Identifying a good metaphor is a superpower that all good product marketers have. (See what I did there?) Being able to abstract a complex technical concept and find a metaphor or mental model that grounds it in something familiar is how we figure out how to talk about it. I’ve picked apart a bad one before, and I’ve sat in meetings where the metaphors were actively making things worse.

For the past few years, I’ve been trying to find the right metaphors for various AI concepts. Most recently, I’ve been thinking about how if the vast majority of content that is being created is AI-generated, at what point will there be no new raw material for AI to use? At what point will all its content just be built on its own generated content?

The first thing I learned is that there’s a word for this (“model collapse”) and a metaphor (“Habsburg AI,” coined by researcher Jathan Sadowski, referring to the European dynasty whose Spanish line produced offspring with progressively greater levels of genetic frailty due to inbreeding). There are differing opinions among AI researchers as to how much of a threat model collapse really is, but it is debated as part of the long-term impact AI will have on our society.

What’s interesting is that some researchers think that as long as AI-generated content is created alongside net-new human content, the threat is low. One paper found that if you keep the real data and pile synthetic content on top of it, rather than swapping one out for the other, the degradation has a ceiling it doesn’t climb past. Other researchers see it as a strong concern as the volume of new AI-generated content significantly outpaces that of new human-generated content.

I wanted a more thorough metaphor so I could form my own point of view.

A sourdough starter. The first metaphor I thought of was a sourdough starter (shout-out to 2020). You keep a jar of flour and water alive by feeding it forever, and every loaf comes from the last loaf’s starter. It never runs out, and over years it picks up the wild yeast in your ingredients and environment. But it fails as a model for content quality, because a starter has no standard it can fail to meet. Whatever yours becomes is, by definition, yours. It might get crustier or sweeter, but there’s no measure of if it gets worse.

Dollar-cost averaging. The next metaphor I thought of was this financial concept, where you buy the same amount of a stock or other instrument every month regardless of price, so that no single peak or valley in the market determines your outcome. At first blush it’s a good model, because its purpose is to smooth out variation over time. But it only describes the shape of the result. There’s no mechanism inside it to inspect, and nothing I can point at to say what would go wrong, or when.

A solera. I think I’ve landed on this one, which is a system describing how sherry is made. Imagine a structure of barrels stacked in rows by age. Each session, you take sherry out of the lowest barrels to bottle, and add new wine to the top. When you remove the sherry, that barrel refills from the row above, and that one from the row above it, all the way up. The bottom barrel is never emptied, only diluted, which means from year to year, the composition of a new bottle of sherry is similar. As a machine for cancelling variation, a magnificent year and a miserable year can taste the same in the glass.

Which brings me to the one part of the solera that doesn’t map to our content metaphor: the capataz. The capataz (“cellar master”) walks the rows of the bodega and tastes the barrels. One working capataz describes wearing out the soles of his shoes going through each solera again and again, tasting every cask of every style, marking them in chalk as his predecessors did.

Because theoretically, even though a solara is designed for consistency, over time it can drift. Ten years in a row of a different harvest will eventually show up in the glass, though it will happen gradually enough that no single bottle gives it away. The system can’t account for that, but a person tasting can notice it. The same is true for content. If we want AI-generated content to stay good, we need to keep creating, publishing and paying for new human work in enough volume that it’s still shaping what comes out. And someone has to be paying attention to whether it is.

Now here’s an uncomfortable part. Right now, with AEO and GEO, many of us are optimizing content for the LLMs that index it. We’re writing for what the model will pick up, cite and repeat. In effect, we’ve made the system its own capataz. The taster deciding what’s good is the same machine whose output we’re worried about. I understand why we’re doing it; it’s where buyers are starting to look. But I’m not sure it’s what we want. A solera works because the person tasting cares about what the wine should be.

A few months ago, a picture fell off the wall in the hallway outside our bedroom. It didn’t do much damage to the wall, and the glass didn’t break. I figured it would take about 10 minutes to put a new hook on the wall and re-hang it. So I put it between the bookcase and the staircase railing and penciled it in for the weekend.

But it stayed there, on the floor, for months.

I walked past it every day. Every single time, some small part of my brain filed a report: still haven’t done that. It was like feeling a little flick of failure, several times a day. When I finally rehung it, it took ten minutes. But before that, the ‘not-fixing’ took months.

Increasingly, I’m beginning to understand that procrastination is a subscription to guilt.

We talk about procrastination as a one-time cost that eventually comes due. Like in college, you think, “I am not going to write this paper all week, I will just stay up late the night before it’s due and do it then.” When we commit to procrastination, we think we’re committing to doing it later and under more pressure. But that’s not what actually happens for me. What happens to me is a charge that hits every time I open my to-do list. I scan the list, see the thing, feel the feeling that made me skip it last time, and then I skip it again. Nothing has been accomplished, but I’ve spent calories on it.

David Allen’s Getting Things Done time management system has a two-minute rule: if a task takes less than two minutes, don’t write it down, just do it. The logic is efficiency. Capturing a task, filing it, reviewing it, and eventually doing it costs more total effort than the two minutes the task actually requires. But there’s another benefit for me, because the overhead isn’t the only problem. The real problem for me is often that writing something down converts a one-time task into a recurring one. Not recurring in the sense that you have to do it repeatedly, but recurring in the sense that you have to see it repeatedly. Every review, every scan, every time you go to the list for some other reason, the task sits there charging rent.

For me, I’m usually avoiding tasks because of embarrassment. Something didn’t go the way I wanted, like I need to report out on a project that didn’t go well. Or I tried to get a raise or promotion for someone and failed, and I have to tell them. Or I’m avoiding it out of dread, like I know I have to circulate a document for feedback and I know I’m not going to like the feedback so I avoid sending it. Either way, the procrastination makes the task SO.MUCH.WORSE.

If I think of procrastination as a subscription, I haven’t yet figured out how to cancel it, but I have started noticing the charge. That’s not nothing, but it’s not the same as fixing it, either. I still avoid the hard email. I’m just more aware of what it’s costing me while I do.

The picture that fell off my wall is back up. It looks exactly like it did before. Next time maybe rehanging it can cost me less. Meanwhile I’ll put ‘unsubscribe’ on my to-do list.

I say please and thank you to Claude.

It’s not because I think AI will become our overlords and reward us humans for politeness, and it’s not because I want different responses from Claude. I just do it, the same way I thank the barista at Starbucks for my latte or my husband for going to the dry cleaner. Claude, please can you look at this. Thank you, that’s exactly what I needed.

I’m not alone. And people love to point out that those extra words cost real electricity, that I’m being courteous to what’s effectively a matrix multiplication. At worst, I’ve been told that this is what it looks like when a person gets confused about what’s on the other end of the conversation.

But I think something else is going on. We teach kids please and thank you as manners. Manners are a social contract that is entirely about the other person. You say thank you so the other person feels seen, acknowledged, or appreciated. We don’t tell kids that saying thank you to a vending machine is necessary or important.

But maybe we should. At my daughter’s Bat Mitzvah I referred to gratitude as “an ancient cheat code.” Every spiritual tradition I know of has a significant dose of gratitude built in. Many include practices that require people to demonstrate gratitude, even when they aren’t in the mood. Enter the social scientists. Robert Emmons, who has spent his career on this, defines gratitude as an affirmation of goodness plus a recognition that the source of that goodness sits outside yourself: something good happened, and I didn’t generate it.

So he’s run an experiment on more than a thousand people. He’s instructed them to keep a journal, write down a few things they’re grateful for each day, and do it for a few weeks. The results? The people who do it sleep better, exercise more, report fewer aches, and score higher on basically every measure of well-being.

I’m ok at practicing gratitude. (And it is a practice, so says Brené Brown and many others.) I have a journal that I write in every few days; I practice certain Jewish rituals; I occasionally do yoga. Perhaps thanking Claude is good for me in the same way writing in my journal is good for me. Or perhaps it’s just a reflex. Either way, it’s another observation around my interactions with AI that helps me ensure I’m not the frog boiling in the pot.

The other day I was talking to a colleague about the Bridges of Konigsberg math problem. I started getting into complexity theory and realized I was confused about some of the details of which pieces of the bridge problem were considered solvable in polynomial time. (Stick with me for one more paragraph, this isn’t a highly detailed math post, although I added a postscript to explain what I was looking up.)

So I asked Claude. And ended up in an exchange for about 10 minutes about all sorts of adjacent problems and concepts. It’s such a different way of “looking something up” than Googling something, or looking it up in a (gasp!) physical encyclopedia. And it has so many interesting ramifications for how we learn and how we think.

When I was a kid, we had reference books. You had a question, you went to the book, you checked the index (or thumbed through entries alphabetically) and found The Answer. In college, I started to use search engines. Suddenly, there were multiple answers easily accessible with the click of a button. I could read something, decide I didn’t understand it, or that it wasn’t exactly answering my question, hit the back button, and try another source.

Where we are now is in a world of what feels like infinite granularity and specificity in how we can get answers to questions. When I go to Claude to find the answer to something, I am doing a lot more than typing a query into a search box. I can zoom in on exactly what I don’t understand or want to know more about. I can customize my learning and understanding from my own perspective, and not waste time on what I already know about.

When I was in grad school, we took an assessment loosely based on the VAK learning styles: Visual, Auditory, and Kinesthetic. My mental model understood those as “learn it yourself from a book” “ask others for help” or “dive in and try it.” Lacking nuance, to be sure, but what I learned about myself was that I relied almost entirely on “learn it yourself from a book” while everyone else was asking each other for help all the time. I made a conscious effort to try and incorporate the other modalities into my learning process, and continue to deliberately do that even now.

What I’ve been reflecting on of late is how the way I’ve been using Claude is a hybrid model – it’s part “learn from a book” but part “ask someone.” While it lacks the Auditory component present in the original model, in some ways it’s a bridge between uni-directional reading and interactive learning that gets me at least some of the benefits of working with others. It forces me to ask good questions, not just passively ingest information. (‘Ask Good Questions‘ is one of my father’s most enduring pieces of advice.) And it vastly lowers the barrier to entry for asking someone: the discomfort (sometimes even shame) of needing to ask for help, especially when it’s something you either think you should know or have asked in the past already. It gets you reps on asking for help that don’t cost anything, other than tokens. 🙂

But it does miss one of the major benefits of “ask someone.” The act of asking someone to teach you something you don’t know or don’t understand is a relationship-building experience. Going up to a colleague and saying, “I think I should understand this but I don’t, I need you” is expressing the type of vulnerability that builds trust between people. They know you won’t walk around pretending to know things, you know they will spare you a few minutes to help you because they think it’s worth it. You appreciate them. They appreciate you.

It also distances us further from the source of the information. Which, in this era of mis- and dis-information can be risky. Now, a person can also tell you what they know without citing a source, and you may trust them. But you generally know their motives, like if they want your job or they want your boss to like them. And even published news sources have recognized biases: I know what I’m getting when I read a piece from the New York Times Editorial staff. What we don’t know when we talk with an AI assistant is what their bias is, or what the biases are in their sources, because that is already abstracted from us. For math, probably fine. For politics, less so.

Claude and the other AI assistants are drastically changing society. Just the other day I heard an interview with an author who writes hundreds of books a year using AI. And an AI-based curriculum that is drastically (and foolishly?) shortening kids’ school days. Those are extreme, and their value to society is up for debate. But I don’t think all the changes are bad, or even debatable. I’m just trying to stay aware of how it’s changing my behavior and my processing of information.

Now, back to the math:
The classic Konigsberg bridge problem describes a city with two riverbanks and two islands, all connected by seven bridges. The question is whether you can cross every bridge exactly once. And the answer is no. What’s interesting is that for any set of land masses and bridges, it’s trivial to figure out if the answer to the question is yes or no by envisioning the configuration as a graph and counting how many edges come out of each node. So it’s considered a P problem, not an NP problem.

Relatedly, however, is the question of if you can visit every landmass exactly once. Funny enough though this question is almost the same, getting its answer is significantly harder. In fact, there’s no shortcut to finding a solution to this type of question that is significantly faster than brute force, which makes it an NP problem.

I learned something about flounder last week. To defend against predators, they use chromatophores (tiny sacs of pigment in their skin) to match the color of what they’re lying on. In fact, it’s not just the color, but the texture too.

Everyone is writing about how AI is disrupting product management. Roadmaps, prioritization, spec-writing, even coding itself: there’s a think piece a day. Nobody is writing about how AI is disrupting product marketing. Not because it isn’t. But because PMM has never been one fixed job that a headline writer could point at and say, “that’s what changed.”

Ask ten people what demand gen or PR is responsible for, at ten different companies, and you’ll get ten recognizably similar responses. Ask ten people what a product marketer does, and you’ll get ten different jobs. At one company, messaging. At another, messaging plus competitive intel plus pricing plus half of sales enablement. At a third, positioning, or campaign strategy. I’ve done this job at different-sized companies, in differently mature markets, at different levels of seniority, and I’ve never done the same job twice.

Part of the reason for this is nascency. Modern product marketing traces back to the tech boom of the mid-1980s, which makes it less than 50 years old. Brand marketing, by contrast, has been a defined discipline since the P&G memo in 1931, twice PMM’s age. It’s hard to identify disruption in a discipline that’s still defining itself.

When I started, product marketing was mostly translation: take the technology, explain the value, build the feature/benefit matrix (although I always hated those and one day I killed ours.) There was always pressure to “launch” new products and capabilities, and a friendly friction with product around what deserved a launch and why. Then it became about also understanding buyers: personas, buying committees, understanding that a “customer” was actually a room full of different people with different pains and goals. And the position, meaning how did we define the market we were in and what differentiated role we were playing in it. Then it became about the campaigns around the message itself: what are we putting into market, who’s delivering it, who’s receiving it, how do we know it worked. Each era added responsibilities to PMM teams, stretching the role across functions with increasing tautness.

This AI era that we’re in is not only adding complexity. It’s compressing time and multiplying scale in ways the earlier shifts didn’t. Personas that used to take months now take hours, as long as you have rich source material (like transcripts and engagement data) that can be continually farmed for buyer preferences and behaviors. We can generate hundreds of tailored message variants, for hundreds of accounts, with one push of a button. And perhaps most disruptively, we’re not only messaging to humans anymore. Value and capability now need to be digestable by agents. (Hi, Claude.)

Meanwhile, product is shipping value-rich releases daily, which means the quarter-long, carefully sequenced messaging campaigns I used to plan are becoming as dated as rotary phones. You know, the kind plugged into the wall without buttons? You don’t know?

PMM is being just as disrupted by AI as everyone else. It’s a quieter disruption because of our ability to adjust to our surroundings. The Sheryl who started working in Product Marketing in the ’00’s would hardly recognize what we do today. And I doubt I would recognize where we will be as soon as a year from now.

My kids both play instruments.  My daughter piano and cello, and my son piano and viola. In addition to their in-school and private lesson instruction, they each practice both instruments several times a week.

They also both play sports. My daughter tennis, and my son – well, its easier to list the sports he doesn’t play (shotput.) They each practice several days a week.

My own pastimes veer less physical. I have an affinity towards puzzles of all kinds. I have gotten good at all of these from years and years of practice working on them.

There is really no alternative to practicing to get better at something.  Much better.  Whether you believe in the 10,000 hour theory or not, nobody becomes exceptional by mailing it in. 

I learned this again when I attended a workshop at work last week.  On the first day, a high-energy and friendly colleague walked in to facilitate. I knew her socially but we hadn’t worked together much. Shameful admission: for the first ten minutes, I underestimated her.  She had several (what seemed like) kitschy ice breakers and activities for us to participate in though the two-day workshop. She was armed with whiteboards and Post-its and strict rules about Sharpie markers. 

About 10 minutes in, I had been completely engaged. As someone who regularly presents to large groups that aren’t always friendly, I was floored.  Her ability to draw in the group, and create an environment where we were immediately collaborative and productive was not like anything I had ever seen.

I had to knowhow she got so good at this. At the first break, I walked right up to her and said, “You are so good at this! How did you learn to do this?” She thanked me, demurring. But I pressed on, “No really, how did you learn this?” 

Her answer surprised me, thought it shouldn’t have. “Practice.  Lots of practice.” It didn’t even occur to me that someone could get good at that skillset through practice, rather than have innate talent or transferable skills from a different domain, (like performing arts.) 

Practice. Wow.

I love puzzles of all kinds. I love jigsaw puzzles, crossword puzzles, and sudokus, and logic puzzles. I don’t just love jigsaw puzzles, I love big ones with lots of pieces;  I don’t just love crossword puzzles, I recently learned how to do (American) cryptics;  I don’t just love sudoku, I love sudoku variants, like knights-move.  My latest obsession (shared with my 10-year-old daughter) is a set of puzzles created by Japanese publisher Nikoli.

And one thing I’ve been thinking a lot about lately is how to start. The thing is – “breaking in” to a puzzle is the key to getting enough momentum to start solving and having fun, but it’s a different skill than actual mid-way solving.

This year, we had some major water damage in our home.  I became BFFs with my plumber and electrician, and one thing I appreciated about both of them was their approach to troubleshooting and problem solving.  Once the demo was done and it was time to rebuild everything, our electrician came by with his crew.

I was completely overwhelmed and panicked.  “Where do you even start?” I asked him. He smiled. This is a man who has texted me line diagrams in the middle of the night. He knows how I think. “We start with the wiring.  We take it down and see what kind of shape it’s in.” I loved how confident he was, and how with experience comes an instinct of where to start.

There are some confident rules I have like that around puzzle types I know.

• With a classic crossword puzzle, I look for something I can answer on the top row or down the left side.  That gives me the largest surface area of first letters to start with. 
• With a Fillomino, I look for spaces around the 1’s, because those are filled with the same number as one of the other adjacent spaces.
• Last night, I tried a Matsu – we learned that white circles on edges were good places to start.  (Go ahead – give it a try)
• I’ve been really enjoying the “Fog of War“ sudoku puzzles, and find that where to start is much simpler with them, because you have a lot less information in a more concentrated area. 

Sometimes this fails. Miserably.  My rule for jigsaw puzzles has always been to start with the edges and then work my way in.  I also am a masochist of sorts and complete jigsaw puzzles without looking at the picture.  So when I got going on this puzzle (a wonderful gift from my folks), starting with edges didn’t work, and it took me a while to understand why not.

So I am thinking about how this can be applicable to work. Where can we “break in” to a problem?  How do we develop an instinct or skill around where to start with problem solving?   And how do we understand what to do when our instinct or system fails because the ground beneath us changes?

First off, if you’re expecting a post about confidence coming from the metaphorical amplification of your voice into a metaphorical microphone, this is not that post.

This is quite literally about using a microphone.

Easy question: what goes through your mind when you are in a large room, and some person gets up front and says, “I’m good without the mic – you can all hear me, right?”  

Well, for about 10% of the population (myself included), it’s dread.  It’s dread that I’m about to spend 5-20 exhausting minutes straining to understand what is said.

I don’t know if it’s laziness, machismo, anxiety, or time constraints that cause people to wave away a microphone when it’s offered. But the thing is, it happens all the time. That is, someone sets up, configures, and probably pays for amplification.  All the speaker has to do is use it, and they decline.

Do you realize when you do this that some of us *quite literally* can’t hear you?  We may have hearing loss from an accident, from aging, from birth, or from illness.  We may have issues with auditory processing. We may have hearing impairment that is temporary (like from swimmer’s ear) or permanent.  We may rely on lip reading to help, which is challenging in an age of masking, or if the speaker is far away.

We may have hearing difficulties that are exacerbated by circumstances, such as crowd chatter or the hum of an HVAC system. It’s like driving in a city with tall buildings that block your GPS. You know it will come back on (i.e., the HVAC will turn back off again), but until it does, you’re grasping and aimless and may not be heading towards your destination.

You may indeed be a person with a loud voice who can project.  What you may not know is that hearing loss can vary across higher frequency and lower frequency registers – which means someone with a low or high voice trying to project ‘louder’ may not help, particularly because their voice may change register as they try to amplify their voice.  It’s also likely that the volume a speaker can start at isn’t sustainable (unless they are, for example, a professional opera singer. Pro tip: ask yourself, “am I a professional opera singer?”) 

What might start out as hear-able in a quiet room becomes more difficult as background noise rises (you do cross and uncross your legs after a few minutes, right? and grab a mint, or zip your backpack, or mutter something to whomever is sitting next to you, or take a note, or type something…), and the speaker’s voice gradually lowers in volume.    

What does it feel like to not be able to hear a speaker addressing the room? You know when you’re in a big group and someone in the audience asks a question and everyone shouts to the speaker, “can you repeat that?” It feels like that, for the whole time.  You may catch the gist of what was said or asked, you may catch a few words, you may have a sense of what’s going on. But you can’t actually hear it.

Here’s the thing. I expect this to happen at a restaurant or a cocktail party.  I can tell within about 30 seconds of walking into an event or dinner whether I will be able hear all night or not. Often I can’t. And if I can’t, I am disappointed but not surprised. But when it’s a meeting, a religious service, a conference session, or a town hall-style event, I have an expectation that there will be amplification and that I will be able to hear.

So the next time someone hands you a microphone and you’re inclined to wave it away because you “have a loud voice,” think twice.  I know wires can curb your TED Talk-style stage wandering ability, and that all kinds of mics can create awful feedback that nobody appreciates. It can be frustrating in the moment to take a minute and resolve the technical issue rather than just volunteer to use your “loud voice.” 

Do it anyway. There are people in the audience who will appreciate it.

As my team has gotten larger, my decisions around transparency have become more complicated.  Really this is a meta-post, because it’s likely some of my team members will read this and what’s more transparent than sharing my complex relationship to transparency.  Inception indeed.

I’ve noticed two kinds of transparency – information transparency and what I’ll call subjective transparency. 

Information transparency is how we make decisions about who learns what information.  When do we or don’t we share information about what the budget will be, whether quotas will be higher or lower, who in the organization is getting promoted, what the OKRs will be. 

I find information transparency pretty easy to manage.  I share as much as I am allowed to share as soon as I can.  While OKRs may not be final or rolled out to the entire organization, I share what I know with my leaders.  While we may not know the specifics of how something will be executed, I share what I do know with a broad audience. 

The other type of transparency is where the complexity comes in.  What I’m calling subjective transparency is how much of what I think and how I interpret things I share.  This is where I need to decide whether to share how I feel about a certain goal or organizational change; what my interpretation of the information is.

“I wanted that role and didn’t get it and I’m disappointed,” “I don’t agree with a focus on that,” “I think the new person they put in charge of that is finally the right person, but they have a lot to undo.”  And positive ones too: “He’s going to be a great leader,” “I really like this new process for approvals,” “This product strategy is the right decision.” 

This kind of transparency requires much more vulnerability and risk. My feelings and opinions are respected on the team, so if I’m sharing that I don’t believe in something or don’t like something, I’m basically setting the team up to follow.  And if I’m sharing positive sentiment I may be setting unreasonably positive expectations for the team. So why do I do it?

Trust.

I think a key reason people want transparency from their leaders is that it builds trust. Sharing quotas ahead of time might be appreciated, but it’s not risky.  Sharing that the quotas aren’t what I wanted to see but they are what they are, can be an empathetic choice.  Sharing that I don’t know whether an executive leaving is a good or bad thing brings me closer to my team by opening the door to discussions. It signals that I’m not an automaton towing the line but that I am interested in opinions, interpretations, and expressions of emotion.  By showing my leaders that I can dislike a choice while still liking my job, I give them the option to do the same.

Let’s be real: It’s not about sharing every opinion about every decision with every person.  That would be exhausting.  Also ineffective.  But balancing where I do and don’t do it is something I’m learning.  And the more I experiment and take risks around this, the better my relationships get.

Someone once taught me that the job of an executive is to perpetually project momentum.  I think it’s more complicated than that.  Indeed leaders need to drive momentum, but to do it, and only it, perpetually, can’t be right. 

Brainstorming has a bad reputation. Is there a way to make it more useful?

I first learned about the concept of Expansive vs Reductive thinking from Duncan Wardle, the former head of Innovation and Creativity at Disney.  (Career goals, #amirite?)  Duncan presented at our company’s annual conference in 2020, and held a private workshop for our team members.

The idea is simple: Expansive thinking is massive open-ended brainstorming.  How big can you think?  How can you encourage the free association that generates great ideas? How can you get past the “no reflex?”   

Reductive thinking is narrowing down a wild list of options. What won’t work because of resource constraints, market conditions, or bad timing?  What ideas have promise but need structuring or grooming?  And what ideas rise to the top? 

Separately, these are each recipes for bad brainstorming.  Expansive thinking can get impractical, and reductive thinking can be too constraining.  But used together, they create a structure that enables a diverse team to find great ideas collaboratively. 

I’ve tried this a few times with a few different teams.  (In fact, the first time it was wisely suggested by one of the leaders on my team.) And the best way to do it is in two separate meetings.  First, you have an expansive meeting.  Everyone contributes all their ideas of nearly any quality.  There’s no editing, no blamestorming, and no limitations.

Then you have a separate reductive meeting, ideally a few days later.  You remind everyone of the problem you are all trying to solve, or the goal you are all trying to achieve.  This meeting is about narrowing down the ideas to one or more that pass a “sniff test” the group agrees on. 

The beauty of separating these two phases out is that you immediately remove the anxiety that plagues the planners and the skeptics in the group.  You can say things like, “we have a meeting next week to validate this can actually be done, but for now, let’s just write it down,” and “that’s ok, we may not actually do this, but let’s capture it for now.” By giving their instincts and anxieties a place to go (the meeting next week), you make space for the dreamers and visionaries.

Similarly, when it comes down to brass tacks, you have already let those visionaries air their ideas.  Now, the thing we all know about visionaries is that they never run out of ideas – so you can say things like, “that would be cool – but today we’re narrowing down last week’s list of ideas,” and “you contributed so much last week, let’s see if we can execute some of those ideas first.”  

Different teams have different challenges with brainstorming, but this approach seems to have solved a few of ours.