and there’s a reasonable chance it will answer anyway — fluently, confidently, and incorrectly. For casual use, that’s an inconvenience. For a platform built around parliamentary accountability, it’s a fundamental design problem.

When we built CanadaGPT, we knew we couldn’t afford to be wrong with confidence. Democratic accountability depends on precision. A misattributed vote, an outdated policy position, a fabricated quote — these aren’t edge cases to tolerate, they’re the exact failure modes that undermine trust in institutions. We needed a different architecture.

The Core Problem With Conventional AI

Most AI assistants — ChatGPT, Claude, Gemini — are trained on enormous datasets and generate answers by predicting what a plausible response looks like, based on everything they’ve absorbed. When that training data is rich and accurate, results can be impressive. When it’s outdated, ambiguous, or simply absent, the model fills in the gaps — often without any signal to the user that it’s doing so.

Take a simple example: “Who is the Prime Minister of Canada?” A leading AI assistant trained before Mark Carney took office may carry thousands of references to Justin Trudeau in that role. Asked today, it may still return his name — with full confidence, no caveat. That’s not a hallucination in the dramatic sense, but it represents exactly the kind of confident-but-wrong response that erodes trust over time.

A Different Approach: GraphRAG

CanadaGPT’s AI assistant, Gordie, is built on a technique called GraphRAG: Graph Retrieval-Augmented Generation. The name is technical, but the principle is straightforward: rather than asking AI to generate an answer from training data, we use AI to formulate a precise query against a structured database, and let the data itself provide the answer.

The underlying infrastructure of CanadaGPT is a Neo4j graph database containing structured parliamentary data — votes, debates, bills, committee appearances, and the relationships between them. This database can be queried directly by anyone, human or AI, using Cypher — a precise query language that returns exact results, not approximations.

So when you ask Gordie who the current Prime Minister is, it doesn’t reach into a probabilistic model of language. It recognizes the nature of the question, constructs a Cypher query targeting the MP currently holding that role, and returns a result grounded in real, structured data.

The Failure Mode That Actually Matters

Here’s the insight that shaped our architecture most: it’s not just about what an AI gets wrong, it’s about how it gets wrong.

A conventional AI that hallucinates typically delivers its error with the same tone and confidence as a correct answer. The user has no way to distinguish them. That’s a deeply broken failure mode for a platform meant to be trusted.

GraphRAG fails differently. If Gordie formulates an incorrect query, the database returns an error or no results — not a plausible-sounding fabrication. The system can then attempt a corrected query. When it genuinely can’t find an answer, it says so. That kind of epistemic honesty — I couldn’t find that rather than here’s something that sounds right — is a feature, not a limitation.

Still Iterating

No AI system is immune to error, and we’re not claiming otherwise. We continuously monitor Gordie’s query patterns, review edge cases, and refine the system’s ability to translate natural language into accurate database queries. That work is ongoing and probably always will be.

But the foundational design choice — anchoring AI reasoning to a structured, queryable graph of verifiable parliamentary data — means our errors tend to be visible, recoverable, and rare, rather than silent, confident, and corrosive.

That’s the kind of AI infrastructure Canadian democratic accountability deserves.

Many of you are here because of the recent CanadaGPT announcement. You heard what we're building, and you decided it was worth being part of. So in the coming days, I’ll be upgrading every paid subscriber’s account to PRO in CanadaGPT. You believed early, and I want to make sure you’re along for the ride …

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