Context isn't the data sitting around a person. It's the weaving of that information together to create understanding of their situation. Co-Hosts Chris Heuer and Marius Ciortea discuss why community managers have always been context engineers, and why AI doesn't improve the relationship — it makes the context that does affordable.

Key takeaways

• Context is a weave, not just the data. Contexere: con, together; texere, to weave. The data was always in the building. What was missing was the weaving.
• AI doesn't improve the relationship. It makes the context that improves the relationship affordable. Thirty minutes of assembling it by hand becomes a brief that arrives before you reply.
• Community managers have always been context engineers. Naming groups, choosing categories, matching people by attribute — that is context engineering, twenty years before the term.
• Four records, one human. The goal is to see the member, not the four views of them through four systems.
• The job has four steps: aggregate, process, understand, act. Understand is where the human stays.
• It's a permission failure, not an information failure. You don't need a data lake or a golden record. You need read access — and MCP servers are how that access is becoming table stakes.
• The information we have about a member is held in trust. Members hand a community their history, expecting the company to receive it as one company.
• Start with what you can reach. Whiteboard the data by type, not by tool. Circle what you have access to today. Earn the second bucket by showing value.
• The cheapest win is telling the AI where the documentation lives. Ten recorded interviews with internal experts is the next one.
• Draft with the assistant, then don't hit the easy button. Read it, put it in your voice, and check that it isn't bloated, off, or wrong.

Time & Chapter

00:00 Cold open — four strangers who are the same person
00:50 Welcome, and the Anthropic "pace the frontier" news
04:39 The double-edged sword: $30,000 and 68 days, now three hours
06:28 Three C's, Sea-Doo, and why context is king
07:55 Situatedness, and the etymology of context
09:35 "I've been a context engineer for 20 years"
11:34 The assistant thesis: AI makes context affordable
12:15 Palm's three hats, and the weaving job
14:35 AI doesn't improve the relationship; context does
15:15 A word from Higher Logic
16:03 Marius's daily brief — what it knows before you reply
21:18 Systems thinking, MCP servers, and table stakes
24:11 Aggregate, process, understand, act
25:57 Ten interviews and the expertise knowledge graph
29:01 Access, not a data lake
30:33 Trust, disclosure, and nine hours with AT&T
32:00 Three ways to act: prepared, prompted, drafted — and the easy button
36:43 The whiteboard exercise: two buckets
39:13 Tell the AI where the documentation lives
40:35 Your own personal intelligence system, and next week's tease
42:37 Close

Bloated, off, or wrong. Chris Heuer and Marius Ciortea build a working test for AI slop, then go looking for its edges: the deep expert who cannot write in English, the member who prompts an AI to make him look good, and the community manager who wants a bot to answer every question. What is at stake is trust.

TC Chapter

00:00 Cold open: human slop, and why we're talking about it

00:25 Welcome

01:37 The pilot correction

02:18 Preferring AI answers, not trusting them

02:35 What's at stake is trust

03:19 What is AI slop? Too long to find the useful part

04:07 Bloated, off, or wrong, and the easy button

05:08 Can AI writing not be slop?

05:16 Code is poetry; slop is in the eye of the beholder

06:39 The Druckenmiller op-ed: written by AI

08:55 The PR-firm defense

09:17 Your personal intelligence system

12:22 Where AI writing helps a community: the expert who can't write

14:04 Where it breaks: "make me look good"

14:42 Low effort, low quality, and intent: the LinkedIn problem

16:10 Communities self-police

17:51 "I failed English": provenance, process, intent

19:17 The verification loop

20:44 The evidence under the answer

21:43 Show your work

22:05 The 15-minute conversation was the prompt

22:50 The 8,000-word wall of text

~23:40 Where is AI actually best deployed in a community?

24:00 Sponsor MidRoll - Higher Logic

24:47 Accessibility, enablement, discoverability

25:53 Accessibility runs both ways

28:38 Bad answers proliferate

28:52 The community managers who refuse AI

29:33 "I want a bot that answers all questions." Then what do you have the community for?

31:41 LinkedIn's slop button

33:08 The leaderboard, flipped

33:59 Monday: automated moderation and the Work Map

34:49 Write your community's AI policy, with your community

37:36 Community managers are enablers

39:20 Human-written, AI-backed

40:31 What is a community for? Answers or connection?

41:16 Uniquely human: built on experience

42:28 Revisit your mission, your values, and take your stand

~44:30 Set what the garden should look like

Key takeaways

• Slop is not merely "written with AI." It is bloated, off, or wrong, and usually reached for as an easy button. Define it for your community and say so publicly.
• AI writing helps the member who is an expert and a poor writer, or writing in a second language. It breaks the moment the prompt is "make me look good" on a question the poster can't answer.
• Communities self-police, because recognition is why people contribute. Negative feedback on slop either drives the poster out or makes them rewrite to be heard.
• Trust now requires provenance, process, and intent. Readers can't tell from the text alone, so show the evidence under the answer.
• If an AI can answer, let it, and send the member into the community when it can't. That enriches the community with new questions instead of repeats.
• Accessibility runs both ways: AI reading the community feeds support, product, and sales. A wrong answer in the community proliferates through all of them.
• Human-written and AI-backed keeps trust. AI-written with human backing loses it.
• Monday: write the AI policy with your team and your executive, run the draft past the community before it becomes terms, and template the removal message so it's kind and fast.
• Communities exist for the questions AI can't answer: "I'm in this situation, do you agree?" Experience is not replicable.
• The same failure produces human slop: a person stopped deciding. Our own pilot is the worked example.

Community managers spent a decade proving their value in page views and then in engagement. Those numbers are going the wrong way now, and they aren't coming back.

Marius Ciortea thinks that's an opening rather than a loss. In this episode, he makes the case that generative engine optimization is a community job: SEO lived in marketing, and GEO will live in or next to the community team, because your community is usually the only user-generated content your brand actually owns and can influence.

He and host Chris Heuer work through what that means for the person with a queue of unanswered posts and a metrics deck due Friday. How AI systems read a community thread. Which signals make an answer legible to a model: recognized author authority, employee and support attribution, community endorsement, answers marked correct, content that isn't stale. Why the highest-leverage work here isn't a file format but the practice of recognizing authority and retiring outdated material. And what you put in front of your executives when traffic stops being the story.

Chris also introduces the Work Map, an open inventory of the jobs community managers actually do, and identifies GEO as a gap it doesn't yet cover. That one will be getting built in public, with your help.

If the only way you justify your existence has been traffic, you're already in trouble. This episode is about the other way.

Sponsored by Higher Logic and supported by Inflection Point.

Chapters

00:00 Cold open: GEO belongs to community
00:57 Welcome, and why this show exists
03:01 Amplification, not replacement
04:32 Underfunding and the business case problem
05:48 Bots now outnumber humans on the web
07:14 The support question that never reached the forum
08:12 How AI reads a community thread
09:51 Authority signals: badges, staff attribution, upvotes
12:00 Temporality, llms.txt, and ontologies
13:17 The prediction: do the community work, not the file format
15:23 What replaces traffic as the metric
16:26 "If traffic is your only ROI, you're already in trouble"
16:49 Citations, bot-versus-human data, and GEO dashboards
18:11 Why GEO belongs to the community team
20:28 Attribution, and the ad model that's coming
21:34 The Work Map, and owning its gaps
23:51 Why Marius is doing this
26:47 "I've always been lazy": outcome-driven automation
28:54 Translation, humans don't scale, and the reframe
33:09 AI slop, and why the brand doesn't care
35:15 Close

Key takeaways

GEO is a credibility opening for community teams. Community is usually the only user-generated content a brand owns and can influence, which makes the community team the natural home for it.

The signals that make an answer legible to a model are things community managers already control: who answered, their standing, whether the community endorsed it, whether it was marked correct, and whether it's current.

The highest-leverage GEO work is not a technical file. It is about recognizing authority, consistently marking correct answers, and retiring stale content.

More of the web is now crawled by bots than browsed by humans, which breaks the page-views story community managers have been telling executives.

Citation and bot-traffic data is starting to appear in platform back ends, which gives community managers a replacement metric for the traffic they're losing.

Automation in community is not new. Profanity filtering, unanswered-question queues, and translation each moved work off the plate. AI is the next turn of that wheel, not a break from it.

The brand's interest is that the member got a correct answer and can use the product. Insisting that the answer come from the community is the wrong hill to die on.