On October 29, Reddit’s leadership announced plans to transition the site into a primarily logged-in experience, starting with initial rollouts in a few markets outside the US. Under the new setup, if you click through from a search engine or a direct link, you’ll still be able to read that specific thread - but for almost any other browsing, you’re going to hit a login wall.

A few different things are driving this: 

1. The first is obvious: scraping. Reddit’s view is that AI labs have been vacuuming up its community discussions at scale for training data without permission or compensation; as a result, they’re currently suing a handful of AI companies while also cutting official data-licensing deals with Google and OpenAI. This makes sense; just because a forum is publicly readable by humans on the web doesn't mean you’ve automatically consented to unlimited bulk scraping, commercial distillation, or eating the server bills for millions of headless browser requests. A community can want its knowledge to be discoverable by people while still enforcing boundaries around how that data is harvested and monetized.
2. The second force is a drop in top-of-funnel search traffic. Steve Huffman told shareholders that in October, Reddit saw over a million fewer search referrals per week - a drop leadership largely attributes to users getting answers directly in AI search summaries without ever clicking a link.
3. And third, of course, is the ad engine. As Huffman noted, logged-in users give you a much richer profile for ad targeting.

When you stack those three variables together, the strategy makes total sense. If the top-of-funnel search hose is drying up because LLMs are intercepting the queries, Reddit has a massive incentive to lock in a direct relationship with the users who do still show up. Forcing account creation lets you retain, profile, and monetize each visitor much more effectively.

Adding a login wall introduces friction, and you’ll lose some casual drive-by traffic; but Reddit is betting that the smaller, authenticated cohort on the other side of that wall is worth more to the business than a massive crowd of anonymous lurkers.

You don’t need some grand philosophical take on whether AI is good or bad for the open web to understand this move. You just have to look at how Reddit makes its money.

It helps to follow a basic search query

Your dishwasher is making a strange noise, and you want to know why. So you search for the model number, and you find a discussion where someone had the same problem three years ago. A helpful stranger identified the failing part, someone else posted a repair guide, and pointed out that you should check the filter before buying anything.

You clean the filter, and the noise stops.

If you step back, this is a pretty cool (and pretty remarkable) process: people who have never met you, and probably never will, just saved you a precious afternoon, and your conversation together became a small piece of public infrastructure.

For a long time, the economics around that interaction were straightforward:

1. You asked Google a question. 
2. Google sent you to Reddit. 
3. Reddit showed you the discussion and some ads. 

You got your answer, the community got a reader and possibly a new member, and Reddit got an opportunity to earn revenue. One click served several interests at once.

AI search changes the sequence. You ask the question; a system reads or retrieves information from the discussion, produces an answer, and presents it directly. You clean the filter on your dishwasher, and the noise stops just the same.

From your perspective, the interaction may have improved - after all, you reached the useful information faster and with fewer steps and less friction.

From Reddit’s perspective, an important step disappeared: you never loaded the page.

The knowledge was useful, and the problem was solved, but the event Reddit could monetize didn't happen. That’s the tension Reddit is trying to solve this week, but it’s not a tension that troubles us at Discourse. 

The same answer can have a different economic value

Now imagine a similar query, and a software company’s public support forum.

A developer hits some obscure config error at 11 p.m. Six months ago, someone else hit that same wall and worked through the fix in a public Discourse thread. So the developer pastes the stack trace into an AI assistant. The model retrieves the thread, synthesizes the fix, and drops a citation link at the bottom. The developer changes the config line, the build goes green, and they get back to work, without ever actually clicking the link.

Now ask yourself: was that interaction a failure?

If your business model is serving ads, then yes - a missing click means a lost impression.

But if you’re the software company running that forum, that outcome is 100% a win. Your user is unblocked in thirty seconds, your support queue has one fewer ticket, and a piece of debugging work documented six months ago is still compounding in value today.

The raw pageview no longer took place, but the actual utility survived intact.

That’s the distinction we care about at Discourse. Our customers pay us for infrastructure to run healthy knowledge platforms, and what "success" looks like depends entirely on what each organization needs. For a SaaS company, that means solving support tickets and unblocking users. For an open-source project, they need to get devs past the initial friction so they adopt the library and eventually submit PRs. For a professional guild, they need high-signal knowledge sharing.

Pageviews are often a nice proxy for those outcomes, but they aren't the objective function itself. If you Goodhart this solely on traffic metrics, you end up looking at a developer who just got their problem solved in ten seconds and logging it as a loss - just because the text rendered inside a chat window instead of a browser tab. That feels like optimizing for the wrong metric entirely.

An answer is only one output of a community

It’s tempting to oversimplify and say: "Look, if the final answer reached the user's eyeballs, the system worked." Sometimes that’s true! But a real community does more than serve static, pre-computed answers.

An LLM summary is great at extracting and handing you the terminal command, but the human discussion holds the surrounding reasoning and the caveats. And more importantly - somebody still has to generate the next discussion when the stack changes.

A live thread is where someone chimes in six months later to say, "Hey, this accepted solution actually breaks in v2.4." It’s where a follow-up question exposes a broken assumption in the original setup. It’s where a maintainer jumps in and says, "Wait a second, why are you even trying to do it this way?" (the classic XY problem).

A thread about a gnarly config bug might have a one-line bash command that makes the error go away. But wrapped around that one-liner is all the high-dimensional context: why the system behaves that way, which versions are affected, and the exact edge cases where running that command will silently corrupt your state.

This is why source attribution and live links are still critical. They act as a pointer from the compressed, lossy summary back to the full context and the actual humans who figured it out.

A lot of people will just grab the snippet, paste it into their terminal, and bounce. That’s totally fine - drive-by copy-pasting has been the default on the web forever.

A subset of people will hit an edge case and need to go deeper. They’ll click through to read the whole thread, ask a clarifying question, post an updated workaround for the newest release, or spot an adjacent question they actually know how to answer. Those are the exact interactions that keep the community's training data evergreen and actually useful.

So when we think about how AI discovery interfaces with the web, asking "Did it send us raw traffic?" is way too narrow. The real evaluation metrics are much deeper: Did it actually unblock the user? Did it represent the thread's nuance faithfully without hallucinating? Did it keep the original source visible? And did it leave a clean, low-friction bridge for the people who needed to step into the actual community?

Those are a lot closer to the true objective function we should be optimizing for.

Openness has more than one setting

We also need to be way more precise about what we actually mean when we say a platform is "open to AI."

Right now, people tend to lump a bunch of orthogonal questions into one bucket:

• Can a human read a public thread without hitting an auth wall?
• Can a traditional search crawler index it?
• Can an AI search tool pull it into context at inference time via RAG and cite it in an answer?
• Can a frontier lab scrape the entire historical database to dump into a pre-training run?
• Who actually has the root permissions to decide which threads are public in the first place?

Wanting an AI assistant to retrieve and cite your public support docs at runtime doesn't mean you’re cool with bulk-scraping bots hammering your origin servers for pre-training data. Conversely, locking down private channels doesn't mean a community is anti-discovery.

At Discourse, the core principle is that the people actually running the community should own those permissions. Some communities want their entire knowledge base completely open to the public web; others are strictly private; and a lot of them run a hybrid topology: public-facing categories for docs and user support, right alongside permissioned, private spaces for paying members or internal team deliberations.

Our job is simply to build the primitives that let each community configure those boundaries however they want.

Who owns the tradeoff?

The trickier structural problem for Reddit is that the corporate entity and the individual subreddits aren't actually optimizing for the same loss function.

Reddit’s revenue comes from ads, but the volunteer moderators and top contributors don't get a cut of that ad spend. They’re optimizing for completely different rewards: solving a hard problem, getting peer recognition, curating a high-signal space, and having new people show up with interesting questions.

For the last fifteen years, traditional Google search referrals acted as a neat coupling mechanism that aligned both sides. More search traffic meant more ad impressions for Reddit HQ, while simultaneously feeding fresh eyeballs and active participants into the subreddits. Everyone won together.

AI search unbundles that equation.

Now, an LLM can extract and distribute a contributor’s domain expertise to the world with zero ad impressions generated for Reddit. At the same time, it also lowers the conversion rate of a passive reader clicking through and becoming an active community contributor.

Whether a specific subreddit views that as a net positive or a net negative depends entirely on the details: how well the source gets cited, how much nuance gets lost in the compression, and whether the people who really care still find their way back to the thread.

There’s zero reason to think every moderator on the site would make the exact same call on that tradeoff.

The problem is that Reddit has to push a single, global platform policy across hundreds of thousands of wildly different communities, and that policy has to be dictated by the unit economics of its ad business.

On Discourse, that control is decentralized down to the individual community operator. Because the people running the community are the ones paying us directly, each instance gets to tune its own hyperparameters - deciding whether maximum open discovery, strict privacy, gated membership, or some custom blend makes the most sense for their specific context.

Of course, our incentives aren't magically frictionless either - a B2B software business still has plenty of its own operational constraints. But fundamentally, our revenue model doesn't depend on wedging an ad unit between a user's question and the community's answer.

That difference gives us the freedom to optimize for a completely different outcome.

Build for now + next

We want public communities to actually capture the upside of the AI shift. At the same time, we want the humans doing the real compute - the people asking the hard questions, testing workarounds, fixing broken assumptions, and moderating the noise - to stay visible, credited, and reachable.

The core principles are pretty simple: the answer should be free to travel, the source should keep its attribution, and the community should have root control over its own data boundaries.

Reddit’s login wall makes total sense given the physics of Reddit’s business model. And our approach makes total sense given our physics. Advertisers pay Reddit to rent eyeballs; customers pay us for infrastructure that makes their communities run better.

If you want your community’s knowledge to be easily discoverable across Google, AI assistants, or whatever interface comes next, we want to build the best stack for that. And if you’re currently moderating a subreddit and trying to figure out what this new landscape means for your community, we’d genuinely love to talk to you.