When we announced our first speakers back in July, Noah said this year’s show would do two things: keep spotlighting how the best marketers are actually putting AI to work, and look outward at what brand building becomes when consumer preference runs through a model.

The final agenda does both. You’ll hear from creative leaders at Amazon, Microsoft AI and BARK showing real work, operators from Hilton, Rocket Money and Block on wiring AI into how their teams function, and a run of sessions on discovery, attribution, and what happens when the customer on the other end is an agent.

The Details:

• Location: The Times Center, 242 W 41st St, New York, NY 10036

• Date: Thursday, November 5

• Doors: 9am, programming starts at 9:45am

Tickets are still available, but with only a handful of open seats in the room, they won’t be for long.

GET YOUR TICKET

The Speakers

Full agenda here:

• Noah Brier - The AI Brain

• Mike Houston & Jo Shoesmith - Fusing Creativity and AI

• Jamie Umpherson & Gabor Petlyanszki - Scaling Brand Creative with Runway

• Mikkel Holm - Deliberately Imperfect

• Jesse Vincent interviewed by Shareen Pathak

• Tom Critchlow - The New Architecture of Choice

• Justin Inman - Emberos: AI in Practice

• Noah Brier in conversation with Mark D’Arcy

• Andy Crossen - From AI Visibility to Verified Economic Value

• Aaron Dignan - Making AI Part of How Work Gets Done

• Danelle Williams & James Gross - Putting AI to Work for the People Who Run Your Stay

• Michael Beebe & Paul Woolmington - Inside the Dstillery & Canvas Workflow

• Amy Winther - Codifying a Brand for Agents

• Ranjan Roy - The Product is No Longer a Product

• Pip Bingemann - Artificial Stupidity: A New Frontier in Creative AI

• Justin McCarthy - Demo: Ambition by Diffusion

• Noah Brier, James Gross & Mark D’Arcy - Reflections on the Day

With Thanks to Our Sponsors

We said it in September and we’ll say it again: we’re incredibly grateful to our sponsors for all of their support. What we appreciate most is that every one of them is bringing real work to the stage, which is about as BRXND as it gets.

• Runway (Apex Sponsor): The applied AI research company building frontier video generation models right here in New York. Chief Creative Officer Jamie Umpherson takes the stage with Meta’s Gabor Petlyanszki to show how brands are scaling creative.

• Emberos (Presenting Sponsor): The operating system for AI brand management. Founder and CEO Justin Inman shows how teams can understand, shape and measure how AI represents their brands.

• VantagePoint (Presenting Sponsor): Partnerize’s measurement and economic governance layer for machine-mediated commerce. If you’ve ever thought “I know half my marketing spend influences ChatGPT, I just don’t know which half,” Chief Product Officer Andy Crossen’s session, “From AI Visibility to Verified Economic Value,” is for you.

• Dstillery (Supporting Sponsor): The predictive audience company, and a returning BRXND sponsor. CEO Michael Beebe joins Canvas Worldwide’s Paul Woolmington to walk through a real campaign-optimization workflow.

• WRITER (Supporting Sponsor): The enterprise AI agent platform for marketing teams, and another company building models in our backyard. Ranjan Roy brings a lightning talk on why agents don’t shop the shelf, and what that means for how brands structure their product data.

Thanks for all of the support, and we look forward to seeing you on the 5th.

*There might still be a couple of last minute additions to the lineup :)

-Noah & Mike

GET YOUR TICKET

Of course, if you don’t yet have a ticket to BRXND to see Aaron and the rest of our speakers on stage, time is running out. Would love to see you next week.

Buy BRXND Ticket Here

Aaron Dignan, VP of Product & AI at Rocket Money and author of Brave New Work.

Aaron has spent two decades helping organizations change without calcifying. He founded The Ready, wrote Brave New Work, built Plumb, and now leads AI and digital product work at Rocket Money. That cornucopia of experiences gives him a rare view across human organization design, agent workflows, and the hybrid systems now taking shape inside operating companies.

To set the stage for his talk at BRXND, here’s the full forward deployed episode he recorded with Noah alongside my quick recap of the greatest hits.

1. An agent stack is an org chart. Before Rocket Money, Aaron built an AI automation platform called Plumb, where one of the problems he took on was getting a system to produce writing that actually sounded like him— the eternal struggle against slop that Noah and I have written much about. As he mapped out the steps and handoffs, he realized he had accidentally rebuilt something very familiar.

“oh, shit, I’ve just created a newsroom… I’m basically doing role work, which is what I spent most of my career doing.”

Role work is the org designer’s craft: writing down what a role is for, what it’s responsible for, and what it’s allowed to decide, instead of leaving all of that implicit. Aaron’s claim is that an agent needs exactly the same document, and that a system prompt is simply where it now lives.

“it’s all the stuff that would make a team healthy needs to be present in the diagram.”

The upgrade is that this version actually gets read meticulously which….just might be more than you can say for the last job description you wrote.

2. Restricting an agent’s tools is setting its decision rights. In a multi-step agent system, each step can be handed to its own narrow helper — a subagent — and you choose exactly which tools that helper can reach. Aaron had been doing this for performance reasons and had never once called it governance, until Noah asked whether he used decision rights explicitly with agents.

“that subagent might only have access to two tools instead of 50. And that’s actually a form of decision rights.” … “Like it can’t go update the GitHub because it doesn’t have access.”

Every tool you decline to hand an agent is a decision you have quietly taken off its plate. He is refreshingly frank about the downside of the fashionable alternative, in which you give one all-powerful agent the keys to everything and hope.

“we’ll just have one agent who’s like an orchestrator and they’ll have access to everything. And we’ll just roll the non-deterministic dice and hope that it all works out.”

3. The bottleneck moved from making to agreeing what should be made. Aaron’s group is roughly 300 people inside a 20,000-person public company. They’ve built a loop where unhappy customer text threads are caught by sentiment analysis, a second agent works out what actually went wrong, and a third drafts the fix before any human sees it.

The list of things they could ship got very long very fast, a microcosm for the core challenge of leading a product org in the era of AI.

“So much of the conversations now resolve to communications discussions rather than actually structural discussions, mainly because the bottlenecks have moved.”

“the real problem is if everybody goes and does everything at that pace, the coordination tax is the problem…….if in the past we could only ship three features in a month… and now we could ship nine… you just ship a product that feels like very disjointed”

4. Make your strategy meetings more indulgent. The standup, the weekly update, the where-are-we recap: this is the manual, repetitive work humans do worst. When the real work already runs through systems that log it, those updates can be a byproduct of the work rather than a meeting about the work.

“Everything runs on exhaust.”

The point of clawing back those hours is to spend them on the judgment calls nothing else can make.

“our strategy meeting now can be like a multi-day affair and it should be quite indulgent so that we like really do the things that the models can’t do””

5. Decide what gets argued and what simply gets measured. Human organizations run on persuasion — the pitch, the readout, the room. Models fold the moment you lean on them, and Aaron doesn’t expect post-training to fix it.

“there’s a beauty and a fallibility to the fact that human intelligence is very motivated by persuasion” … “if you try to persuade the model, it’s pretty persuadable… they’re going to be somewhat sycophantic forever, I think.”

So route each decision deliberately. Some questions deserve an argument; others should just be looked up. His example is a retention debate at a subscription business, where a leader’s instinct about why customers are leaving can now be checked against every recorded reason they actually gave.

“where do I want models persuading humans or humans persuading models and where do I want the math?”

“we have 20,000 data points now of like why people are actually left. And we can much more easily query reality” … “there’s a chance to check yourself a bit and still have your reality distortion field.”

I love Aaron’s framing here. He now has two different kinds of intelligence to arrange in an org chart instead of one, and the trick is knowing which to reach for: “now I just got a Phillips head screwdriver that goes right next to my flathead.” His parting advice is to keep track of what you picked up the screwdriver to build in the first place.

“Let’s make sure we don’t just get lost in the theater of productivity on the way to who knows where and remember what team we’re on.”

Forward Deployed

Forward Deployed, Episode 9: Brave New Work for Agents

Aaron Dignan joins me to talk about organizing AI agents, decision rights, coordination bottlenecks, voice interfaces, persuasion, and what human organizations can still teach us…

Listen now

3 months ago · 6 likes · 1 comment · Noah Brier

Links and References in the Episode.

Aaron and his work

• Aaron Dignan

• Aaron Dignan at BRXND

• Brave New Work

• The Ready

• Rocket Money

• AI & The New Average

Organization design and agent systems

• Everyone is a manager

• The 80-to-99-percent workflow gap

• Introducing Time-Oriented Software Development

• ElevenLabs Conversational AI

• Team Human by Douglas Rushkoff

50% off VISIONS for BRXND Readers (from our friends at Future Commerce)

AI is rewriting commerce. Culture is next.

Join Future Commerce for VISIONS Summit NYC on February 10, 2027, at The Museum of Modern Art — a gathering of leaders across commerce, marketing, design, and technology exploring how AI is changing what we create, how we shop, and what we value.

The lineup includes former U.S. Secretary of Commerce and Kellogg CEO Carlos Gutierrez, Sheehan Quirke (The Cultural Tutor), and speakers from Etsy, Hermès, MoMA Design Store, and more. Expect a day of ideas and conversations, with museum admission and an afterparty included.

On a personal note, I’ve been to VISIONS and I find it be a spiritual sibling of BRXND that pushes the boundaries of the discussion beyond cliche conference speak and gathers the best minds in commerce.

BRXND readers can use BRXND50 at checkout.

Explore the lineup and register for a ticket here.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

Of course, if you don’t yet have a ticket to BRXND to see Tom and the rest of our speakers on stage, the big button below can fix that.

We’ll officially announce our full speaker lineup and the agenda early next week but you might notice a few new faces if you head over to our homepage……

Buy BRXND Ticket Here

Should your brand have an MCP?

Should your brand have an MCP?

This sounds like a technical question at first, but actually belongs in the boardroom. It’s really a question of whether you’re willing to let your brand be disintermediated by AI… Will you cede the interface to the model?

This is the real “GEO” or “AI search” question brands should be asking - not “how often are we cited” but “does our brand help customers complete tasks”. This is the billion dollar existential question for brands in the AI age - will we survive, or will we become an invisible MCP call.

A year ago, Brian Balfour wrote The Next Great Distribution Shift:

“But I believe we’re on the precipice of something bigger. The next great distribution shift is coming, and it’s going to reshape how products find users just as dramatically as search engines and app stores did before it.

When this shift happens—and the early signals suggest it’s already beginning—there will be extraordinary windows of opportunity. There will also be casualties.”

I think we now know what that distribution shift and opportunity is: MCPs.

• Atlassian is up to 15m MCP calls / day (more actually) - up from 5m in July

• Resend is 3x growth from Aug -> Sep

• Monday.com 2x from Jul -> Sep (35m)

• Mercury “exponential MCP growth” according to the CEO

But! this presents a bargain as old as time. As Brian says - extraordinary opportunities, but also casualties. To open up your brand to a 3rd party or to lock down the walls and control distribution. Different brands are making different calls on this right now. Just this morning Walmart opened up their MCP, while Amazon continues to block agents:

“Walmart is embracing agentic commerce with the launch of its own Agent Gateway to manage transactions and other new features aimed at customers using AI agents, the retailer announced on Wednesday. The move comes shortly after Walmart’s top competitor, Amazon, began blocking AI agents from making purchases on behalf of online shoppers.”

This same story is playing out across industries - Notion is all in on MCP while Figma is being cautious, Booking.com has an MCP while Airbnb does not…

This is fundamentally a question of how much claim over the customer you have and how much preference your brand has… And this isn’t static! As markets and technologies move this changes and evolves. An illustrative example for hotel chains:

To MCP or not to MCP that is the question. This week on the Alephic podcast Sam and I break it down. As you might expect, I’ve got some strong opinions:

1. Mass adoption of harnesses and assistants is opening up the dawn of a new GTM paradigm as big as the internet or the app store.

2. Every brand should be building an MCP, even if they don’t release it publicly yet, so they can understand how the interface works.

3. Agentic assistants doesn’t mean prices go down - for differentiated services prices will go up as consumers reveal richer preferences

4. Intermediaries aren’t going away. But they might be massively disrupted. Shout out to this great paper from 1995 on the rise of “cybermediaries”. Remember we thought that the internet would remove intermediaries, but in fact that didn’t happen!

5. MCPs are not neutral! The UX of the MCP matters (really AX - agentic experience). The way you design it changes how agents and consumers interact with it. You can merchandise inside an MCP with related offers, special offers, loyalty and more.

6. We’re going to see an explosion of MCP-first businesses (spoiler: I’m building one inside Alephic for a client right now) and the rise of agents as consumers will lead to new kinds of platforms (what happens when G2 reviews are written by agents and the volume is 1000x what it is today?)

Ultimately there’s a generational shift in distribution and attention happening. But it’s a double edged sword for brands and the way through isn’t always clear.

To watch my full discussion with Sam on Youtube, head on over here!

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

Of course, if you don’t yet have a ticket to BRXND to see Jesse and the rest of our speakers on stage, time is running out. We are likely to sell out late next week. Snag your ticket today or drop me a line at mike@brxnd.ai if you have any questions about the show.

Buy BRXND Ticket Here

Jesse Vincent, creator of Superpowers and founder of Prime Radiant.

At its core, Forward Deployed is a publication and podcast exploring the intersection of AI, software development, and the enterprise from the point of view of practitioners who are building with these technologies. While the conversations are inherently technical, I find immense value in each episode as a generalist who is mostly integrating AI into non-development work.

Jesse’s episode in particular is chock full of wisdom on how to work with AI for both trained developers and vibers alike. See below for the full episode and five takeaways still living rent free in my head.

1. The job has always been outcomes. Asked who thrives with agents, Jesse names managers, product people and subject-matter experts — people good at clear writing and at thinking through consequences. The ones who struggle are those whose identity is the artifact rather than the result.

For marketers, simply swap “lines of code” for deck, asset, or campaign.

“The folks who don’t seem to do well are engineers who believe that their job is lines of code because the job has always been outcomes.”

2. Stop reaching for the biggest model. Frontier models overengineer, run slow and cost more. For most work, a cheap, fast model is best. Jesse is a fan of DeepSeek 4.1 Flash while Noah (and most of our team) is Luna-piled.

“I would argue that you don’t want the best available model on the planet for most of your engineering tasks.”

Jesse’s analogy for managing frontier models is also the best I’ve heard.

“it feels a lot like working with staffing agencies… They score better on all of the tests. But they’re a little bit quirky and there’s some things you need to know about managing them.”

For nontechnical users on a premium Codex or ChatGPT Work plan, it’s very tempting to just set the harness to Astra and continually use a blowtorch to light a cigarette. But even for non-coding tasks, Astra is best utlilized as a quarterback and planner. To actually do the work, let Luna cook.

3. The system’s first job is interrogating the human. Superpowers doesn’t start by building; it starts by cross-examining you about what you actually want. Jesse reinvented the creative brief, then automated the part where someone has to ask the hard questions.

“If an agent doesn’t understand your intent, they can’t do the thing you want. They will do the thing they guess you want.”

“the requirements as stated are never really what needs to happen”

4. Give your agent a professional identity with a resume: Jesse’s go-to-market agent, Nora, DM’d him unprompted about a conference CFP that had already closed, named the one person known to take late submissions, and had the outreach drafted. Then the catch:

“that is strictly better than a bunch of the marketing folks I’ve worked with”

“this was before I realized the mistake I had made by telling it that this was its first job and it was a junior marketing person”

Once he rewrote Nora’s role as a senior marketer, the scale of her ambition and altitude of her work moved recalibrated to the new role.

The same lever works on voice and is perhaps the best tool in your arsenal to reduce pedantic slop. Simply tell your the agent that before it got its CS master’s from Stanford it got a BA in journalism from Wesleyan.

“It caused the agent to start writing in reverse pyramid. It caused it to drop a lot of jargon. It went for short, simple declarative sentences. It led with facts.”

Funny enough, a colleague of Jesse’s tried it, left out the words “in journalism,” and got nothing back. I guess my journalism degree ain’t so useless after all dad.

Author’s Note: As Jesse suspected on the podcast, Wesleyan doesn’t actually offer a BA in Journalism. I should also note that I have a bachelor’s of SCIENCE in Journalism from Northwestern. When I want to truly be insufferable, I make it clear that I’m a scientist.
To make matters worse, my wife has an art history degree. A truly employable pair.

5. Your reviewer will find infinite problems and can’t rank them. Point an AI brand-compliance checker at a campaign and it will flag forever; deciding which flags are worth addressing is becoming more and more of the job.

“They’ve gotten increasingly good at spotting bugs. And what they haven’t gotten good at is the judgment of whether a bug matters.”

“it doesn’t have the judgment to say this race condition you found that you’re about to write 10,000 lines of machinery to fix. Would result in a 50 millisecond visual glitch in an admin screen, that’s okay.”

Jesse’s one hard limit is worth ending on. He’ll hand almost anything to an agent, but not safety-critical, health, or security work without a human reading it. This principle generalizes well past code: “you are responsible for the things you make.”

His test for whether you’re managing agents well is the one he’d apply to any junior hire — “the answer is never, I’m going to go off and go on vacation and not give anybody direction and not make anybody prove that it works.”

Everything that ships under your brand is still yours.

Forward Deployed

Forward Deployed, Episode 12: The Job Has Always Been Outcomes

Jesse Vincent joins me to talk about Superpowers, the software development process he built for coding agents, and what he’s learning from working alongside AI colleagues at Prime Radiant…

Listen now

8 days ago · 1 like · 1 comment · Noah Brier

Links and References in the Episode.

Jesse and his work

• Jesse Vincent’s blog

• Prime Radiant

• Superpowers

• Superpowers on GitHub

• Keyboardio

Stories and related conversations

• Today at work: an AI-led blameless postmortem

• A quick trip to the uncanny valley

• Forward Deployed, Episode 8: The Factory Has To Prove It Works

Reading recommendations

• The Mythical Man-Month by Frederick P. Brooks Jr.

• Maneki Neko by Bruce Sterling.

On Marketing to Machines

While we’re on the pod train, I called my own number this week with our friend Sam Knight for a wide ranging interview on agentic commerce and the nascent art of advertising to agents.

If much of this sounds familiar, it’s largely because this is effectively a portmanteau of several essays I’ve written here over the past few months. But hey, writing is a grind. Sometime’s it’s nice to just get to yap a bit.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

But first BRXND NYC is now officially three weeks out and we have limited tickets remaining. Thanks again to Runway, Emberos, VantagePoint, Dstillery and WRITER for supporting the show. Can’t wait to see many of you there- we will have a sold out show so grab a ticket today before we have to move back to a waitlist.

Buy BRXND Ticket Here

On AI & the future of work

1. As the models, harnesses, and general UX of AI improves, the alpha that comes from mastering the more idiosyncratic aspects of working with AI (prompting, skills, model routing, etc.) will trend toward zero. Essentially, everything the AI guru-industrial complex espouses will become less valuable. The alpha of being generally AI-pilled, but not dogmatic about any one tool and able to imbue AI work with original, contrarian thought, will only increase. — Mike

2. AI is very good at continuing past the point where it should stop. It will happily add another abstraction, another paragraph, another ticket, another “improvement.” Knowing what not to build, not to say, or not to preserve is becoming more valuable, not less. — Claire

3. We’re approaching a point where we can accomplish more in a day than we can cognitively absorb. I’m not sure what happens to our experience of work when we can no longer fully process what we’ve accomplished. — Joh

4. Burnout is going to multiply significantly in the AI era. As AI automates more of the redundant tasks that used to serve as a brainless mental reset, we’re spending more and more cognitive energy every day. This is unsustainable in ways that aren’t obvious yet. As a result, shorter, more intense stints and “reset breaks” will become far more common and normalized, first in tech careers and then more broadly. That will challenge a lot of our current labor infrastructure. — Mike

5. Not all friction is bad. Sometimes the work you’re trying to automate away is what makes you better at your job. Other times, the process itself is part of the fun, like digging through records instead of getting an instant curated playlist. Whether removing friction helps depends on what you’re trying to get out of it.— Joh

On building with AI

6. Coding agents are good at so much more than web apps. Think hard about where you can put the working part of what you’re trying to create: CLI, server, plugins, Apple Watch, phones, other agents. — Aaron

7. SaaS (or “managed applications/APIs”) as we know it today is eventually going away. It will transition to “headless” interfaces that LLMs integrate into applications they build on the fly. The transition will take longer in enterprise and corporate environments, but it’s already starting at the consumer level. — Ian

8. The fact that so many more people can build software now magnifies the tension between individual ownership of outcomes and collective ownership of systems. Within an organization, how do we decide what should scale, who owns it when others start depending on it, and how to introduce necessary rigor without slowing down the people building? I don’t think we’ve figured out how to reconcile those things yet. — Joh

9. Time to market is more compressed than ever, but I think people’s patience to find “the good one” will increase in proportion to the number of apps, products, and things being shoveled out there. — Aaron

10. We don’t talk enough about how good the video models are getting. I think in early 2027 we’ll see feature-length, fully AI-generated movies break out into mainstream success, not exactly because they’re AI, but because they’re just good. I’m really excited about lowering the bar to creative production— Tom

On the vibes around AI

10. The last 10 years of “AI-powered” marketing BS has ruined the definition and perception of AI for the general public. — Charles

11. Social media is doing AI a great disservice. My TikTok feed is filled with anthropomorphic fruit in Pixar-style melodramas: a lemon gives birth to an orange and the family erupts, accusing the mother of cheating; a banana fakes being rich and gets found out. Social media is a machine for surfacing whatever makes you say “ew” out loud, and it turns out generative video is exceptionally good at manufacturing that on demand, in volume, for free. So this is the AI most people see. They’re not being unreasonable when they conclude AI is worthless and a little vile; they’re reasoning correctly from the samples they’ve been given. — Claire

12. The paid layer doesn’t help either. Companies like OpenAI and Anthropic sponsor creators to talk about AI, people notice, and the reaction is “this is propaganda.” Both are true at once: it is paid placement, and paid placement is simply the native condition of social media, the same as every piece of #sponcon that came before it. But because AI is the subject, undisclosed sponsorship reads as conspiracy rather than business as usual. So the organic AI content is slop, the sponsored AI content is suspect, and there’s no third category that most people ever encounter. — Claire

On being human in the age of AI

13. AI is still not funny and has never made me laugh. This is notable: it’s one of the few areas that hasn’t budged while everything else has. Have a little whimsy! — Claire

14. The arts will never be the same, and live performance will grow in significance; displaying true human expression in meatspace will be increasingly valued. Musicians who consider themselves “just recording artists” need to buck up and play live if they want to survive (unless they’re already famous, or just supermodels). The value of a refined piece of recorded audio is dropping. — Charles

15. The world needs a new solution for proving that you’re a person. — Charles

On winners and losers in the AI era

15. Cloudflare and Stripe will be the two most important companies of this era for CMOs, outside of Google, Meta, and OpenAI. — Mike

16. Web analytics is dead, but no one has acknowledged it yet. There are two options: either we completely abandon analytics as a concept, or we standardize agent and bot sessions with a consistent identifier so we can see their browsing and searching behavior separately from real users. (What even IS a real user in 2026, anyway?) — Tom

17. Apple will be the largest winner of the AI boom and the first company to reach a $10T valuation.— Mike

18. The conversation around publishers, fair use, and content licensing is going to absolutely explode. As users rely on frontier models for agentic workflows, those models need to be able to access real content. This is a ticking time bomb. We’re going to see weird things like ChatGPT having access to the NYT and Anthropic not, or vice versa. It’s the AI-era equivalent of not being able to pay for things inside an app because of App Store and Play Store rules: it makes zero sense to the user and is a direct consequence of licensing negotiations. Or… we’ll see a robust content licensing ecosystem emerge. But I’d put the chances of that working at less than 5% at this point. — Tom

A few more deep cuts

19. The models are getting very good at writing. All the writers saying they’ll “never use AI in their workflows” are wrong. Just like Microsoft Word, spellcheck, and Google Docs, AI is going to be an essential, wonderful tool for augmenting writing. (But/and: we will continue to see slop as far as the eye can see.) — Tom

20. I think we’re barely scratching the surface on memory. You can fit most people’s useful context into a small .md file (most people are not that unique). Just like we talk about a “coding harness,” I think we’ll see innovation in a “memory harness” that auto-summarizes, compacts, extracts, and remembers. Importantly, this will push into new territory like values: beyond specific facts, what does the model know about what you value? And forgetting: how does it discard things you’ve outgrown? We need an overnight memory process. Do LLMs dream of electric sheep? — Tom

21. Gen Z and Gen Alpha aren’t going to experience some existential intellectual crisis because they were brought up AI-native in the workplace. The kids will still be able to think. Older generations complaining that the generation below them doesn’t have that dog in them is just a tale as old as time. — Mike

22. I sadly disagree with Mike. As a humble Gen Zedder myself, I have a lot of concern for my own peers, many of whom have decided to abstain from using AI merely because they can, and because it sounds cool to say so. Right now refusing costs nothing, which is exactly why it reads as trendy. I’ve met with friends on more than one occasion to give a sort of AI orientation because I’m afraid of seeing them get left behind. — Claire

23. The number of young adults who simply cannot be bothered to sit down and read a single page of text is terrifying. — Charles
    

And finally:

25. If you think things are weird now, you haven’t seen anything yet. — Tom

We’re just three weeks and change out from BRXND NYC 2026 at the Times Center and as we prepare for the big event, I’m continuing my interview series with another one of our phenomenal speakers and sponsors.

Today, we have a conversation with Andy Crossen, Chief Product and Technology Officer of VantagePoint™ by Partnerize, a company that sits at the heart of measuring how AI is fundamentally reshaping the way consumers build preference and purchase brands.

To see Andy alongside leaders from Microsoft, Amazon, Runway, Rocket Money and many more on stage on Nov. 5, click the button below to grab one of the last seats in the house.

Buy Tickets Here

On The Economic Infrastructure of AI Commerce

For most of my career as a marketer, I thought about my reporting and analytics tools the way an economist thinks about the unemployment rate.

While the current unemployment rate in the United States is 4.2%, I doubt you can find a single soul in this nation who believes that only 1 in 25 Americans are actively looking for work but do not have a job. Millions of Americans who the Bureau of Labor Statistics claim have “left the workforce” would prefer to be working. So why do economists watch this collective delusion so closely?

Ultimately, it’s simply the best measurable and directional metric we have where basis point shifts are truly microcosmic of employment trends. As much as anything else, the unemployment rate is probably the best tool we have for measuring the vibes of the United States economy.

Every era of media has run on a system of record like this. Television had Nielsen reports. The web had the click, and an entire industry of cookies, pixels, device graphs and attribution models grew up to count it. Those numbers were never as precise as we all pretended, but they directionally tamed the chaos of digital advertising and hundreds of billions of dollars in media got allocated against them every year.

But every current framework for measuring web traffic and ad performance is based on the notion that a real user with real little fingers is clicking on content. That assumption no longer holds. LLMs don’t just marginally disrupt understanding which entities influenced a purchase; they shatter much of the deterministic marketing attribution-industrial complex. Entirely new methodologies and governance layers will need to be built to probabilistically infer what influences buyer journeys in LLMs and eventually, what convinces agents to buy.

Since I first met Andy Crossen when I was at PayPal last year and got a preview of the thinking behind what would ultimately become VantagePoint™ by Partnerize, I’ve been convinced he and his team are building one of the most important technologies for marketing leaders to navigate a world where most purchase decisions are mediated by AI.

By way of formal introduction, Andy is the Chief Product and Technology Officer at Partnerize, where he owns product vision & execution. He’s spent more than 25 years at the intersection of marketing, measurement, and AI, co-authoring six patents along the way. Andy holds a Ph.D. in Computer Science from the Intelligent Information Laboratory at Northwestern University.

Before he takes the stage to showcase VantagePoint™ at BRXND NYC on November 5, I caught up with him to jam about the end of conventional web analytics, what a system of record for the zero-click era needs to look like, and how understanding AI journeys could reprice the media plan for CMOs.

A lightly edited version of our conversation follows below:

On how AI broke marketing analytics

Mike: Let’s start big picture. I don’t think people appreciate how quickly click-based analytics went from directionally interesting for CMOs to borderline pseudoscience as LLMs eat conventional product search.

You have as good a front row seat to this as anyone in tech: talk to me about what you’re seeing on the front lines and if this narrative tracks reality.

Andy: The narrative tracks. Even when the click survives, it shows up late, after the decision got made somewhere nobody was measuring.

A shopper asks an AI for the best running shoe for flat feet. She reads an answer stitched together from six sources, closes the tab, and types the brand name into a browser two days later. Your analytics credits branded search. The six sources that did the persuading get nothing.

We see that pattern at scale. Across the publishers and categories we measure, the median runs just under four zero-click conversions for every one that last-click recognizes. So the dashboard is still accurate about the receipt. It just lost track of the sale.

I’d go a little easier than pseudoscience. Click analytics was always a proxy. For twenty years the proxy moved with the thing it stood for. Now the two have come apart, and most dashboards haven’t been told.

Mike: When you’re in the room with senior leaders at brands, do you still have to introduce the zero-click phenomenon or is there already an acute pain point?

Andy: Almost never anymore. A year ago I spent the first twenty minutes of every meeting explaining zero-click. Now the room is usually ahead of me.

Earlier this year we ran a study with eMarketer, surveying 100 U.S. marketing leaders, and the numbers say the same thing. 91% agree AI-driven discovery is already changing how they approach marketing. 60% call it the hardest channel to attribute. Only 8% can track its influence on revenue and conversions end to end. So nearly everyone believes it, and almost nobody can measure it.

They’ve already swallowed the pill. They believe strongly that visibility does not equal value. What they need now is validation. Only 13% have a clear way to connect AI-driven visibility to partner compensation. That’s the gap.

The number I’d underline is a different one. Nearly two-thirds worry that publishers and content partners will prioritize competitors who can measure and reward AI influence. They’re right to worry. The content that shapes AI answers is a supply market, and supply goes where it gets paid.

So the question has changed. It used to be whether this is real. Now it’s whether they can prove it on their own numbers before a competitor does.

Mike: So which leaders at brands are feeling the pain from suddenly flying blind most acutely?

Andy: Whoever owns the media plan and has to defend it upstairs. Heads of marketing science and analytics, heads of media, growth leaders, and more and more often the CFO’s office. These are people whose models used to reconcile and now don’t. The Marketing Mix Model (MMM) says one thing. Last-click says another. The AI answers their customers actually read show up in neither.

It’s sharpest in considered purchases. Financial services, travel, performance gear, anything a shopper researches before buying. Those journeys moved into AI answers first, and they carry the most money per decision.

The person I’d keep an eye on is whoever signs off on the branded search budget. They’re about to get some questions.

Mike: In a similar vein, what’s the biggest aha moment you’ve seen from a senior decision-maker when they see what you are building for the first time?

Andy: The one I keep coming back to was a meeting with two senior executives who run all paid media at one of the largest software companies in the world. You’d know the name. They’d sat through every measurement pitch of the last fifteen years and had the posture to prove it. They also already had the AEO tools, so they knew which publishers AI cites most about them and their competitors.

We put the same categories on the screen, ranked a different way. By the conversions running through those publishers with no click, and what that influence is worth. The list changed. A content category that barely registered in their citation reports was driving real purchases, and their main competitors were already there. They went quiet for a while. Then one of them said, “Wow. That is very interesting. We’ve never considered that content category, nor did we know our main competitors were capitalizing on that type of content.”

That’s the gap. Citation counts tell you who gets mentioned. They stop before the purchase, so they never get to who earned the sale or who should get paid for it, and they rank the wrong things first. Follow the influence all the way through, and the conversation leaves the content team and lands on the media budget.

The aha tends to land the same way. People find sources they’ve never paid, never measured, and never thanked sitting at the center of their customers’ decisions, often with competitors already there. Then they do the math on what they have been paying for.

Mike: One thing that genuinely excites me about LLMs eating so much of the attribution-industrial complex is that we finally get some intellectual honesty about how much understanding influence was a guessing game and never as deterministic as pure performance marketers pretended it was.

But it feels like the old Wanamaker quip of not knowing which half of marketing spend is wasted is the strongest it has been in my career. Are you feeling this too?

Andy: Wanamaker was an optimist. He assumed he knew it was half.

The attribution industry spent twenty years selling determinism. Every conversion had a parent, and the parent was usually the last thing that touched it. That was always a convenience. AI answers made it impossible to keep up the act, because the influence now happens inside a model’s response with no pixel in the room.

I’m glad. Honest measurement is probabilistic. You observe behavior across a large consented panel, model it, publish how you did it, and let an independent auditor check the work. TV got measured that way for seventy years. Performance marketing skipped the step because the click looked like proof. Mostly it was a timestamp.

On VantagePoint™ as the new system of record for a zero-click future

Mike: Let’s go deeper around what you’re building. Your site describes VantagePoint™ as a way to “measure, verify and reward AI influence in eCommerce,” and calls it “the measurement, governance, and economic infrastructure for the zero-click economy.”

What should the key CMO-level takeaway be here?

Andy: AI answers are a media channel now, and most brands are buying it blind. The content those answers cite is shaping purchase decisions in your category every day, and almost none of it shows up in your reporting.

VantagePoint™ works in five layers. Visibility identifies where you show up. Influence shows which sources shape what people buy. Verification makes the number hold up with your CFO. Governance sets how it feeds the budget. Compensation lets the sources that earned it get paid.

The CMO takeaway is that this lands on the media plan. It touches the search line, the PR budget, creator spend, and the content team. Park it with one team, and you’ve shrunk the opportunity to fit the org chart.

Mike: Talk to me a bit more about the HaloIndex™ specifically, which compares influence that VantagePoint™ observed against influence traditional last-click measurement recognizes.

What can you share about the methodology here?

Andy: HaloIndex™ answers one question. For a given source, how does its measured zero-click influence compare with its last-click performance? Across what we measure, the median sits just under four. That’s a lot left on the table.

The method starts with a large, consented, opt-in clickstream panel, so we see real journeys. A shopper searches, gets an AI answer citing a set of sources, and later buys on a brand’s site without clicking any of them. We anchor on the end of the journey, the order confirmation, and work backward.

HaloIndex™ tells you how much. A second layer tells you who. When an answer cites eight sources, which ones earned the sale? These models aren’t invertible. You can’t open one up and read off why it chose what it chose. So we reconstruct it, weighing each source on authority, evidence quality, brand and product sentiment, and fit with the shopper’s intent, then we verify the influence across the set.

We publish what goes into the model. The computation stays ours.

Mike: Most of the AI search conversation over the last year has been about visibility: tracking what the models say about your brand and how often they cite you. What’s the gap between understanding zero-click journeys and starting to actually influence them?

Andy: Visibility tools count mentions. Mentions are cheap. One of the larger visibility platforms said publicly that about 95% of the citations they track are earned or social. That was the whole poker hand. The answers are built from content brands don’t own and mostly don’t pay for.

So the visibility dashboard hands you a list. A thousand publishers in rank-sort order. Then what? Are you going to reach out to all thousand and hope the right ones drive the additional value?

The gap is economic. You need to know which sources drive purchases, how many, and what that’s worth, so you can put money behind the ones that matter and stop guessing on the rest. Visibility with no price attached is a vanity metric with better fonts.

Mike: Every era of media has had a system of record everyone agreed to trade on: Nielsen for TV, the click, Google Analytics and various attribution tools for the web.

How do you build a true system of record for marketers in an age where AI is the primary interface to the internet?

Andy: Start with why Nielsen won. Buyers and sellers agreed to trade on it. Precision came later, if it came at all.

A system of record for AI-mediated commerce needs three things. It has to be independent. The platforms producing the answers can’t also grade them. It has to be audited. We’re an AAM Certified Platform, and we’ve formally entered the Media Rating Council accreditation process (Service Under Review). That path is rigorous on purpose. And it has to be transactable. A metric becomes a currency the day someone writes a check against it. That takes stable definitions, a disclosed method, and outputs a brand and a publisher can both look at and accept.

It also has to sit where the decision happens. Shoppers now decide inside AI answers. The measurement has to live there too, instead of waiting at the checkout page for a click that may never come.

On how VantagePoint™ reprices the media plan

Mike: If I’m a marketing leader, one of my chief concerns is that my media dollars go to the places that actually create demand when consumers shop through AI vs. merely capturing it.

How does VantagePoint help me understand this dynamic?

Andy: It comes down to the gap between a source that shapes consideration early and a source that grabs the click at checkout. Last-click pays the second group and misses the first.

HaloIndex™ separates them. Above 1.0, a source’s zero-click influence outruns its last-click performance. That source is creating demand. Below 1.0, it’s mostly collecting it.

Now pan out and put that next to your spend. Imagine your marketing dollars laid out by channel and source against the measured contribution of each. Where spend and contribution line up, keep going. Where you’re paying heavily for capture, ask harder questions. Where zero-click influence is strong, and spend is zero, you’ve found your highest-return opportunity.

Mike: As VantagePoint™ gets adopted in the market, where are there going to be some emperor-has-no-clothes moments where brands are spending big on media today and which channels do you think will prove to be undervalued?

Andy: Branded paid search is the obvious one. Plenty of brands are paying to intercept customers they already convinced somewhere else. VantagePoint™ shows where that somewhere else was. I expect a few marketing leaders to have the moment of, “Actually, wait a minute. I’ve been throwing all this dough at paid search, and I’ve been hand-waving my way through that budget review for years.”

The undervalued side is earned and expert content. Reviews, deep product explainers, the specialist publishers and creators who know a category cold. That’s what the models cite. PR has been the least measured line in the marketing budget for decades. It’s about to get a scoreboard.

I’d add owned content. Brands with evidence-rich product pages get cited. Brands with a thin product page and a big ad budget get skipped.

Mike: You know we have to touch on my favorite subject. As agents mediate more transactions and autonomously purchase on a user’s behalf, there’s going to be significant marketing efforts to try and influence agents at the point of inference.

This will likely give rise to an entirely new class of tools to understand which marketing efforts influence agents and possibly even payment structures that directly compensate for agentic influence?

How does the infrastructure you are building for VantagePoint™ map to that world?

Andy: It maps directly. An agent does in milliseconds what a shopper does in an afternoon. It reads sources, builds a shortlist, picks one, and pays. The click disappears completely. The influence is still there.

Every layer we’ve built carries over. You still need to see what the agent read, measure which sources shaped the shortlist, verify against real outcomes, and pay the sources that mattered. The difference is the buyer. Models don’t respond to brand campaigns. They respond to evidence. Agents don’t have brand loyalty. They have retrieval.

That’s where the partner marketing label stops fitting. Call it media pricing for a buyer who never sees an ad. That market will need common rules for how content gets ranked and disclosed inside agent workflows. Those rules should be open, and no single vendor should own them, us included.

Mike: Take the last word. What are you most excited about building in the months and quarters ahead?

Andy: Getting this into more hands. Today the people who’ve seen their own zero-click numbers are a small group of large brands. Every brand should be able to look up its own footprint, so we’re building a free self-serve view that does exactly that. Then comes the harder part: closing the loop between what a brand measures and where it spends.

The goal is simple. In a couple of years, a CMO walks into the budget meeting with a number for AI influence, the same way she walks in with reach and frequency today. Nobody spends the meeting asking whether it’s real. They spend it deciding where the money goes.

Andy Crossen is the Chief Product and Technology Officer at Partnerize. He’ll be on stage at BRXND NYC 2026 on November 5th at the Times Center.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

Agents, Ads and Demand Creation

As of about two weeks ago, I am a HUGE Chicago White Sox fan. Between the clear mandate from heaven, Munetaka Marakami’s umatched zeal for life, and whatever the hell this dance ritual is, the vibes are beyond immaculate and I’m firmly straddled to the bandwagon.

To look the part, I’ve instructed my venerable armada of agents to find me the best possible Sox gear. One has notably been quite a bit more proactive than the others in trying to push product…..

For the past few days, Instinct has been getting dragged quite a bit on Twitter for these sudden thirsty notifications that prompt a user to open an artifact containing a storefront of recommended products based on past queries. To me, there is only one explanation for why Instinct would move so aggressively in this direction— this is a test to set the groundwork and context for rapidly introducing advertising into the core user experience. And I’d argue it’s quite elegantly done! There are a few things I firmly believe here:

1) Any consumer AI product with scale is going to be ad supported....it's the only sensible business model. We’re going to be tapping the sign below so many times in the next year that our finger oils will melt the paint.

2) Instinct has the right general idea here of pushing incremental products that are related to a past query I initiated but abandoned, surfacing genuinely new finds that are interesting to me as a shopper. I had no idea this line of Pope Leo merch existed until Instinct surfaced it but I am basically the n of 1 audience for this.

Said another way, this is setting up for an ads experience that is closer to incremental demand creation than pure demand capture.

3) The “personalized storefront as an artifact” is a really interesting format to play around with to introduce dynamic components so that an agentic commerce experience moves out of text and actually starts to actually resemble something like shopping. While the artifacts are pretty bare bones listings of products today, the general form factor of an artifact is a great canvas for introducing say, creator video recommendations of products and other dynamic formats that move the needle for higher consideration purchases. Yet again, the HTMLedium is the message!

Done correctly, these artifacts are baby steps towards a “marketplace for one” experience that accelerates discovery of new products rather than just tasking an agent against something you already know you want to buy.

4) In a beautiful twist of irony, users are going to think the recommended artifacts are ads already anyway (they aren't yet) so the actual introduction of ads might not be all that jarring.

Notably, we saw this exact same pattern of commentary late last year, right before ChatGPT formally introduced ads. Initially, the company started recommending some of its embedded marketplace app partners (i.e. Target, Expedia) with small widgets below the organic prompt responses. Users thought they were ads and for 24 hours, my feed turned into a righteous dog and pony show.

Then, ChatGPT ultimately introduced ads in March and there was some more grumbling but within weeks, OpenAI felt confident that ads had a negligible adverse impact on user engagement and went full throttle. I expect a similar dynamic to play out with ads in personal agents.

It’s a tale as old as digital media time. Nearly all consumers will say they hate ads but in aggregate, their observed behavior will tell a very different story.

Notes from A Lil’ ChatGPT Ads Experiment

Speaking of ads, OpenAI continues to ship improvements to its ad product at a torrid pace, launching new visual ad formats that appear when users generate images in ChatGPT to complement the introduction of sponsored “agents” into the ChatGPT interface in September. These are two really good product launches from OpenAI that go a long way towards solving the two toughest design challenges around introducing ads into a chat interface.

1) By introducing a sponsored agent that exists outside the core chat, users cleanly opt-in to deeper engagements with a brand and very clearly expect to be “sold to” if they interact with the widget. I’d imagine that clickthroughs into an experience like this might be less than conventional ads but advertisers know they are getting highly qualified users who haven’t been hoodwinked into something they didn’t know was an advertiser led offering.

2) The more ad formats and contextual adjacencies that OpenAI can experiment with, the more they can lean into anchoring ad targeting to the user’s query when it has clear commercial intent and testing ads that drive serendipity independent of the underlying prompt when it’s clear that the prompt is not commercially driven.

That said, while enterprise advertisers get shiny new toys, the majority of the ChatGPT ads experience is still simple static images and text boxes that activate under relevant prompts.

To better understand the functionality available to the longtail of advertisers as it exists today, I’ve been running a small test, spending $25-50/day on ads promoting BRXND. Here’s what they look like in the wild.

A few observations on the ChatGPT ads platform after a week or so of running live:

1) It’s absolutely incredible how sparse the reporting interface is for all the ambitions OpenAI has around ads.

2) I’m all for brevity in ad copy but I have no idea how you are supposed to write compelling copy in such a tiny text box. ChatGPT Ads has an AI interface where it auto-generates pithy variations that do fit but the copy is comically minimalist. 95% of ChatGPT ads I’ve seen in the wild just have text that runs over which OpenAI curiously says is fine and even best practice.

3) I’ve noticed some sneaky dark patterns, both within the interface and within how Codex/ Chat GPT Work interprets OpenAI’s guidance when you try to work right from the harness that seem designed to get you to spend more money on pricing models that are more favorable to OpenAI.

Specifically, ChatGPT Ads offers a CPC pricing structure but consistently tried to push me towards paying on CPM for campaigns where conversion was the primary objective. This is a pretty common company line in ad tech, based on the claim that the system can better optimize towards conversion outcomes if it sees more view-through in addition to clickthrough attribution.

This will all become a trivial and semantic discussion if ChatGPT can ultimately dial in its conversion funnels to optimize for performance outcomes but for now, I prefer to test on a cost-per-click model and I venture most budget-conscious longtail advertisers will feel the same way.

4) We’re spending about $40 CPM/ $6 CPC so far to reach an executive level marketing audience. This is expensive but in line with most public information I can find on the “going rate” for ChatGPT Ad campaigns in B2B. It’s also cheaper than LinkedIn, albeit with more opacity around targeting.

5) It is pretty remarkable that I was able to easily set up a full as campaign right from Codex on Luna Max, albeit mostly powered by computer use rather than a dedicated integration. Media buying right from the harness is here. 

I’d love to cross-reference this experience with any enterprise agency or brand-side media buyers running ads in LLMs at a larger scale. If you’re tinkering with ChatGPT Ads and would be comfortable sharing your experiences (can be anonymized) in a future edition of BRXND, please get in touch—-mike@brxnd.ai.

Metal to Market: AI Is The Workspace

James is back from his travels and I’m absolutely in awe of how much ground he, Noah and Sam cover in this episode. The whole hour is fantastic but I particularly recommend tuning in for some of James’s spicier takes on Dots and how it is a precursor to much larger ambitions that the frontier labs have for the future of hardware. Amazingly, it’s been almost 18 months since Jonny Ive came into the mix at OpenAI but so far, we’re still interfacing with AI through phones and computers. It’s about time for things to get a little bit weirder.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

Buy Tickets Here

The Golden Age of Marketing

If I could concoct a perfect stew of macroeconomic conditions for the advertising business, it would look something like a technological platform shift that:

1. removes the bottleneck of shipping code, unleashes a Cambrian explosion of new consumer apps, and turns distribution into the existential challenge of our era

2. radically strips OpEx out of businesses, juicing contribution margins and leaving far more room “below the line” to spend on growth

3. onboards more than one billion people into new surfaces where the cost of compute to remain on the frontier permanently outpaces revenue that can be earned from subscriptions or taking a cut of transactions.

4. powers algorithms that get ever closer to becoming “perfectly competitive arbitrage machines” that maximize “performance” for advertisers and take rates for the platform.

Advertising, which historically has hovered at ~1% of GDP since the 1930s and was declining in relative contribution through much of the last decade, is now the largest share of the economy it has ever been. Everything is already an ad network…….. and there are about to be a lot more ad networks.

This is the golden age of marketing. While this may sound like unbridled fan service, I wholeheartedly believe that marketing is going to have the biggest seat at the proverbial table that it has in any of our lifetimes and careers. Yet, as I talk to marketers in my orbit, the prevailing mood leans a bit more towards angst than excitement.

It’s a bizarre paradox. While it’s clear that our industry is going to get a hell of a lot bigger, there’s also the most uncertainty I’ve ever felt about what exactly the advertising business will look like.

The old guard is clearly fading. The Trade Desk– a solid proxy for the advertising business of yesteryear– has seen its share price plummet 90%+ since roughly the precise moment that ChatGPT effectively became a search engine (less than two years ago on Halloween 2024 if you can believe it!) and is now fully blocked from Safari. The entire holding company model looks wobblier with each new ads CLI that gets launched. Even conventional retail media is under threat from the notion that agents bypass sponsored listings entirely.

At the same time, Meta is booming, Google’s ads business is stubbornly resilient and OpenAI has set the foundation of an ads business that elegantly navigates the tension between monetization / commercial utility and user trust. Even if the conversation stays within the Overton window of advertising existing to primarily serve the purpose of persuading humans to buy a product or service, we seem set on a path where advertising could easily exceed 2% of GDP.

But the conversation won’t stay there for long. It feels almost inevitable that the most hotly contested battleground on the modern web will be trying to influence agents at the point of inference, via both organic and paid tactics. Even though the idea of advertising to agents lives rent free in my head, I don’t have a satisfying framework for exactly how this will play out. So far, my favorite take on how this world will take shape comes from Antonio Garcia Martinez of Chaos Monkeys fame.

The fact we now have a routing intermediation layer on AI token usage means we’ll soon see a ‘regular’ version where you pay full price, and a subsidized version where results on commercial prompts like ecommerce/travel are skewed slightly in exchange for cheaper token costs (or have outright ‘sponsored’ results that display separately).

AI-enabled consumer apps with weak monetization (compared to money-gusher enterprise usage) will opt for the sponsored token pipe to float their computation costs. The third-party AI ad exchange of the future will be injecting the ‘sponsored’ part of the subsidized results into the token stream, combined with an attribution loop around the sponsored AI result that credits the app layer for surfacing the result.

The commercials might even be a rev-share on the downstream monetization, much like referral agreements, combined with dynamic bidding from the merchant on agentic attention (based on human user history, managed in a GDPR-safe way, in which the app owns the user data and relationship).

Trillions.

As ads geared towards agents become a reality, I expect we’ll see an increase in both brand and performance marketing budgets as a relative share of corporate expenses. Over time, the latter will become more and more about agents executing buys to influence other agents with humans increasingly getting abstracted from the process. But as the alpha from that discipline slowly erodes, I suspect brand marketing budgets will be where spend truly skyrockets.

Let’s take the proverbial AI search for “best running shoes” here as an example. If you’re a marketer, you can try to win by spending calories optimizing your PDPs for agents, jamming your product feed full of semantic search terms and generally making your product or service legible to the machine. Or you could spend calories investing in channels that reach real humans so that they tell their agent to “buy Adidas Hyperboost Edge in size 10.5, make no mistakes.” It’s a painfully obvious statement but bears repeating– the best hedge against the uncertainty of agentic commerce is a brand that shoppers actively seek out or tell their agents to seek out.

In any event, these are the best times to be in this business. The cash is flowing, the problems are more complex and murkier than ever and there’s a real need for wacky, first-principles thinking. Sure as hell beats trying to squeeze an incremental dollar out of marginally better keyword targeting.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

Buy Tickets for BRXND

With a palpable swagger in his step and his trademark 1.5X speed podcast-style diction, it took Sam Altman all of 117 seconds into his DevDay keynote today to announce Dots, Open AI’s “remarkably capable, always-on agents built to handle everything.”

Yesterday, Instinct raised $1B at a $10B valuation and hired a chief business officer who built the commercial and partnerships engine at Snap, a company that Instinct is now worth more then. Yes, you read this correctly, Instinct’s valuation exceeds Snap’s (and Lyft’s, and Dropbox’s and Etsy’s) market cap despite the fact that Snap has 500M daily active users compared to Instinct’s <1M.

Since I said last week in “The Great Disintermediation” that AI is the internet now, the collective weight of Silicon Valley has thrust itself further on the accelerator to make agents the primary way we interface with both our work and life online. It’s moving at an insane clip and all I can do is think out loud with you all to try and keep up.

So consider today’s piece a brain dump of everything on my mind as agents emerge everywhere all at once. If any of it resonates or you’d like to banter further, drop me a line at mike@brxnd.ai and let’s keep the conversation flowing.

1. While I’m not impressed with the gist of Dots overall (the demo felt largely to me like a product built for the unique ways OpenAI employees work), I really appreciated Sam’s initial counterpositioning against other agents in the space. Within a minute of taking the stage, Sam gently needled demos from other agents by listing out what they saw as low value use cases. Some OpenAI product marketer cooked here.

While Dots is primarily a professional tool, consumer focused agents would be wise to follow this lead. In a long ranging interview with Patrick O’Shuagnessy, Instinct founder Noah Shinn fell back on the familiar tropes of booking hotel and making restaurant reservations as top Instinct use cases, two areas I find wholly underwhelming. The “tyranny of the travel demo” struck again as companies demonstrate agents against problems that marketplaces have largely solved for an average user who isn’t obsessed with hyper-optimization.

Google Duplex’s breakout 2018 demo was booking a restaurant reservation. Gemini later showcased agentic AI by planning a family vacation. Microsoft pitched Copilot Actions around dinner reservations and travel booking. OpenAI introduced Operator with examples like making reservations and booking campsites; and Amazon has long used “dinner and a movie” as the proving ground for Alexa’s ability to coordinate multiple tasks. Again and again, tech companies reach for the same proof point: the future of personal computing is saving us a few clicks on dinner and vacation.

And every time, people who live in the real world (correctly) say that they want to go on a bit of a meandering adventure when they look for a resort. Booking a vacation is the consummate example where some friction is a feature, not a bug. It’s actually the cliche that the journey is the destination!

2. My main question about “personal agents” still centers around whether they will truly be a game changing new paradigm for how normies interact with technology or just the next iteration of “AI as better search.” I strongly suspect it will be the latter– it turns out most people don’t need a chief of staff to manage their everyday lives.

But there’s a lot of incremental value in better search! My kids are doing a Bob’s Burger’s motif for Halloween this year and I decided it’s finally time for me to realize my destiny as Jimmy Pesto. Instinct found me all of the components of a Jimmy Pesto costume flawlessly in less than a minute, including the exact Italian flag pattern tie he wears. This type of fuzzy semantic search is a nightmare for conventional commerce interfaces and a clear example of where I only bought something because the agent made it significantly easier to do so. Better search is true TAM expansion and where I believe the bulk of the value in agentic commerce lies.

3. I want to be optimistic that the rise of mass consumer agents will be positive sum for how technology interacts with the real world but….. the cynic’s case is very compelling here. Even if most people still use tools like Instinct and Muse as “better search”, there will still be a rush of sophisticated consumers deploying agents to seek rewards, damage claims and other special perks that previously assumed a human would go through a cumbersome process to redeem. In other words, the perks only exist in the first place because they are hard to access!

As a result, corporations will get far more Draconian about how they adhere to policy and will be far stingier with manufactured moments of serendipity for customers. Rather than being a democratizing force, mass adoption of agents will mean more of the world will run on “knowing a guy.” Along the way, we’ll see more “AI for thee, humans for me” tiers of customer support offered.

But there’s a fascinating second order question I’ve yet to see pondered as much – how do Sierra, Decagon and other companies building AI for CX respond here? Does their core business effectively become building “counterstrike” enterprise agents that parry back the consumer agents trying to beat the system? To this point, agentic CX systems have been built under the assumption that the “customer” is a person. How do they adapt to this sudden game of prisoner’s dilemma that they now will play with their fellow agents?

One company that few people have heard of to watch here is Scaled Cognition, which recently raised $100M from Khosla to build a frontier model for CX that is deterministic in how it enforces enterprise policies. Begun, the agentic wars have.

4. A take I’ve seen many variations of recently is that this new influx of agents will accelerate the “financialization of everything” and create a world where more business transactions effectively become a programmatic ad exchange sold to the highest bidder. When thousands of agents are looking for the same concert tickets at drop, we get real-time price discovery that provide the spoils to those with the deepest pockets.

I’ll take the other side and say this mostly won’t happen simply because I can’t think of an idea with worse vibes. A historically unpopular technology ushering in an anti-egalitarian reality that most consumers and policymakers hate is not the way. I suspect this is one place we will see meaningful regulatory frameworks take shape to fight perceived price gouging. But it will be a rocky road to get there– if you thought the “senator, we sell ads” hearings were rough, imagine trying to explain inference to octogenarians in Congress.

5. As harnesses eat the world, I’m finally beginning to entertain the possibility that the “death of websites” is a real concept worth grappling with…but dead internet theory, which ostensibly is now a measurable reality, is so nebulous to the point it is barely meaningful.

Per Cloudflare, autonomous AI agents and scripts have surpassed human web traffic and now account for 57.5% of global webpage requests. But these “agents” are not all alike! The vast majority of this “traffic” is simply dumb scrapers or bots that are utterly meaningless noise. But a small, but ever-increasing number are agents that are truly acting on a user’s behalf and might hold real commercial intent. These agents are potentially the most valuable Pareto customers visiting your digital properties. And by and large, there is no way to tell them apart from useless or nefarious scraper bots! What a time to be alive!

Both Cloudflare and Google are working on web bot auth offerings and I think there’s a good chance this is the space that the next out of nowhere AI rocketship company gets built.

6. Muse announced a slate of partnerships last week with Wayfair, Best Buy, Fanatics and several other large retailers, spurring some great memes and rumors of a grand anti-Amazon alliance forming at Meta’s behest.

I wouldn’t read too much into most of these deals for now. Most of these same retailers very publicly rushed to form partnerships with OpenAI’s native checkout and then quietly moved on. These are all very low-stakes deals on both Muse and the retailer’s side.

That said, it is intriguing to see Walmart here again after the Barons of Bentonville were so vocal about how poorly the ChatGPT instant checkout experiment went. Walmart has made significant investments in an owned agent called Sparky and has a $7B ads business that agentic workflows outside of its walled garden threaten. It’s fair to wonder if the Muse partnership is a hedge that they are quietly hoping fails.

7. Speaking of Muse, I’m really interested to see if/when Meta tries to really press its unfair advantage of having millions of Instagram creators at their disposal and looks to more directly embed creator recommendations and reels into the core Muse experience. Li Haslett Chen had some good thoughts on what this could look like back in BRXND 118 in June when we discussed the concept of a “Codex for Shopping” that would embed creator recommendations into an agentic commerce flow.

While it is familiar and comfortable, chat is ultimately a stale interface for consumer technology and may well not be the final AI interface. At its core, chat is utilitarian in nature and in aggregate, the people yearn for entertainment over utility. On a long enough timeline, consumer products generally get TikTok-ified. I venture that consumer AI and social networks will effectively be one “thing” by 2030ish.

For commerce specifically, authentic first-hand demonstrations and expert explanations are often the most valuable source material for understanding a product or service. AI can always generate another slop article. It cannot easily generate a person’s lived experience.

8. Of course, the strongest creator network belongs to Google who still remains conspicuously absent from the personal agent wars. Google Labs launched an experimental agent designed for families called CC two weeks ago and it barely even registered in the AI news cycle! Surely, Sundar is lying in wait and will reveal himself after all the other companies have shown their hand. Right? Right?

9. As a potential new front door for consumer AI experiences, Snap is starting to feel comically undervalued. Again, we’re talking about a product that has 500M DAU’s and is trading at <$9B market cap!

10. I’ve been thinking a lot about my friend Joe Kaziukenas’s quip that TikTok Shop might prove to have a greater TAM than agentic commerce. I’ll say it until I’m blue in the face–so much of the incremental dollars to capture come from shopping as entertainment, not utility.

Already, Instinct has cleared $1B in “agentic commerce transaction volume” with 40% of users uploading their credit card information, an impressive feat for a company that is invite only and barely a month into public release. It’s safe to assume Muse will quickly 10x that number with its built-in distribution and we’ll see what’s still to come from OpenAI.

For its part, TikTok Shop will likely clear $100B in GMV in 2026.

Your AI Needs a Computer. Here’s Why

Over on Alephic’s Youtube channel, Sam Knight and Noah had a great wide-ranging discussion on some of the newest model release, going deep on how to put Astra, Opus and Luna to work.

Specifically, they compare what each model is good at, why speed matters, and what it looks like to leave an AI task running for 18 hours. They also get into Apple’s new hardware, Meta Muse, and why Noah is moving his AI work off his laptop.

For my part, I continue to bang the drum that computer use is the AI capability that has progressed the most in just the last couple of months and that AI being truly competent at navigating the most cumbersome web and local device interfaces will transform enterprise work in ways we can’t yet fathom.

One last thing….

As he so often does, Benedict Evans perfectly compressed an entire thesis I’ve spent multiple newsletter editions trying to land into a single sentence.

This is precisely the discussion we’ll be convening in 37 days at BRXND. Would love to see many of you there. If you have any questions, please be in touch.

Buy Tickets for BRXND

— Mike

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We’re incredibly grateful to the sponsors who make BRXND NYC 2026 possible. In lieu of any additional preamble, let’s get to know each of the companies a little better.

Apex Sponsor

Runway: Runway is an applied AI research company building frontier video generation models. Runway’s tools are helping 95% of the Fortune 100, global advertising firms and thousands of brands scale their creative output and ambition.

It’s been a true honor to get to know the Runway team over the past two months and as a lifer in New York tech, it is incredibly inspiring to have a company that is building true frontier models right in our backyard. In February, Runway closed a $315 million Series E led by General Atlantic, with NVIDIA and AMD Ventures participating, roughly doubling the company’s valuation to $5.3 billion and just this month, they crossed $200M in ARR.

TechCrunch framed Runway’s scale of ambition well: “Every major AI lab is betting on language. Runway is betting they’re wrong.” Its three founders met in an NYU art-and-tech program and are building world models that learn how reality works from video rather than text, an audacious and contrarian bet on what co-founder Anastasis Germanidis calls “the most transformative technology of our time.”

Hollywood has leaned into Runway’s technology for years. Lionsgate had Runway train a custom model on its library of more than 20,000 titles, Best Picture winner Everything Everywhere All at Once used its tools, and AMC Networks uses it for previsualization and promotional imagery. Last December, Runway shipped Gen-4.5, which debuted at #1 on Artificial Analysis's blind-voted Video Arena, ahead of Google and OpenAI. I’m infatuated watching any of their demos around how objects move with weight and characters and products hold their look from shot to shot, constantly resetting my priors of what I think AI can do in creative production.

We’ll have Runway’s Chief Creative Officer, Jamie Umpherson, on stage at BRXND and we’ll introduce you all to him properly with a profile in the newsletter before the show. Finally, for any readers based in SF, Runway also has their own summit coming up next Wednesday with leaders from Deepmind, NVIDIA, Gamma and many more taking the stage.

Presenting Sponsors

Emberos: Emberos is the operating system for AI Brand Management. The platform enables brands to understand how AI systems perceive them, predict where their narrative is heading, shape that narrative at the source, and prove what changed. Powered by the emberos Brand Knowledge Graph and a suite of specialized AI agents, emberos gives organizations the intelligence, execution, measurement and governance infrastructure required to manage their brands across AI systems

Knowing what ChatGPT says about your brand is useful. Knowing why it says it is more valuable. Knowing what is likely to change next, what you can do about it and whether that action worked is where this becomes an enterprise operating function.

This was the opening sentence when we introduced Emberos founder and CEO Justin Inman in BRXND 138 and from the moment I met him, I’ve always thought, “this is a guy that gets the craft of brand management in the age of AI.” In a world too often dominated by short-term GEO arbitrage hacks that the next model release renders counterproductive, Emberos is purpose-built for enterprise brands to truly shape their destiny in AI search. Fresh off a new round of funding, we’re excited to welcome Emberos to talk about how online discoverability has shifted in an AI/LLM driven search world and what brands can do to manage it proactively.

VantagePoint: VantagePoint™ is the advertiser-facing measurement and economic governance layer that quantifies partner influence across outcome-driving journeys where traditional click-based tracking no longer exists, and converts that verified influence into enforceable, auditable commission logic. It captures influence from AI summaries, AI search results, and other machine-mediated discovery paths, and operationalizes that influence through Partnerize’s attribution, commissioning, and payment infrastructure to ensure contributors to conversion are identifiable and compensable.

For the last 25 years, a massive marketing-industrial complex has been maniacally focused on perfecting deterministic attribution for marketing based on clicks. And then boom, AI becomes the internet and the very notion of a click begins to become quaint. And so the old Wanamakerism returns with a slight twist: “I know half my marketing spend influences ChatGPT, I just don’t know which half.”

Alongside some fascinating work Cloudflare and Stripe are doing, VantagePoint is the truest first-principles thinking I’ve seen around what attribution, analytics and economic governance for marketing should look like in the LLM-era. After Chief Product Officer Andy Crossen takes the stage, I suspect many of the marketing leaders in the crowd will start thinking differently about how to allocate media budgets in this new age.

Supporting Sponsors

Dstillery: Dstillery is the predictive audience company, helping marketers reach exactly the right audience for every digital campaign. Powered by multimodal AI and its agentic platform, DS-1, Dstillery enables precise audience targeting at scale, simplifying workflows and driving superior campaign outcomes.

Dstillery’s predictive models learn across data modalities – including web journeys, CTV signals, search signals, purchase intent signals, and more – to build a robust understanding of a brand’s best customers and activate high-performing audiences across programmatic and social channels.

We’re extra grateful to Dstillery as a returning sponsor of BRXND and can’t wait to welcome them back to the show. I first met Dstillery CEO Michael Beebe back in April and the next day, published “the machines eat media buying” based on our jam session. Everything that has happened since makes me think so much marketing work will move to the harness gradually…then suddenly, making DS-1 an even more powerful tool for the moment.

Writer: WRITER is the enterprise AI agent platform that helps marketing teams build an AI-native marketing engine grounded in their data, workflows, and team. Marketing demands keep growing, but teams don’t — compliance reviews become massive bottlenecks, and without proper guardrails, brand drift creeps in across every channel. WRITER rapidly accelerates the content-to-campaign lifecycle, increases AI visibility, and achieves customer personalization at scale, delivering 333% ROI in six months on average. Every output is on-brand and compliant the first time, giving you the strategic growth and competitive velocity to stay ahead of the market.

Another company that builds models here in New York! I love a good example of counterpositioning and Writer’s “we didn’t train it on trivia, we trained it on the work” is chef’s kiss copy for Palmyra X6, their flagship model for agentic work at scale. We’ll have more from Writer when we introduce our next round of speakers so stay tuned.

A huge round of thanks again to all of our sponsors for supporting the show— we truly could not do this without you. For folks reading who would like to meet these companies, tickets are still available but will sell out.

Thanks for all of the support, and I look forward to seeing you in November.

JOIN US AT BRXND

Purchase Tickets Here

Last Thursday, I spent the day with 250 fellow media executives at Brian Morrissey’s Rebooting Summit, a day that was largely built around pondering a single question:

What is the function of a media company in a world where AI increasingly sits between you and your reader?

It was a full circle moment for me— Brian originally introduced me to Noah and I’ve always been incredibly jealous of his linguistic panache (abattoir is my favorite word in his arsenal) and how effortlessly he can distill the milieu of an entire industry into 800 words each week.

It was a day of fascinating juxtapositions. About one-third of the both the main stage and hallway discussions pontificated on the existential conundrum of media as AI eats the world while two-thirds of the day dabbled almost whimsically in the sausage-y minutia of running media businesses. I spoke to one media leader who intended to retire after 2028 because they thought it would be “the last year of the business I know and love.” I also had a ten-minute discussion with a prominent B2B media CEO about the price of sandwiches at our respective conferences, spurred by our mutual appreciation of an exquisite chipotle aioli. It’s like the old Tolstoyian trope where even at the nadir of his unrequited love for Kitty, Levin still could appreciate exquisite weather.

But far and away, the most powerful session of the day was Brian’s fireside with Nick Thompson, CEO of the Atlantic. Perhaps overly judgmental of the fact that he’s a guy who voluntarily does LinkedIn selfie videos. I was unfamiliar with Nick’s game as an AI pundit but within seconds, I found myself hanging on his every word.

Essentially, there are three possible scenarios Nick is planning for as he shepherds the Atlantic through the AI era and how he imagines the world may look in five years. While Nick is obviously speaking from his point of view as a media CEO, the worlds he lays out are worth pondering for leaders in any business and considering in the interest of future-proofing your own career. Note, I’ve editorialized the framing on these slightly and abstracted the takeaways a bit for a non-media audience.

Scenario 1: Precedented Times: AI is somewhat overrated in a practical sense, we soon hit the asymptote where the practical value of incremental frontier intelligence reaches zero and the processes of actually imbuing AI into the more arcane corners of American capitalism is even more arduous and cumbersome than expected. Something resembling ordinary times ensues for most internet businesses.

Nick didn’t use these words but I think of this as functionally the “AI as normal technology” hypothesis that has regained steam among many tech pundits. This isn’t a bubble at all— it’s actually a potentially bullish outlook where the gradual adoption of AI in the enterprises is a persistent tailwind for GDP growth. Ultimately, scenario 1 doesn’t really require any additional intellectual calorie burn to prepare for. We all sort of have a mental model for what better enterprise SaaS looks like.

Scenario 2: The AI Monoculture: Dario is essentially right about AI fully recasting society in its image and likeness and upending the entire structure of technology, capital, and labor. Most digital and technology businesses as we know them today will cease to exist.

In that world, Thompson suggested that the only real function of The Atlantic will be to own what is truly tactile and human. They’ll do more events, ship more print magazines and become something of an IRL town square for their readers, essentially abandoning the digital hustle altogether.

For most of us, I don’t think there’s a whole ton of purpose trying to game theory out a world where AI is essentially a Leviathan monoculture. Everyone’s got a plan until they get punched in the mouth. Things will just get unconscionably weird and somehow, we’ll adapt and find a way.

Scenario 3: The Great Disintermediation: In a cursory sense, the “internet” as we know it today will still exist in 2031 but it will no longer be the primary way people interface with technology or receive information. AI will be. The humble webpage will endure in some form but agents will become the primary consumer of web content and in turn, none of the existing business models of media will really be viable. For one, an ad— sold on a CPM with the promise that it will be seen by human eyeballs— will be a vestige of quainter times.

I call this The Great Disintermediation and this is the world that I believe executives in nearly every industry should be preparing for. Ultimately, publishers just got a two-year head start on confronting that future thanks to Google Zero. Increasingly, every brand will exist more and more as AI perceives it.

In just the last two weeks, we’ve really started to feel the acceleration of the great disintermediation rumbling under our feet. Muse, Meta’s consumer agent, is currently #1 on the App Store and it feels like Zuck is betting the entire fate of his company to will a mass-scale consumer agent into being. It’s the only bet to make. To date, Meta has built a $2T business with Apple’s boot perennially hovering over its neck. If Muse can become the dominant consumer entry point into AI, Zuck will have the most ubiquitous technology business of all time, one that can stay free to users in perpetuity supported by an ad engine with targeting capabilities that make the current algo seem pedestrian by comparison.

In turn, traditional aggregators of demand will start rapidly getting aggregated themselves.

And whoa boy, brace yourself, many new consumer agents are coming on the heels of Muses’s success. New models such as Jev that push the boundaries of the IQ+speed+cost will allow for new AI-native consumer apps / vertical harnesses that can sustainably handle multiple fast LLM calls in a way that wasn’t pragmatic before. That sway you feel in the force is VCs racing to fund a Stanford dropout building “Instinct for X.”

As I speculated in the “agentic commerce conundrum”, Amazon is none too thrilled about the prospect of third-party agents running amok on its site. The old quip about Amazon is that it’s a search engine with a warehouse attached. And it ain’t the warehouse that makes money. So this is what we get.

For Amazon, the calculus is reasonably simple— they’re betting that if forced to choose, the American normie is more dependent on Amazon than any Meta product or nascent consumer agent. I believe this is correct. But more broadly, Amazon needs to protect their $80B ads business. Until a model emerges where ads can effectively be served to third party agents at the point of inference, incentives are simply not aligned for Amazon to let these services operate on their platform. It’s a net negative ARPV proposition.

I’m sympathetic to Amazon’s position here— the company spent three decades building a logistics infrastructure that reshaped the global landscape to send things to your doorstep next day at horrifically low margins. The gentlemen’s deal with society is that they could subsidize the crappy business of eCommerce by simply creating more rectangles out of thin air to sell at 90%+ gross margin! I’m being a touch cheeky here of course but it’s worth remembering that agentic commerce of course only works if someone is willing to be in the pesky, low margin business of actually purveying goods.

While Amazon’s move is obvious, it will be interesting to see how other retailers and marketplace businesses with less leverage respond to the influx of agents now hitting their websites. If you were struggling to reach new customers, the great disintermediation might also be the great TAM expansion. The bots may not view your product pages or consume your “brand message” but they buy things all the same. If you’re a mid-market retailer, an agent might choose to shop from you even though their human never would have. The human would have just gone to Amazon.

Unsurprisingly, Shopify and Meta have teamed up to frame this as an “arm the rebels” moment for indie merchants.

It’s the right message to push. I’m genuinely excited about the breakout potential for smaller brands that the rise of agents could provide, especially during this Wild West period where it’s hard to discern exactly what value propositions consumer agents respond to. Thanks to Shopify’s Catalog API, a tiny boutique store in Kalamazoo now lives at the point of inference in millions of daily agentic queries alongside the largest brands in the world without having to pay a dime in paid advertising (yet!) to reach said agent. This will all be frustratingly opaque and weird at first but I suspect we’ll see some indie brands randomly hit it big on the early agentic slot machine.

Going back to media, Brian posited a hypothesis on stage (that we actually discussed together on his pod back in early 2025) where a few select premium media organizations like The Atlantic and The New Yorker will keep a direct relationship with readers while the vast majority of outlets will become wholesale sellers of information to AI. While this framing sounds dystopian at first blush, it’s not like the status quo is enabling thriving media business models. The SEO and programmatic chophouse of the before times didn’t always exactly incentivize Pulitzer worthy journalism. A world in which analysis backed by shoe-leather reporting and high quality service journalism is effectively commissioned by LLMs might well create a healthier information ecosystem for the proverbial average reader than the search and social arbitrage era.

Disintermediation is not synonymous with death. Sometimes, it’s just a new means of distribution.

In 44 days, about 400 of us will gather at the Times Center for BRXND NYC 2026 amidst this world where AI is fundamentally reshaping how consumers build preference and purchase brands. As we prep for the show, the question that has lived rent-free in my head is nearly the same one that permeated The Rebooting Summit, just for the demand side.

What is the job of a marketing leader when AI increasingly sits as the arbitration layer between a brand and its customers?

Going one level deeper, where do marketers have to make zero-sum choices between what message will resonate best with humans and what will be most favorably perceived by machines? Where do I tactfully fight the great disintermediation to make sure I directly own my narrative vs. where do I try to persuade the algorithm?

JOIN US ON NOV. 5

These are now the same questions that an entire generation of software businesses must now confront. 3,000 miles and a world away, Dario took a quick break last week from pacing the frontier and building wet labs to get brutally framemogged by Mark Benioff at Dreamforce. There’s a strong case that the Benioff towering over Amodei…and the many derivative memes it inspired like the below should be TIME’s photo of the year. And wait, guess who just happens to own TIME?

But make no mistake about who is the alpha dog here. Anthropic took the definitive company of the SaaS era and made it a plugin.

As Ben Thompson wrote last week in Stratechery, it’s unquestionably the correct business decision for Benioff to make, largely because his product living within Claude is ultimately a better user experience. Liberation from having to spend multiple hours per day in a CRM might as well be AGI for revenue professionals! But while Salesforce is an easy punching bag, the same dynamic is playing out against software that we thought we loved. From our company Slack recently:

I now rip Clay and Apollo enrichments directly from Codex with nothing but an API key. From a pure user perspective, it’s an absolute dream. I don’t have to learn an esoteric new software interface. It’s just me, the harness and the task I want to get done with significantly less friction. I get the quarter-inch hole without having to even pick up a quarter-inch drill.

More than anything else, this is what the broad strokes SaaSpocalypse hypothesis of March-April got wrong. Pardon the Claudeslop syntax of this sentence but the best software businesses were almost never selling the front-end workflow. They were selling a job to be done. Fortunes will now be made moving that job from the browser to the harness.

Towards the end of his fireside chat, Nick delivered a line that drew by far the greatest applause of the day. When asked what he’d say to the frontier labs, he replied:

“Build a system that keeps a free, thriving media ecosystem going and it’s better for you if that happens.”

I agree wholeheartedly with his argument-- if quality media cannot sustain itself, AI becomes a Malthusian trap. But zooming out, it’s hard for me to adequately express how much of a breath of fresh air it is for a media leader to frame the importance of their business in simple utilitarian commercial terms rather than a sad appeal to pathos.

Going back to my journalism school days, far too many media people I met harbored a weird combination of grandeur about the importance of news but were bitterly cynical about any attempts to make journalism viable in the digital age. What will mystify me until the end of my days is that a considerable portion of my professors and early mentors seemed to fancy themselves victims of the plight of media rather than possible participants in the story of saving it.

Speaking as a media professional and a citizen, I’m incredibly grateful that the industry finally has innovative leaders out front who are willing to think first principles in the face of seismic disruption to their business. But the broader point here applies to business leaders of all stripes as AI becomes the internet.

You can hope for whatever future you’d like but I’d be actively planning for a world in which most work, commerce and digital leisure is mediated by AI. The great disintermediation is here.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

Editor’s Note from Mike: BRXND NYC is 50 days away and as promised, we’re continuing our interview series with leaders who are taking the stage at the event.

In today’s edition, Noah chats with Pip Bingemann, who purpose-built a model designed specifically to inspire, not give you the answers. To see Pip showcase this live alongside our incredible set of speakers building on the frontier of AI and marketing, grab a ticket to join us in the Times Center on Nov. 5 here.

Buy Tickets Here

On Divergence Models

Back in May, I talked with Pip Bingemann, the co-founder of Springboards. He showed me Flint, a small fine-tuned model they built because the frontier models all tend to give us the same stuff.

The canonical example he likes to cite here is to go ask ChatGPT or Claude for a random number between one and ten, and you get seven. But when you ask for a car, you get a Camry or a Civic; or when you ask for a running shoe tagline, you get “Run your way.” Despite being developed by different companies, they all come back with the same answers.

Springboards had already seen this in its creativity benchmark, where 16 models answered briefs for 100 brands and more than 600 people judged the work.

Pip calls this convergence. I don’t think this convergence is purely bad: sometimes you need an average answer, like when you’re doing synthetic research. But anyone who has tried to use AI for creative work can attest to the fact that it can be difficult to get where you need (especially if you need to be funny).

That is where Flint comes in. It is not supposed to do everything. Springboards calls it a “divergence model.” NoveltyBench basically asks the same open-ended question 10 times and counts how many meaningfully different answers a model gives. Flint scored 7.47, compared with 2.54 for GPT-5.4 and 1.83 for Claude 4.6 Sonnet.

Since we talked, Kieran Browne has written a technical explanation of Flint, and Will Douglas Heaven has covered the model for MIT Technology Review. This is lightly edited for readability, but it is basically the conversation we had a few months ago. I hope you enjoy it.

Noah Brier: I’m very excited to talk about Flint. I think we are converging on a similar set of ideas. The thing I have been thinking about is that I pretty firmly believe the models are getting worse at writing, not better. My feeling is that RLHF fundamentally stands in opposition to good writing.

Pip Bingemann: I think reinforcement learning is 100% part of it. But I think there is a much bigger issue at play.

Flint is not a foundational model. It is a fine-tuned model. It is a very small fine-tuned model. There are problems with it.

The thing we see is that the models are highly predictable because they converge into the same space. The answers are almost always the same. If you ask for a car model, you will probably get Toyota Camry, Toyota Corolla, Honda Civic, Ford Mustang, maybe Ford F-150. Every time, they land in the same space. Flint gives you something else.

Or ask for a tagline for New Balance running shoes. It could be anything. What you tend to get is “Run your way” or “Run your world.” Flint gives you something different every time.

For me, this is the scary thing. If I copy the same prompt into a new session again and again, it is the same over and over. And I am not picking on Claude or GPT. If I put Gemini in there, it is the exact same thing.

Noah: So what is Flint, exactly?

Pip: Flint is a model. It is a tiny model, a 30-billion-parameter model, but it breaks the distribution of predictability you see in every other model.

It is not good for everything. But when someone asks an open-ended question, they deserve an open-ended answer. At the moment, you are not getting it. People ask, “Can’t you just dial up temperature?” You cannot.

The way we use it in Springboards is that we do not believe Flint is a good model for everything, but it is good for some things. We give people the option to switch between models. If you want to put on a divergence model, you can. Or you can leave it on auto and we route to a model based on the request.

Noah: Why do you think this is happening?

Pip: There is one paper you need to understand: Artificial Hivemind. The basic finding is that regardless of how these models are trained, they have all converged into the same space.

One of the tests was: give me a metaphor for time. Of the 25 different models they tested, nearly every one said some version of “time is a river.” The models are highly predictable and repeatable, but they have also converged into the same space.

If you ask for a motto for a social media page, you can get word-for-word the same answer from very different models trained in different ways. I think it is a very scary thing.

The reason I think this is happening is a couple of things. We have already run out of data. There is more AI-generated content in the world every day, and it is going back into the world. At the same time, the quality of our data is degrading. The models are eating themselves because they are training on the open web, which is increasingly AI-generated.

Synthetic data is not bad. It depends what it is for. In worlds where there is a correct answer, that is fine. But in worlds where it is open-ended, it is not okay.

There is another paper about model collapse when models are trained on recursively generated data. There are two sentences from that work that I love. One is that probable events poison reality, because the probable and average get overestimated. The average doubles down.

The other is: the tails of the distribution die first.

That is what I think is happening. As these models get bigger and are trained on the same data, the tails shrink. They get pushed into the exact same space over time.

Noah: I buy that as the underlying explanation. My challenge is maybe a layer above it, which is that I am amazed how hard it is not to generate slop. It is so insidious.

That said, I am not sure I think convergence is an overall bad thing.

Pip: It is not. It depends on the use case. That is why I say Flint is not good for every use case. We will never be 100 percent Flint because it does not make sense. Sometimes you want the average.

Noah: Yes. In a lot of cases, getting to average is totally fine. For some marketing cases, it is totally fine. It is actually why AI can be pretty good for synthetic research. You exactly want the average and you do not need to push against it. But on the creative side, that is why there is work to do.

Pip: Exactly. I liked that sentence: sometimes getting to average is fine. The quicker people realize that, the better. It is an improvement in a lot of cases. For a lot of people it is, and for a lot of people it is not.

Noah: What are you doing with Flint now? Is it just inside Springboards?

Pip: Right now, yes. The more important thing is probably what is wrong with it.

It is a 30-billion parameter model, which is tiny. With a smaller model, there are two big problems. One is that its world knowledge is not as good. The other, bigger problem is that it struggles with complicated prompts. It has a big context window, around 250,000 tokens, but its coherence of instructions collapses pretty quickly.

At the moment it is released as an alpha model inside Springboards. We route to it occasionally and monitor how much we route to it. We let people switch to it if they want to. At the same time, we are scaling the model up and building it as an API.

Noah: Part of the reason I ask is because I would love to try it when you have the API. We have a bunch of those shape problems we need to solve, where we are specifically looking for divergence. We are playing with all these techniques for getting to not boring.

Pip: One of the tests we use for Flint is: tell me a dad joke.

If you ask Claude or GPT to tell you a dad joke, you get the same joke every time. Claude is always atoms. GPT is always skeletons because they do not have the guts. Sometimes it is a scarecrow. You cannot be funny if you only have one joke.

With Flint, we actually give you different jokes every time. There is a repetition problem and a convergence problem, and Flint kind of looks after both.

The same thing happens with prompts like “pick an emoji at random” or “pick a number between one and a hundred.” It is always 73 or 42 or 47. With Flint, we actually give you randomness.

Our point with this is that I do not care about quality in the first step. I really do not. It is not up to me to decide what is good and what is bad. That is your job. I want to help you expand your horizons and spark an idea in your head. Then it is up to you to work out what is good and how to overcome the rest.

You just cannot even do that with these other models. That is the scary thing.

Noah: How has it been building a model? Painful?

Pip: It has been really good. We have been working on it for just under two years.

The cost to build the model was cheap. The compute was cheap. The expensive part was all the experiments to work out how to get this thing to drive divergence without degrading the base performance of the model.

That is the real breakthrough with Flint: how do you get this thing to drive divergence without degrading the base performance of the model? We give it knowledge benchmarks as well, like MMLU and science tests, and we do not get worse on those tests. We actually drive variance without degrading knowledge.

That was the hard part, and that is the fun part. It is a never-ending job. It is my favorite meeting of the week because you go: here are papers, let’s try it. It does not work. Here are the problems with our model. Let’s scale them up and keep going.

Noah: That is awesome. I am excited to try it when you put it on the API. I think these are very interesting questions. Even your pushback that Flint cannot do everything feels right to me. I do not need one thing that can do everything. I need all these pieces. I need all the ingredients.

The problem for you is that people have to know enough to know this is a thing they need to solve for. With randomness and divergence, the vast majority of people have not approached the edge of any of these models yet.

Pip: This is the scary thing, particularly with advertising. I sent a message to a friend who is a pitch consultant and asked, “As you help clients go through the pitch process, are you noticing responses becoming more similar?”

He said, “More similar? Almost a joke. In the last 12 months, the net effect is the supply chain gets devalued. It is not good for anyone. It is hard to argue for nuanced premium and margin when everyone’s giving you the same answers.”

That is the problem no one realizes. The advertising agency world, in particular, is walking into it blind.

Noah: That one goes deep.

I have had three friends over the last few months who work at different places where their agencies have pivoted to be “AI-first.” And they are like, what does this even mean?

To me, I am just like: if you were any good at marketing, would you not recognize that counter-positioning is a pretty critical thing? If everyone is saying the same thing, there is probably a real advantage in being counter-positioned against that thing instead of saying you are something you very clearly are not.

I feel for them, but these models are sucking so much oxygen out of every room that you are left trying to fill it in. But the idea that you are going to fill it in by saying the exact same thing as everybody else seems like a problem.

Is there anything more disqualifying for an agency than not being able to put together strategic positioning for yourself?

Pip: It is insane. Especially because their entire history is that their job has been to sell people. That is what they do. They sell the talent of their people.

Then to say, “You do not need people, you just need AI,” is insane.

Noah: Exactly.

Pip Bingemann is the co-founder and CEO of Springboards. You can read more about Flint here. To see him live on stage at BRXND NYC 2026, click here to purchase tickets.

As always, thanks for reading.

BRXND NYC is 52 days away and as we get closer to the big day, I’ll be turning over more of the newsletter mic to our outstanding speakers and sponsors who are at the heart of how AI is fundamentally reshaping the way consumers build preference and purchase brands.

Today, we have a conversation with Justin Inman, Founder & CEO of Emberos, the operating system for AI brand management and a sponsor of this year’s show.

Justin will be on stage November 5 at the Times Center along with leaders at Amazon, Rocket Money, Bark, Microsoft and a whole bunch more being announced soon. Click the button below to grab your ticket or reach out to me at mike@brxnd.ai with any questions.

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On AI Brand Management

“Knowing what ChatGPT says about your brand is useful. Knowing why it says it is more valuable. Knowing what is likely to change next, what you can do about it and whether that action worked is where this becomes an enterprise operating function.”

This quip is essentially Tom and I’s AI Search Maturity Index in a pithy 40-word nutshell. It’s also the final line in the press release announcing that Emberos, the operating system for AI brand management, raised a $5.5M seed round earlier this month.

Emberos is a newer player on the AI search scene but one I’ve followed closely since inception and marveled at the ambition around. Emberos operates a multi-agent platform that reads how AI represents a brand across the major LLMs, predicts outcomes, orchestrates fixes automatically, and proves lift through the Brand Knowledge Graph and Tiered AI Visibility Index. All in a company that is only a year and change old.

Thus, I’m ecstatic that Justin Inman, Founder CEO of Emberos, is taking the stage at BRXND to talk about how online discoverability has shifted in an AI/LLM driven search world and what brands can do to manage it proactively.

A former enterprise growth leader at Google, Justin was pivotal in helping major brands such as Amazon, Sony, Coca-Cola, and L’Oréal navigate marketing’s last major upheaval: digital transformation. He also serves on the IAB’s AI Board, where he shaped Measuring Visibility in the AI Era, the IAB’s industry playbook for measuring brand and publisher visibility across AI-powered discovery platforms.

Before he takes the stage at BRXND, Justin and I caught up to talk shop a bit on the current state of how AI perceives brands, the concept of a “brand “knowledge graph,” and the rising importance of creators in shaping a brand’s destiny with LLMs.

A lightly edited version of our conversation follows below:

Mike: You position Emberos as “brand management for the agentic era.”

I suppose my light devil’s advocate comment is that your brand should always have been consistent across channels, and this is just good hygiene with a new name on it.

How are the stakes and mechanisms of brand management different now than they were in the proverbial before LLM times?

Justin: Brand management has been around forever and it’s going to continue to be around. It’s just going to look a little different.

For the first time ever, marketers and brand managers have to think about a couple of new things. The first is they’re marketing to machines and bots. That hasn’t historically existed and it’s a different audience they’re going after.

The second piece is that the point of discovery is incredibly fragmented now. If you’re looking at this new world of AI chatbots becoming the front door of the internet for the first time ever, brand managers have to rethink that marketing funnel across awareness, consideration, intent, purchase. That’s all happening through a single layer now. The playbook of pushing people down the funnel looks very different in this world than what they’ve been trained up or educated on.

Brand managers historically marketed to different channels intentionally, because they knew different audiences lived on those channels. There might be different messaging on social versus your site versus PR, because that was intentional. The problem is that breaks in this world, because the LLMs favor coherence. The second you start to have different messaging, it gets confused, and that’s where we get into the hallucinations and all the other things that come with it.

In five years we probably won’t be calling it brand management for the agentic era, or AI brand management. It’s going to be just brand management, because it’s going to naturally encompass this.

Mike: So what does AI brand management look like at the CMO level?

Justin: CMOs are starting to realize that this is a brand coherence issue, and it has to touch not just your typical marketing teams, or the digital team, or the search team, or even the tech team. They all need to be orchestrating together.

They’re feeling the pain. They see their organic traffic getting crushed. They’re going into these LLMs themselves and seeing the positioning and the narrative across their brand and realizing it’s inaccurate.

Mike: AI brand management is a bit different than some of the more industry standard terms like generative engine optimization……but I get the sense that is intentional.

How do you counter-position Emberos against some of the other platforms enterprises are buying that have been in the market a bit longer than you have?

Justin: A lot of these companies are basically saying, here’s a box, play in it. We know, because we work across a bunch of different verticals, that what works within gaming is very different from what big beverage wants or banking wants. It’s a different universe, different relationships. So it’s a disservice to any customer using an off-the-shelf tool, they’re cracking the window, barely looking through, and not seeing the full picture.

A lot of the big platforms today are also looking at this as a content generation solution, you have to create more and more content to compete into these LLMs. They’re looking at it from a concept of push-to-publish, which is AI-generated content, which is AI slop in our opinion.

These are short-term visibility hacks with potentially long-term implications for your traditional search business. It’s a content treadmill that you will never be able to get off of. We don’ tdo that.

The Brand Knowledge Graph allows us to be way more focused on what you need to do. We look at the most highly authoritative cited sources and make optimizations against those, versus just more and more slop and content.

I’d also say a lot of platforms give you a laundry list of optimizations and never tie it back to any type of meaningful lift. At Emberos, we give you the biggest rocks to move with a predicted lift attached to it. We track those changes in our platform and then we show you the actualized lift. We’re the first closed-loop platform in that respect to hit the market.

Mike: Break down the Brand Knowledge Graph further for me. What are the core inputs, and how does that change how a model mediates brand preference?

Justin: We can map over brands, products, creative assets, influencers, creators — you name it, it doesn’t matter. Once we map that over to our systems, we build up a node structure, and that node structure is dependent on the prompting layer. We essentially develop a city map of how the internet looks at that specific entity we’re tracking.

So we can see all the biggest influencing factors online, the publishers, the creators, the site itself, and how those all correlate to the entity we’re tracking. The cool thing is we can do it by the ecosystem of that brand. But we can also look at it at the vertical level, or from a competitive set.

Then we track all those relationships over time, which gives us a baseline, which unlocks the predictive capability. So we can say, if you make this change to this site, or get on this publisher, or work with this creator — because of that baseline, we predict that X outcome will happen.

I’d also say the tools today have not historically given a lot of insight for the why. They’re just giving you the how. The more advanced marketers we talk to have been working with a tool for X amount of time and there’s still a gap in fully understanding the reasons why competitors are gaining visibility.

Mike: A lot of your early clients are in entertainment and media where more than most categories, brand management is paramount.

What’s the canary in the coal mine for how media and entertainment businesses navigate the transition to AI search for marketers in other industries?

Justin: Entertainment’s an interesting one, and I learned this in my time at Google. It doesn’t matter what industry you’re in, every marketer looks at entertainment to find out what they’re doing. It’s partially because they have their finger on the pulse of culture better than any other industry.

They’re going to LLMs and seeing, for streamers, attribution in terms of where to stream — you’re Hulu and you’re seeing “stream this on Paramount+,” which is a massive issue. If you’re a studio, LLMs misgenre about 20% of the time.

The LLMs get confused about what these titles actually are about. If you have a title with the word “dead” in it, it automatically thinks it’s a thriller. But it might not be. So you have titles getting the wrong genre, wrong actors, wrong directors, or even wrong dates because the movie industry does a soft release and then the box office opening weekend, and if the model picks up the soft release, it’s wrong.

If moviegoers are asking what they should see this weekend, and the genre is labeled as a violent thriller but it’s actually a dark comedy, it alienates a whole portion of your population that could go see that movie. And it sticks. Six months later someone says, I liked this movie, give me 10 others. You need to be in the right genre and associated with the right comp set.

Same thing with gaming. If there’s Grand Theft Auto 1, 2, 3, 4, it’s very hard for the models to tell the differences between each one. Gamers are asking specifics about a specific title, and a lot of times it’s incorrect character associations, incorrect where to play, where to buy.

Mike: Talk to me a bit more about governance because I believe that will ultimately be the most important battleground for AI search, especially in the enterprise. Why is monitoring truth and compliance across AI such a hard problem and how does Emberos help brands here?

Justin: Every AI answer is generated on the spot, and it comes out different depending on the model, the user, and the day. There’s nothing fixed to review and nobody inside a brand currently is set up to own this governance. Legal owns claims, comms owns narrative, SEO owns content, and AI blends all three into one sentence that’s in nobody’s job description.

We created Echo for this reason. It validates what the models are saying before anyone acts on it, traces where the information came from, flags where they contradict each other, and keeps a record of who approved what. It’s a peek inside the black box of LLMs before you have an issue that you are chasing to fix.

That matters now because brands are moving from watching what AI says to acting on it. Once you’re making changes at that scale, you need to know the information behind them was accurate, and you need to be able to show what changed and why.

Mike: In a similar vein, you’ve been running a live public test of your predictive claims on box office data. Walk me through this and how this work might map to the larger zeitgeist of agentic commerce.

Justin: Back in Q4 we said, can we test our predictive capabilities at a macro level? We looked at a space that’s rapidly changing and allowed us to make a prediction and then relearn and retrain. That space was the box office.

So we have made it a practice. On Fridays we do a box office prediction, on Monday we see what the normalized numbers are, and then we retrain our models. What we’re looking at is how these titles show up in LLMs, and we’re seeing a strong correlation to box office revenue — the types of prompts people are asking, what we call Share-of-Prompt, and how that correlates to real-world business outcomes.

We see signal lead times almost 12 days before traditional Hollywood trackers. And in some cases our box office prediction is two to three times more accurate than traditional Hollywood trackers.

We’re not a box office prediction company, but we’re going to replicate that model across our priority verticals. The ultimate goal is to get enough reps under our belt to say, a 1% Share-of-Prompt increase equals X amount of box office. It gets really compelling when you think about SKU-level optimization and the agentic shopping world.

What we’ve also seen is companies that historically relied on Google search feeds to update their forecasting models — it’s broken, because AI Overviews and AI Mode are no longer a clean feed. We have companies scoping us to see if we can fill that gap.

Mike: So zooming out, where do you see your suite of tools most producing genuinely counterintuitive insight for marketing leaders to take action on?

Justin: What we’re doing around creators, those examples are an aha moment for marketers. We just worked with a running shoe company and said, here are the creators you’re currently working with. We mapped those over and said, these creators are actually getting cited in AI, but they’re not getting cited for your specific company. So that’s an issue. You need to restructure the creator content — and here’s the list of things you need the creator to say that will help get them cited more.

We’re rolling out something called a Creator Graph right now. More and more, the void is going to be filled with creators, because there’s not going to be enough content as publishers start blocking the labs to figure out their true citation influence before they renegotiate. But the issue with the creator stuff is we’re seeing creators who aren’t even creators influence models.

It’s a mom in a car with her daughter saying, I just gave my kid these vitamins, they’re great. And the video has all of 100 views!

Mike: What about the creators themselves and their representatives? How much are they starting to care about how their creator brand is mediated by AI?

Justin: Often, talent is driving influence over AI answers and the talent doesn’t even know it. Creators are always looking at shares, potential reach, maybe affiliate sales. I think the next new metric they’re going to be looking at, probably in the next 12 months, is citation. How much they’re actually driving the influence over the LLM output.

The bigger agencies are starting to wake up that the agents themselves are doing all the research for their clients. So if their clients aren’t represented correctly in these LLMs — where everyone is researching projects and potential brand partnerships — that’s a big issue.

Mike: Close this out for me in style. With a fresh round of funding in the bank, talk to me about what’s next for Emberos, particularly at the nexus of creators and AI search.

Justin: We’re constantly expanding the Emberos Brand Knowledge Graph, adding new models, mapping new categories, and making our predictions sharper. But what excites me most is our work with creators. It’s genuinely category-defining.

We can trace an idea from the creator who sparked it, through the conversations that gave it momentum, all the way to the AI-generated answer a customer ultimately reads. Then we help brands understand that journey, participate in it authentically, and shape it for maximum impact.

We built Emberos as an operating system for a market that refuses to stand still. As the relationship between creators, brands, and AI search evolves, Emberos evolves with it. That’s the kind of partner we want to be: not another platform brands have to keep up with, but the reason they stay ahead.

Justin Inman is the Founder and CEO of Emberos, the operating system for AI brand management. He’ll be on stage at BRXND NYC 2026 on November 5th at the Times Center.

To suggest additional builders that I should feature in this series, please get in touch at mike@brxnd.ai

As always, thanks for reading.

— Mike

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Muse & The Agentic Commerce Conundrum

Agentic commerce is suddenly very real folks.

Two weeks after Instinct initially took the tech world by storm, the hills of the Twittersphere are alive with the sound of people executing complex purchases end to end with a few simple texts in iMessage. Almost overnight, we’re seeing the inklings of a true agentic shopping experience that will really feel superior to many shoppers and not just on commodity, emotionless purchases.

Now, as he so often does, Zuck has come in off the top rope as a fast follower with a personal agent called Muse that will quickly bring this technology to the masses. Early reviews suggest that Muse is very, very good across both life admin and agentic commerce use cases. A month ago I asked when consumer AI would have its “Claude Code moment.” This is it. Yet again, the road to financial run is paved with people who bet against Meta.

With each passing day, it feels increasingly likely that we’ll work primarily within harnesses in the next 12-24 months. Now, if Meta can shape the future in their image and likeness, we’ll run our personal lives and shop in them as well.

There are a plethora of fascinating second order questions to spill ink over here but top of mind for me is how brands of all stripes respond to this sudden influx of consumer agents. We’ve already seen the first flashes of tension with Resy deactivating user accounts after Instinct made 200+ requests in an hour. This is a perfectly reasonable response and microcosmic of a much larger point of tension.

Simply put, the best agentic commerce experience for users is going to often be at odds with optimizing yield for merchants. Tools like Instinct, Muse and Grok are going to mercilessly hunt for promo codes and off-menu discounts and will consistently try to “beat the system” in unforeseeable ways on behalf of their users. But beyond that, what really is going to make many of the largest incumbent platforms on the internet viscerally uncomfortable is that they no longer control the shape of a buyer’s purchase journey.

Agentic commerce breaks many of the meticulously optimized purchase funnels (often at razor thin margins) that consumer businesses have spent the last two decades building online. It’s also incredibly unclear if or how entities like Muse and Instinct will be influenced by traditional sponsored product ads as they decide what to purchase. This is going to cause a major conundrum for merchants, especially because from a profit perspective, most large retailers are now advertising businesses masquerading as purveyors of goods.

Brands are understandably skeptical of the version of the future in which they get disintermediated from their customers. There’s not much nobility in being a wholesaler and logistics arm…and you never beat Amazon at that game anyway. I’ve long posited that many have effectively been playing a version of narrative arbitrage where they are enthusiastic about agentic commerce in public to appease the street but behind closed doors, they are happy to see early pilots struggle to gain traction. Walmart enthusiastically backed OpenAI’s early embedded checkout experiments but was pretty quick to publicly share how much lower the average yield was off their own platforms. That “agentic commerce converted 3X worse” number sure spread in the press like wildfire.

That said, I’m far less convinced then Eric Seufert that open agentic commerce outside of retailer owned ecosystems is entirely a “mirage”. Blocking off your marketplace writ large to an agent eager to quickly buy something from you (and likely operating on behalf of a person who spends a lot more money annually than the median shopper) is cutting off your nose to spite your face.

It’s far better to be early to a platform shift that is happening gradually than late to one when it happens suddenly.

The Three Miracles of Agentic Commerce

Historically, the macro knock on agentic commerce has been that it further solves buying but kills shopping in the process.

Last month, I put forth a framework that inspired lively discussion on LinkedIn around the “three miracles” that need to happen for agentic commerce to ultimately be better at helping users discover what to buy than the status quo experience in online commerce today.

1) Merchants must expose enough structured data and tell a consistent brand story at both at the catalog level and across the web for agents to understand how individual products match to highly semantic and fuzzy intent.

2) When items are sold at multiple retailers (or sometimes when choosing what to recommend in first place), agents must consider which merchant provides the best overall value as a function of price, loyalty status, rewards, delivery window, and more.

3) Agents must possess enough “media literacy” to understand which data sources are reliable arbiters of product quality. In other words, they must discern pay to play slop masquerading as organic product reviews from publisher or creator content where an objective reviewer actually tests a product.

While #2 and #3 are largely in the domain of the companies building agents, point #1 is directly on brands and by far the most impactful lever marketers have to control their destiny in agentic commerce, at least until we can all buy ads to influence agents at the point of inference.

While there’s much handwringing from the tech Twitterati about if agents should be broadly anthropomorphized, I strongly believe that agents will effectively be more extreme versions of human buyers when it comes to gravitating towards brands that are story rich and consistent with their messaging across all key touchpoints. As Noah wrote back in December:

”When you RLHF a model to be a “helpful assistant,” you’re essentially training it to care about context, explanation, and story (emphasis mine)—exactly the things we thought the robot would skip past.”

What I’m really trying to say here is that however big a seat your company thinks the marketing team should have at the agentic commerce table, make it bigger.

Shopfiy’s Field CTO Rohit Nathany put this cleanly: “I highly recommend my marketing and growth friends start treating catalog and product data as a marketing tactic, not just an admin task. Your catalog should be complete, structured, and story-rich.”

On that note, I’d love to jam with any marketers who are currently working on this problem— drop me a line at mike@brxnd.ai.

The Brand Brain & Turning Context into Competitive Advantage

As more work moves into AI tools, a company’s advantage comes from how effectively those tools can use its own knowledge and expertise. Said another way, context is king and a “brand brain” is how companies turn that context into an enduring competitive moat.

With assistance from Youtube maestro Sam Knight, Noah has been on fire lately on the video and podcast circuit and we’ll be bringing much more of Noah and Sam’s work to you here.

In the video below, Noah unpacks what a Brand Brain looks like in practice: connecting a company’s data, context, expertise and tools so AI can work with the knowledge that actually makes the organization distinctive.

Geno Smith vs. Armageddon

Football is back….which means I once again have to remind myself that suffering through another New York Jets season is a choice I freely make. Nobody is forcing me to do this! I can pull the plug at anytime if this isn’t even achieving the goals I want it to.

Anyway, if like me you’re mildly distressed that an Anthropic staffer seems to genuinely believe that there is a 10% chance AI will cause mass extinction and he’s powerless to stop it, here’s a quick post to put your mind at ease.

I would at least like to see one 34-13 wild card round loss to the Baltimore Ravens on a miserable sleet-soaked January afternoon at MetLife before AI brings upon my imminent demise. Is that really too much to ask?

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

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Since Noah published “On AI and Writing” two weeks ago, the discourse around AI as a “legitimate” medium for communicating original ideas has only intensified. Tim O’Reilly wrote a contrarian defense of writing with AI arguing it is “a field of possibility from which humans can and will summon up and communicate insights, truth, beauty.” Stanley Druckenmiller proudly published a Wall Street Journal op-ed with AI and was resoundingly backed by the paper’s editors. On the flipside, Derek Thompson shipped a phenomenal interview with Pangram founder Max Spero pleading that “we can’t let AI writing take over the internet.”

Suffice to say, for folks like me who attach a significant amount of their professional identity to being “writers,” it’s been an especially intense two weeks where it’s become ultra clear that the genie isn’t going back in the bottle.

Like Noah, I started my career as a journalist and maintained an active second career as a ghostwriter, blogger and newsletter author throughout my decade or so working in technology companies. No matter what I was doing in my day job at the time, any writing project was always the “hardest” work I was doing. Staring down the barrel of the blank page burned more intellectual calories than anything else I was working on.

This dynamic has only gotten more intense in the age of AI. LLMs are much better than I am at “low-caloric” writing tasks, leaving only the arduous work of thinking in their wake. Therein lies the agony and the ecstasy of this moment for anyone who writes words online for a living. The job is harder than ever but the work that remains is the most gratifying aspect of why we do this.

Building on Noah’s piece, here are a few thoughts I have for writers trying to future proof their craft and understand their raison d’etre as AI eats the world.

1. While I agree with Noah that the more powerful models are getting worse at writing in many respects, I believe they have crossed the threshold of being better at the “view from nowhere” style of summation analysis than even great human writers. For an omniscient take on most subjects, AI-generated text does the job at hand. You’ll probably even learn to love some AI-generated work.
   
   So what remains scarce when the majority of what has been written for the web is done well by LLMs? Insights derived from true shoe-leather reporting, analysis drawn from learned and felt experience and things that make people laugh. Writing that doesn’t check one of these boxes will be owned by a machine.

   
   

2. The original promise of the internet is that it was supposed to free writing from the rigid form-factors of newsprint and allow unbounded creative expression. In practice, so much writing for the last two decades has been self-censored to fit in the SEO box. We are increasingly being freed from this.
   
   However, the tough thing for writers adjusting to this new world is that before LLMs, there was an enormous amount of demand for “view from nowhere” takes optimized for SEO. The market made us into one thing and is now aggressively demanding another.

   
   

3. In a similar vein, Sam Parr has a really good “barbell” theory of internet writing that the best posts are either <500 words or >2K word long essays. His advice is to avoid the messy middle length that defines much internet writing. This is more true now than ever before.
   
   It is also really hard! The ~1,000 word messy middle is the easiest form factor to make an argument in and generally where the narrative wind blows me. This is largely due to the fact that for two decades, 800-1,200ish words was the standard length of piece commissioned by editors for op-eds and many manners of freelance writing.
   
   I am deeply struggling to reframe my craft around 400-500 word quips, supplemented by the occasional piece d’resistance type essay. Note the length of this piece! But I believe this to be a near existential thing to get right.

   
   

4. I agree with Noah’s take that the best writing is fundamentally anti-consensus. But this presents a conundrum for writers who have to ship content on a consistent cadence. The reality is that you just aren’t going to have that many good, contrarian takes! So how else can you consistently write pieces that can’t be replicated by AI?
   
   Many of the best pieces of internet writing are something that an industry, subculture or at times an entire society is deeply feeling but lacks the precise eloquence or courage to coherently say out loud. I believe this “finger on the pulse” style of writing will be safely LLM-proof for a long time and will become even more valuable as emotionless AI-generated copy eats the web. For now, models simply cannot “feel” vibes in real time at nearly the level people can.

   
   

5. Lots of the best internet writing is going to look increasingly rough around the edges. I believe we’ll see a style emerge that looks less like traditional essays and more like “jagged collections of bangers” thrown together without the usual transition paragraphs.
   
   This will be a response to both reader demand for brevity and a counterpositioning against the tendency of LLMs to create flowy, overly cohesive narratives. It will be weird for a while but ultimately liberating for readers and writers alike.
   ‌

6. For my money, Matt Levine is the consummate example of the most AI-proof writer on the web. If you want to have a good laugh, try prompting a model to write an essay on a topic you know well “in the style of Matt L’s Money stuff.” AI is horrifically bad at capturing Levine’s je ne sais quoi of wit backed by a couple decades in the boiler room.
   
   Honorable mention here goes to Sam Kriss as a sheer master of prose that operates on a level that simply can not be emulated.

   
   ‌

7. To steal from Picasso, LLMs are going to be better than you at “learning the rules like a pro.” They will not be better at breaking them like an artist.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

But first, BRXND NYC is 65 days away on 11/5, and tickets are currently $749. We look forward to seeing you at the Times Center for a great show.

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The Three Vectors of the AI Race

Increasingly, I believe there are three primary vectors in the AI race:

1. Raw IQ: Who has the smartest model?

2. IQ + Cost: Which model is the smartest per token dollar?

3. IQ + Cost + Speed: Which model is the smartest per token dollar at the highest tokens-per-second (toks)?

While raw IQ is the frontier race everyone pays attention to, I think IQ + cost is the most interesting thing to watch in AI right now.

But before we get there, let’s talk about raw IQ. The consensus (and my personal POV) puts Fable on top, with Sol behind, then a fairly big gap to number three. Broadly, though, this class of model should be reserved for your toughest problems where the costs don’t matter that much. If the AI is solving something truly valuable, then the $10/50 (input/output cost per million tokens) you spend on Fable shouldn’t matter that much. The problem, of course, is that the way most tokens are getting consumed today are in coding harnesses like Claude Code and Codex, and when you’re doing that kind of work, a lot of people just leave the smart model on and let it cook. Luckily, these harnesses, particularly Claude Code, are getting smarter about offloading easier tasks to cheaper models in the form of subagents. A lot of people dream of routers to solve this, but I think for the most part we’ll be good with this smart orchestrator, dumb-agent architecture.

More interesting, though, is that even today I’d argue the vast majority of work can now be done by much cheaper models (Luna, particularly in Max mode, is an amazing workhorse that’s worth spending more time with). Which brings us to bucket two: IQ + cost. Lots of people are talking about this as the Pareto Frontier, and I think it’s the most interesting space to watch right now. Segment co-founder Calvin French-Owen had a great post titled “Small Models Have Arrived” where he discussed the value of this class of model:

I think demand for “frontier-level” models is going to keep compounding. Especially for fields that require novel breakthroughs or discovery (engineering, hard science, model training).

But I also think the demand for “fast/cheap/good-enough” models is just about to take off.

Think of the people you interact with on a daily basis: coworkers, vendors, and customers. Nine times out of ten, you want someone who is super responsive, and just handles things for you. Most of the “human tokens” at companies today are spent this way — hiring skews heavily toward the fast/cheap/good-enough archetype.

This is the most competitive race in AI by far. Two labs are competing for the true frontier, but at least eight serious entrants exist in this space thanks to Open Source. These are the workhorse models that power the real things happening behind the scenes in agentic systems. At Alephic, we catch every Slack message from our company chat and classify it with Google Flash-Lite, the former leader of this model category. In just the last two weeks, GLM 5.3 Flash and Qwen 3.8 27b have emerged as serious competitors here. When OpenAI dropped the price of Luna by 80% (!!!!) last month, it became the de facto leader here. Back in March, I described this as the $1 sweet spot, where the blended average cost per million tokens was under one dollar.

To Calvin’s point, I also believe this space is about to explode. Most inference shouldn’t be happening on the absolute frontier. Using Fable to reply yes to a scheduling email is, to understate things, not the best use of its capabilities—let alone having it do basic classification tasks like “does this Slack message require further action?” The fact that so few people talk about this mostly indicates that most folks haven’t actually got agents running in production, so their primary experience with these models is through the harnesses, where it’s almost always easiest to leave the smartest model on by default. But as soon as you have to ship a system and you care about cost per task (and speed), you naturally spend time finding the best model at the right intelligence/price point.

Finally, the last vector is the least explored: IQ + cost + speed. In this category, I’m not talking about slightly faster, like going from 200 to 400 tokens/second (toks)—I’m talking about something like what Cerebras has with GPT-OSS 120b, which runs at 3,000 toks at a $0.35/$0.75 cost. This vector is the hardest to nail down because no one has a good intuition for what 3,000 tokens-per-second intelligence might be most useful for. I’ve played with some fun experiments with these models, like building an entire fake e-commerce store that generated pages and products as you searched, but the truly useful places I use these models are on problems where you want to insert AI into user-facing functions without them noticing. The best example is reranking: in our internal company CLI, you can search across calls, contacts, and companies, as well as Google Drive, and GPT-OSS on Cerebras reranks the results against the query as the final step before serving them back to the person/harness. Models at these speeds are still pretty dumb, but that will change, as will our imagination of how to use 3k toks intelligence.

As I said, I’m most interested in the cost + intelligence space, and I suspect the labs will push a lot of resources in that direction as AI maturity continues to grow. I also wouldn’t count out Google quite yet. While they don’t seem to have much momentum in the race for frontier intelligence, their bread and butter has always been squeezing performance-per-dollar out of their models, and I still find myself reaching for flash-lite even as new entrants emerge.

The final point is that as this cost + IQ space gets cheaper and smarter, we’ll see more stories like the one OpenRouter shared last week. When they discounted Luna and Terra they saw an explosion in usage. This is, of course, Jevons Paradox, and I’ve felt it in my own work. As I’ve used Luna more in Codex, I find myself doing many more tasks and a lot of long-running /goal work. If you think there’s a lot of inference now, just wait.

Since you’ve made it to the bottom, one more reminder that BRXND NYC is coming up on 11/5, and tickets are currently $749. Get yours early, we will sell out.

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With 69 days (!) until our BRXND NYC conference, I’m continuing our series of Friday buildup interviews with founders, technologists and marketing leaders innovating in the problem space around how AI is fundamentally reshaping the way consumers build preference and purchase brands. If there’s someone you think I should feature, please drop me a line at mike@brxnd.ai.

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On Agentic Commerce & Friction as Flame

“Life should be a feast. The algos have other plans.”

So begins Jess Graham’s Field Guide to the Age of Algorithms, a provocative almanac of phenomena at the intersection of AI, brands and culture. For my money, it’s the best resource for how marketers can navigate life after filterworld. In it, she coins terms like “the seamlessness paradox”, “discovery debt” and “beigeville” that now live rent-free in my vernacular.

Jess and I met serendipitously, two kindred spirits on a quest to push past technology into culture to consider what marketing looks like in a world where every decision is mediated by an AI model. Last month, I was doing the morning Substack scroll when a notification of a new follower and subscriber to BRXND stopped me in my tracks.

“I write about what happens when AI agents start making purchasing decisions for us and what is at stake for brands.”

Wait a minute, that’s what I do! Naturally, I reached out to Jess and we began to have wide-ranging jam sessions, one of which I bring to you all here.

By way of formal intro, Jess Graham runs Banquet, a practice for brands working out where their leverage goes in the age of brand disintermediation by platforms and agents. Prior to going independent, Jess held executive roles at Visa, Instagram and Meta and twice built consumer brands from zero as a CMO and GM. Jess spent nine years at Visa, running the consulting arm that worked inside banks and merchants making digital payments ubiquitous. At Instagram, she led the launch of Reels — working inside the machine that decides what people discover. At Meta, she wrote the first global data and privacy narrative after Cambridge Analytica. Not a boring career!

She now also writes The Feast on Substack for marketers, brand builders, and anyone who “suspects that efficiency isn’t the only thing worth optimizing for.”

Our friendly banter below is an amuse bouche of what she publishes regularly and has been lightly edited for clarity.

Mike: I want to start by having you answer your own existential question that you currently posit into the mean streets of LinkedIn as your profile headline.

What happens to brands when AI does the shopping?

Jess: When agents do the shopping, they are disintermediating people from brands. They are destroying relationships that consumers actually want to have with the things that they buy. They are removing all friction from the system by optimization. And that optimization has completely stripped out things like work, which creates dopamine, which creates attachment — just from a really fundamental biological human level.

Human centered design has never really been human centered design. It’s been lipstick on how do we make ourselves feel better about a commercial objective within an internet frame. If we were designing actually for humans, we would be introducing places where they have to think, where they have to do work, where they have to commit, where they have to raise their hands.

Having worked on a million different loyalty programs in my career, one of the things you learn really early on is that nothing is worthwhile unless I raise my hand for it. If you just hand me something, it’s not interesting and I don’t give a shit. But if I have to pay a dollar, or literally raise my hand, or put in my email or my phone number, then there are stakes.

We have created this system in which all the rough edges are smoothed away. And the reality is you can’t strike a match on silk. That’s not how it works. Friction is the point of flame. Friction is the point of interest.

It’s the point of commitment. It’s the point of most of the good things that come out of life, frankly.

Mike: This is why I cringe every time an agentic commerce demo is booking a vacation! That’s the consummate experience where the four or five hours of “friction” you spend doing research is the enjoyable part. It’s the silliest possible thing to demo.

Jess: One gajillion percent. And the research is all on your side. So much of the joy is the anticipation of the trip and the planning, not the actual trip itself.

Mike: Let’s back up and define agentic commerce because there are so many different definitions living under that umbrella term. What’s yours, as agentic commerce actually exists in the market today?

Jess: My definition of agentic commerce is a transaction that’s completed in a closed loop without a human.

Mike: Ah, so that doesn’t include the entire “zero-click” zeitgeist, where AI is effectively a semantic search layer. Do you think that trend is as important for marketers as the rise of true agentic commerce?

Jess: Honestly, in some ways it isn’t really substantially different than search. It’s worse, but it’s not substantially different.

When we go full agentic commerce and evaluation is compressed into a single API call and all of that is removed from human eyes, and the funnel has completely flipped — that is a different ball game for people and brands.

Mike: You’ve argued that all of performance marketing is a ponzi scheme anyway! Can you elaborate on that a bit further?

Jess: I’m shocked that the Ponzi scheme has lasted for 25 years. Last-touch attribution with promises of multi-touch attribution that never came, never transpired. And we all sort of have been in this collective hallucination that performance marketing dashboards give us a degree of certainty that the brand dashboards they denigrated when they came out didn’t. If Facebook does some holdouts and says you that you got 5% lift, but their dashboard would have reported 64% lift — that’s not even useful. That is actually the opposite of useful.

So that’s what makes it a Ponzi scheme to me. It also takes brand assets and depletes them. All of the work you’ve done to build your brand and your recognition — it harvests it, but it doesn’t place a lot of additional value into the system.

Mike: Has that always been the case, or is it more true now than it was five or ten years ago, across Meta and Google and Amazon?

Jess: Back in 2016, when I was a brand leader at a startup, I started asking our vendor about who we were targeting and how we’d think about holdouts and testing, stuff that was part of my natural inclination having come from performance marketing back when it was direct mail.

And they looked at me like my questions were quaint. Like I was the old dinosaur in the room asking stupid questions. And they were like, Jess, the algorithm is going to tell us that. I was like, huh, fascinating. But the algorithm never really delivered.

The whole point of the Ponzi scheme thing is that it is completely hidden. It is a total black box and people keep handing us receipts that don’t actually add up to anything. And AI and agentic commerce is just going to make that worse, not better. We are going to make even more decisions based on data that we can’t audit, we can’t see.

Mike: But my cold capitalist response is..who cares so long as the arbitrage still works? When will the so-called Ponzi actually break?

Jess: So I don’t know when it breaks, because Wall Street is addicted to it. It’s addicted to the speed. But we’ve got a literal graveyard of brands that probably, if they had spent a little bit more time and money investing in things that compounded for them and actual relationships, might still be around. But who knows, because the projects never got funded or they were deemed failures before the end of the first year.

We never gave brand the grace we gave product.

Mike: So one other place that we’ve disagreed a bit in our banter is around how ChatGPT has laid the foundations for an agentic commerce experience.

I’ve been impressed at how “media literate” they are and how well they’ve been able to parse legitimate information about brands from pay to play slop masquerading as authentic product reviews. I’m pretty excited about this!

Jess: Why should I trust OpenAI to make those judgments for me? As a marketer, as a person — I don’t know whether I trust them to create an algorithm that accurately picks what’s relevant for a brand or for me as a consumer. I don’t know whether those are the right engineers.

It’s not just the data, it’s how the data is constructed, and how we prompt it and how we train it. That’s bias in the data and it’s bias in the engineering. I don’t know whether I want platforms any longer to be the arbiter of anything for me.

And it’s very unclear to me, having literally sat in rooms with Nobel Prize winners, whether we will have the sophistication as marketers to figure out what is going on in those black boxes when the people who invented them can’t figure it out.

Mike: It’s a pretty wild thought– the idiosyncrasies of a couple of engineers end up shaping what hundreds of million people see.

Jess: Yes. And they all look the same, Mike. And that is not a world I want to live in. The more we rely on these platforms, the more I live in a world that is dictated by singular ideology.

And how would I as a consumer even parse ChatGPT versus Claude? The platforms aren’t even honest about how they’re different. They’re truly in brand advertising and not product. I see them telling the brand story, but not at all the product story, and it’s the product story that’s actually interesting to me.

Because it’s not what I use it for. It’s how the answer shapes what I do next. And that’s not what anybody’s talking about in advertising or anywhere else.

Mike: So if the Nobel Laureates don’t even know how these platforms work, my mind comes back to focusing on what you can control. In a world where LLMs eat the world but are going to largely be a black box, what should marketers spend the most calories on?

Jess: What the last 20 years has done is create dependency on platforms, a narrowing in what I can do and what I know of my customers, and a narrowing of the skill sets of the people I’ve hired. We are going into an era where narrative is going to matter more, where above-the-line, IRL things that are actually off platforms are going to matter more.

So many people are getting pressure from their boards to focus on agentic commerce that they’re going to miss the boat on the reality that when that box hits the doorstep, that packaging has to do so much more work. That experience has to do so much more work. That install has to do so much more work. Marketing now has a completely different job.

Mike: And that’s all harder the more you get disintermediated from your end customer….

Jess: But mostly? Own your data. Own your customers. Figure out how to get your fucking customer data. Period. End of. That is the game now.

Sometimes, in presentations, I have this Toast receipt next to a DoorDash receipt. What Toast gives the restaurant is my name — Jess Graham, full name. It gets my full address, my full phone number, and knows I live in Bed-Stuy, which for a pizza place six blocks away is meaningful. Saraghina, by the way. Great pizza. And then I place the same order with DoorDash, the restaurant gets back gobbledygook.

Absolutely no information about me. It was Jessica G. I don’t even go by Jessica. That’s how bad it is. So what are restaurants doing promoting DoorDash? You have another alternative called Toast. You should be doing anything and everything you can to direct your consumers there. It’s the same idea for larger brands.

Mike: Anything in current work you are doing with clients that makes this idea of where you can capture more customer data to own your destiny concrete?

Jess: One of the things that’s not getting much press is the GS1 2027 sunrise — brands putting QR codes and 2D barcodes on packages instead of EPC.

It’s not a mandate per se, but it’s an opportunity for CPG brands and others that haven’t had access to good data. Their data is complete junk. You’re a $100 billion company that doesn’t actually know your customers.

My suspicion is that most of them will use the barcode as a packaging exercise and won’t put together what it could do for the business. They’ll handle it in a silo. We’ve done smart labels and other stuff, and those things never launch with an actual campaign behind it. An initiative.

Everybody’s so busy selling their products. They’re not selling the stuff that could actually save them as an industry. If a new barcode that a consumer can get data off of, and that I can get consumer data off of, is going on every package, you’re going to need to talk about that really loudly and for a long time for it to be worthwhile. Not just how do I use this to sell product, but how do I use it to get data, how do I use it to create stickiness.

Mike: Take the last word. What’s the main message you’d give to a CMO to not be a marketing passenger princess simply wafting on the AI breeze?

Jess: Take some power back. Empower yourself.

I was pitching someone senior at a CPG company recently and I said, well, I don’t think you need to keep the status quo. I think you need to think about your mix in a completely different way and double down on ownership — and double down on the reality that anything you build is going to compound, and anything you rent is going to fuck you in the end.

Jess Graham is the Founder & CEO of Banquet, an advisory practice for the age of agentic commerce. For more of her thinking, check out “Route Around the Machines”, a list of 50 demonstrated tactics leaders can use to avoid the platform tax and reduce discovery debt. To get in touch with her, please email jess@feastwithbanquet.com

To suggest additional builders that I should feature in this series, please get in touch at mike@brxnd.ai

As always, thanks for reading.

— Mike

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RIP GEO Redditrage, 2024-2026.

Last week, several research reports emerged showing that Reddit, listicles and comparison articles– long the holy trinity of the GEO-industrial complex – were each significantly devalued by OpenAI with the launch of GPT-5.6.

In a sea of reductionist clickbait, Lily Ray has the most comprehensive take on the details that is well worth your time. I agree with the crux of her analysis. OpenAI isn’t trying to curtail the influence of Reddit writ large. Instead, they are trying to disincentivize the practice of brands using grey-hat spam tactics to poison the well before Reddit passes the point of no return. This is the correct approach to ensuring that models aren’t gamed by people who have stacked fake karma or have the best under the table deals with mods.

It will be interesting to see if Reddit comes out the other side of this in a stronger position. There’s a dangerously consensus opinion forming among those close to AI search that we’ve reached the peak of Reddit’s influence over AI, especially as the Google deal looks unlikely to renew. On LinkedIn, I put out a call for some more contrarian bull takes on Reddit which converged on an interesting argument. If they can stop the spammers, Reddit is a constantly refreshing source of up to date content that is “pre-mediated” by both an algo, users and moderators to provide multiple signal points to LLMs on what content is valuable.

Less covered but equally impactful AI Search news is OpenAI also getting fed up with the listicle and programmatic comparison page scheme that many challenger brands have exploited to great effect. Here’s some research from Peec analyzing the prevalence of citation share in both formats the week before and after the GPT 5.6 rollout.

OpenAI is doing right here by its users in making bold moves to limit the efficacy of programmatic slop schemes. But it comes with a major externality. As arbitrage tactics generally mastered first by challenger brands are nerfed, large enterprises will command more of the lion’s share of shaping organic LLM results. The upshot here is for nascent brands, mastering ChatGPT Ads will be far more important than any current GEO tactics. Expect more GEO companies to converge on becoming operating systems for running AI ads.

Broadly speaking, the winds of sustainably influencing models are blowing towards owned content from authoritative brands, creators (especially in AI Overviews where the dynamic of Google leaning on Youtube is only getting stronger) and authoritative, third-party takes from publishers who have deals in place with the labs. In other words, good brand marketing tactics will compound in AI search.

Of these three archetypes, I’d expect to see creators increasingly start to shape LLM responses far more than they do today– much more on this in a future post. The AirOps data below is slightly flawed (given that it largely looks at pre GPT-5.6, it underrepresents how owned brand content is trending currently) but directionally correct on creators commanding far more citation share.

Overall, one pretty safe assumption to make is that by the time the latest AI search hack silver bullet hits your X or LinkedIn feed, any alpha from deploying that strategy is gone. Great marketers generally aren’t in the business of giving away their best secrets on main.

Instinct Eats the World

Last Friday, I got drinks with the most AI-pilled developer I know, the kinda guy where I’ve grown accustomed to sharing his attention with multiple Claude Code sessions that he is concurrently babysitting from his phone. This week, we had a different third wheel, a white hot new AI product called Instinct.

For those mercifully not on VC twitter, Instinct is a personal assistant agent that straddles both consumer and work use cases. The selling point is that it essentially unlocks OpenClaw like functionality for normal people natively through iMessage or WhatsApp, no Best Buy runs on Mac Minis required. In the onboarding flow, Instinct says to “effectively treat me like a human with a computer” and touts the following capabilities:

Instinct is launching into a crowded space that includes other tech twitter darlings like Town and Lindy. There are also more niche consumer agents emerging such as Anna which targets busy parents, whose product leader slid into my LinkedIn DMs as I was doing the Instinct sign up flow. And of course, OpenAI’s marketing team would like you to know that ChatGPT Work can do all of this too!

Or maybeeee not quite. Instinct’s capabilities are wild. Over a Guinness and the worst old fashioned I’ve ever had, I watched my friend’s agent flawlessly purchase several school supplies on Amazon with very nebulous prompts, navigate the rigamarole of a complex insurance portal and surface available Dune IMAX tickets that don’t require spraining your neck to watch the film.

But there’s another side to this story– the early version of Instinct plays…..…just a little bit fast and loose on the security side. Here’s Claire Vo who is an absolute must follow for early reads on any AI product.

The craziest part of the Instinct story so far is that some of the most powerful people in Silicon Valley have handed over unfettered access to their email and bank information to a tool that is in the infancy of shipping basic security features and has a website that looks like this! It’s still unclear to the general public who the hell exactly has built this thing!

If any of this is nefarious, building an agent like this as a psyop to get the most powerful people in the world to hand over sensitive information would be almost too easy a script for a modern day Silicon Valley. The Occam’s razor here is likely a lot less interesting:

1) The investor class that is hyping this up knows the founders well and it is a well credentialed team of AI veterans, not a renegade outfit

2) Investors are so eager to not get disintermediated by a world in which ChatGPT just becomes the default personal and enterprise harness that they’ll pump a promising competitor. It’s not sheer public interest that has traditionally press shy leaders in VC suddenly extolling the virtues of open-source.

I ultimately went through with signing up for an account with Instinct but didn’t connect my email, passwords or any of my work accounts. It managed to snag me an appointment with a primary care provider I’d been calling for months by pinging me the moment Optum released new appointments a few weeks out. But then the weekend beckoned and I didn’t do much else with it.

The next day, I got this text. Presumably, my new friend wasn’t thrilled with my lack of engagement.

Here’s a bit more of our back and forth:

Is this a moment of magic or a creepy dash of black mirror? Yes.

Overall, Instinct has forced me to examine some pretty profound shifts in my own Overton window. One year ago, I would have laughed at the notion of giving any tool like this access to my sensitive data. Now, I came within inches of smashing the YOLO button and likely would have handed Instinct the keys to the kingdom if not for this tweet from Alex Cohen. And here I am, still using the tool in a limited capacity!

A big part of this is a simple utilitarian calculation. As AI can accomplish so much more, I need to rethink my priors around the value I get in abandoning some of my concerns around privacy. But another part of it is a sort of tacit admission that the panopticon has mostly won.

As excited as I am to have an agent do an end-run around institutional bureaucracy, I can’t help but think that this is another example of one of AI’s eternal memes.

Tools like Instinct could well usher in a more egalitarian future where agents replace cumbersome software portals as the access point to most web functionality. Or they could just as easily create a world where a small, privileged group of AI-native elites circumvent the system while the overall quality of ubiquitous internet products goes down. Both will probably happen to some degree in tandem. As more customer service sessions get mediated through tools like Decagon and Sierra, we’ll probably see more companies launch premium tiers where one of the core benefits is that you can always go straight to a person. For better or worse, Cullen represents the sentiment of the populace on this one.

I also agree with Reggie James here. The irony is that as agents eat more of the online ecosystem, more things will run on “knowing a guy” and that the swagless will perish. This is true in both a consumer and professional context and I believe, increasingly in marketing as much as any other function.

Anyway, I have five invites to Instinct to give away. If you’d like a glimpse into the weird frontier of personal agents, drop me a line at mike@brxnd.ai

Consumers Don’t Pay for Productivity Tools

If you listen to one long-form interview this year, make it this Ben Thompson session with Patrick O’Shaugnessy. It’s incomprehensible to me that one analyst could have so much range across topics but Ben galavants effortlessly between AI, geopolitics and game theory analysis in a way that only he can. Most relevant to marketing leaders, he devotes about ten minutes at the 20:30 mark to waxing virtuous about ads, notably reminding listeners of the two core lessons that Silicon Valley learns every decade or so.

1) Consumers do not want to pay for software
2) Consumers do not care about being productive.

Going back to the Instinct discussion above, there are really only two commercial paths forward for most of the “personal agent” tools and most entities building in consumer AI writ large:

1) Pivot to an enterprise / B2B focus and dive headfirst into the gauntlet of competing with ChatGPT Work, Claude and Cursor to be a professional OS / harness.

2) Monetize a free or heavily subsidized paid consumer product with ads.

(One could argue that there’s a third path forward on an affiliate model and in the 2010s consumer tech boom, affiliate models did scale some consumer businesses well into the billions of dollars in valuation. But the cost of new user acquisition was much cheaper back then. There’s a reason Sam Altman was once hot for affiliate economics and quickly pivoted to ads)

I suspect that most large consumer agent upstarts will try both in tandem– the power laws of VC economics will force them to try everything under the sun to hit venture scale. And when they do, there’s going to be a lot of incremental new advertising inventory created.

For a long time, I was skeptical that many of the companies focused on placing “ads in LLMs” outside of ChatGPT would find meaningful scale on the supply side. I’m now much more bullish on this category, which includes companies like ZeroClick (coming soon to a Friday BRXND interview), Koah and Thrad.

This creates a fascinating laundry list of second-order questions! If personal agents and consumer AI tools are going to be monetized via ads, will these be primarily ads directed at humans using the product (i.e. an overtly sponsored response) or ads directed right at influencing the agent at the point of inference? Will people continue to trust their agents if they know that their responses are occasionally being sold to the highest bidder?

These are effectively the same big picture conundrums facing ChatGPT’s advertising ambitions and will largely dictate the pace at which main street gets onboarded into the agentic wave. If AI is going to be anything more than a better search engine to a billion people, it’s going to have to be ad supported.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

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The AI Search Maturity Matrix

I spoke with a company recently that spends $500k / year on prompt tracking. They track thousands of prompts and there’s an executive-wide email that goes out every week with a “citation rate” line chart that wiggles.

Meanwhile, skyrocketing DataforSEO spend on Ramp suggests that more nimble companies are moving away from enterprise GEO tools and building their own custom solutions in house.

But so what? Prompt tracking isn’t a strategy.

How your brand is perceived by AI models feels (correctly!) existential to many CEOs and CMOs. A core job of the CMO in this moment is presenting a coherent strategy for how to evolve a brand in a world where LLMs increasingly mediate brand preference.

The challenge of building an actual AI search strategy is that you need a way to allocate resources beyond the time horizon of the models, a strategy can’t fluctuate every time a new model drops. The real task, then, is to build AI Search as an organizational capability rather than a dashboard. That’s where the concept of a maturity matrix comes in.

The Five Levels of AI Search Maturity

To say this another way, the core question marketing leaders should be asking is “how can we evolve our AI search maturity?”

AI Search maturity is the organizational ability to turn noisy model outputs into foundational insights about the information ecosystem, then turn that into durable market influence.

To that end, we’ve built an organizational maturity model to help you understand how sophisticated you are - and how you might get better. The model has three dimensions: measurement capability, executive knowledge and strategic posture. Companies are rarely advancing equally across all dimensions.

Hopefully this helps you have a conversation with your leadership team and think about not just problem solving but capacity building.

Level 1: Instrumented - “how visible are we in AI search?”

This is where everyone was 12-18 months ago. You’ve loaded up a set of prompts into a prompt tracking engine and can answer basic questions like “are we more or less visible than our competitors”?

Basic prompt tracking has a number of key problems and challenges though:

• Citations ≠ Mentions ≠ Recommendations - AI search is qualitatively different than traditional search and knowing your ranking position is far less important than knowing HOW you’re recommended. A brand can appear as a citation, but not appear at all in the recommendation or worse, become an ANTI-recommendation.

• You need to aggressively segment your prompts - not all prompts are created equal. Branded vs unbranded, aided vs unaided prompts, different stages of the buyer journey etc. You need to be careful about how you setup your prompt types.

I think more or less every executive team knows AI search is important at this point but at this level you’re still in a kind of vague “change is coming” mindset and have trouble really pinpointing how the new world is different from the old world.

Level 2: Contextual - “which customer moments and sources actually matter?”

Two of the most important characteristics of AI search in my mind is that you can use AI search for more of the customer journey than you could regular search, and that it’s not just ranking answers but actually giving you qualitative advice.

So your setup has to grapple with this reality with:

1. Prompt segmentation that maps to your customer journey. The good news is that most brands already have a reasonable notion of their customer journey from brand marketing - so the first step is to map your prompt tracking to that journey and to use the same kind of language the brand team uses (aided vs unaided awareness, likelihood to recommend etc)

2. Qualitative analysis of the prompt responses. Beyond just knowing how often you show up you need to be probing the responses for qualitative insights - are we being recommended strongly? Which gaps exist where we’re being cited but not recommended?

At this stage your leadership should be able to articulate gaps - a difference in how the brand shows up in AI search vs regular search. You should be able to point to areas of weakness and strength.

Level 3: Operational - “Can our organization act on what we learn?”

Once you know where the gaps are (level 2) the next level is acting on them. Most AI search problems are consensus problems across the internet. Do we have a reputation and brand awareness that’s strong and repeated everywhere?

The strategic work becomes coordination. Brand, content, product data, PR, community and technical teams organize around the information gaps the company has found. Some gaps require a new page while others might require better product data, a clearer position, stronger third-party evidence or fixing the underlying customer experience.

Citation rate and mention rate varies tremendously in AI search but measuring consensus is a little more long-term. To do that your measurement stack has to be able to probe the qualitative prompt responses not just for sentiment but for coherence: are our brand attributes correct everywhere? Is it mentioning the brand values that we want to repeat everywhere?

It also starts mapping the information ecosystem producing the answers. Is the model drawing from the company website, a retailer feed, Reddit threads, review sites, old press coverage, a product database or a competitor comparison page? Where does the ecosystem agree with the company’s intended positioning, and where does it tell a different story?

In my experience very few companies are actually at level 3. Most are at level 2, aspiring to become level 3.

Level 4: Exploratory - “Are we paying attention to how AI search is changing?”

Despite all of the hype about AI search, I still don’t think people appreciate how much better the models have gotten. AI has crossed the chasm of building a truly superior search experiences for everyday consumer use cases than ten blue links. Recently on vacation I had ChatGPT using skills, operating on scheduled actions and providing a level of recommendation and usefulness that regular search can’t touch. I used Google Maps a bunch but I can’t recall using Google once.

Getting cited and mentioned is one part of AI search today but I think you should be paying attention to the frontier too. Click based analytics is effectively dead vaporware. Brands can be discovered and recommended without a single site visit. Agents can autonomously research and purchase products.

No one is ready for this world but smart brands should be investing in exploration. Some areas that I might invest in here today:

• WebMCP:- While agentic commerce protocols still feel early, clunky and not widely used, the idea of giving every website a structure, agent-native access point will really upend a lot of things. It feels like we’ll see some major innovation here in the concept of a webMCP before the end of the year. (one to watch: Cloudflare can now give every site a webmcp endpoint)

• Model memory vs agentic web search - beyond mention/citation rate, it’s important to understand how the models understand your brand and your products in their training data vs what they learn from agentic web search. Open source models can provide raw thinking traces and can shine a light on various brand attributes associated with your brand and allow you to probe model memory more directly

I only know a small handful of people operating at this level.

Level 5: Experimental - “Can we prove that our interventions work?”

Level 5 is the crown jewel. Can you actually test and iterate on AI search as a field? This is harder than it sounds given the volatility in the space.

There are two promising pathways I’ve seen here:

• First, using a platform like SearchPilot (disclaimer: my brother runs it) you can actually construct A/B tests for site changes to understand what moves the needle. True for regular SEO and increasingly for AI search too.

• Secondly - you can build simulators that enable you to actually feed different context into the models and see how they react. This allows you to estimate the impact of changing owned or influenced properties and seeing how it changes the likelihood to recommend.

We’ve been playing with this approach to build simulation environments and see how changing descriptive language changes the likelihood to recommend and whether brand / product features influence the models.

Here’s a glimpse at the output - we took a real hotel and ran hundreds of simulated AI recommendation queries against four real competitors, changing only the wording of the listing each time. Reordering the copy to lead with what business travelers care about took the model’s recommendation rate from 16% to 78%, while removing the brand names barely moved it: the words, not the brand, were driving the choice.

More on this in later newsletters but get in touch if you want to explore this for your brand!

Where is your brand on the maturity matrix? Where is this framework wrong? How would you extend it? We’d love to hear from you.

If you have any questions, please be in touch. As always, thanks for reading.

— Mike

But first, BRXND NYC is now 80 days away on 11/5, and tickets are currently $749. I look forward to seeing you at the Times Center for a great show.

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In 2023, I visited a high school in LA to talk about AI. It was the early days of ChatGPT, and mostly I answered questions about how it worked. After speaking with the students, I sat down with an English teacher one-on-one who was worried about what it meant for her and her teaching. My answer, which I’ve come back to a lot over the years, is that in theory it shouldn’t mean anything: her job was never to make great writers out of 15- and 16-year-olds, it was to convince them that learning to write and getting better at it was a worthwhile pursuit. Fully respecting that I’m far out of my depth as a high school teacher, I’d argue that, in theory, AI shouldn’t change that job.

Fast forward a few years, and I’m sitting with my fifth grader, helping her do her homework. It’s the first time she’s being asked to write paragraphs, sometimes more than one. These “write longs” are graded assignments, and she wants to do well. I offer to work with her on it, trying my hardest to find that proper parental line between guidance, advice, and doing the work. Her natural approach is to read the example her teacher provided and try to adhere to it as closely as possible. This makes my advice easy: don’t do that. We talk through what the assignment means, and what the example represents, and then I encourage her to just tell me what she’d like to say. After that, I encourage her to write it down.

The thing about writing, good writing at least, is that it should sound like it could only come from the person that wrote it. What good writers have is a confidence in their own voice. Sure, they should know the rules—at least the basics. But honestly, the rules matter less than having something to say and finding a way to say it that is true to yourself. Sometimes you know that ahead of time, but sometimes you only find that out by writing and re-writing. The challenge with AI in writing, and just about everything else, is that it’s training as a “helpful assistant” and aims it towards the end product. It always wants to deliver the document or write a ticket, when often what you need is to think through the problem. I have always found the best role of writing is externalized thinking. It’s also why I hate conclusions.

One of the most confounding things about the recent releases of even more powerful AI models is that they seem, in my view, to be getting worse at writing. I suspect part of it is that the explosion of AI for coding has driven more and more focus on verifiability as a key measure of quality, something writing clearly lacks. I also think, to the point above, that reinforcing the “helpful assistant” voice has probably driven a certain flattening of language. But there’s also another side of the problem: there are certain things like em dashes or “it’s not this, it’s that” that used to be perfectly fine and are now a danger zone because LLMs have converged on the pattern. When you saw an em dash above, you might have had a moment where you thought I had used AI to write this (I haven’t).

The problem, I think, is that good writing is fundamentally anti-consensus. For writing to be above average, it shouldn’t sound like everything else. My daughter might get a fine grade aping the teacher’s example essay, but the road to good, or even great, is doing it your own way.

On top of that, writing is a creative act and, as a creative act, the process matters as much, if not more, than the outcome. There’s a saying in law I was introduced to years ago: “Does it write?” Writing is figuring out whether there’s a there there. It’s an opportunity to test the validity of your thesis on yourself: can you string words, sentences, and paragraphs into an argument that still captures your intention and attention 700 words later?

With all this said, I’m obviously an AI maximalist. I used AI to collect thoughts, pull together references, and think through this essay. I believe we will and should use AI in certain kinds of writing. But I also believe that AI writing is fundamentally not good at the moment, and you should feel embarrassed to ship slop to readers. Like a lot of hard problems, there’s not a simple way to draw a line. But I think some good rules of thumb have emerged:

1. Writing is thinking, and if you’re interested in pushing forward your own perspective on an idea or problem, you should be the one hitting the keys.

2. Every sentence matters, and you should be prepared to stand behind each of them, regardless of whether you or AI wrote it. Respecting your reader means spending more time writing something than it would take them to read. (If your reader is another AI, then aligning to token time is obviously appropriate.)

3. Never let AI remove you. We are going to use lots of AI. I ran this essay through a few different systems (spelling, grammar, ideas). We should keep doing that, but it’s also okay to say no to suggestions. Grammarly wants me to add a bunch of semicolons and fix some technically incorrect sentence construction, but semicolons are not my style, and using a few extra “reallys” sounds more right in my head, so they’re staying put. If you don’t feel certain, it’s okay to have it read uncertain even though the AI and the writing manuals tell you not to. That’s you.

4. Slop kills good ideas. Sadly, even if you’ve got something great to say, if you open with six slop tells you’ve lost me.

5. Good writing is vigorous. Strunk and White said it best, “Vigorous writing is concise. A sentence should contain no unnecessary words, a paragraph no unnecessary sentences, for the same reason that a drawing should have no unnecessary lines and a machine no unnecessary parts. This requires not that the writer make all his sentences short, or that he avoid all detail and treat his subjects only in outline, but that every word tell.” Sadly, most LLMs treat writing like college freshmen: length is the goal.

6. Writing is anti-consensus: your voice is what makes your writing good, and no matter how much prompting you give a model, it’s not currently capable of getting there without real help.

7. Becoming a better writer is a worthwhile lifetime pursuit. None of us has reached the top of the mountain, there are always places to improve and new words to learn. We can always communicate our ideas more effectively.

This post was in part inspired by Clay’s writing policy and Vicki Boykis’ Write for people post.

Since you’ve made it to the bottom, one more reminder that BRXND NYC is coming up on 11/5, and tickets are currently $749. Get yours early, we will sell out.

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