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Things I Think I Think About AI (2026 Edition)

1. Everyone is going to be working in harnesses (Codex, Claude Code, etc.) in the future.

2. I’m not sure if that harness-first future is 6, 12, 18, or 24 months out.

3. If your primary experience of AI is still in ChatGPT, Claude, or Copilot, you have no idea what these models are really capable of.

4. Don’t count out Google and Microsoft in the harness race quite yet. I still think the fundamental question is whether these model providers can build great interactive surfaces before the interactive surface providers can figure out how to properly integrate models.

5. Anyone who says AI can’t do useful work is lying to themselves or to you.

6. Fable is the best model on the market, and I don’t think it’s close.

7. Fable is the first model that feels smart enough that you shouldn’t waste its time having it write simple code. Its best use is as a planner.

8. There are only three real vectors to judge models: Raw intelligence (frontier foundation models), cost-per-intelligence (value models), and tokens-per-second (there’s some minimum bar for intelligence here, but it’s low), everything else is competing for scraps. The frontier foundation models are where we all pay the most attention (Fable, Opus, Sol, etc.), but I think the most interesting/dynamic space right now is cost-per-intelligence, where you have Gemini Flash-Lite, 5.6 Luna, and open source models like DeepSeek Flash v4. These are workhorse models and play a huge role in almost any large-scale AI project.

9. I still think the hype around local models is far from the reality. The only thing I find myself using them for at all is a tiny bit of local search/embeddings stuff and almost nothing else. The good open source models are too big to run locally, and everything is just too slow to make it worth it (before you ask, I have an M5 Max with 128 GB RAM and a DGX Spark in my home rack).

10. Codex Desktop (now ChatGPT) is awesome and lowered the barrier to entry on harnesses in a really powerful way. I don’t understand why everyone isn’t using it all the time.

11. The tokenmaxxing conversation is stupid: the vast majority of companies should be spending way more on AI, not way less.

12. The best way to learn AI is to buy a $200 Claude or OpenAI plan and try to exhaust your limits.

13. There are a lot of companies that are trying to optimize their AI spend before they have any. That is a dumb mistake that will come back to bite them when, in a year, they still have no one using AI.

14. While token leaderboards are a blunt object, generally tracking adoption and making sure teams are using the technology is a good thing to do. Obviously it can create bad incentives, but the more damaging incentive is individuals doing nothing.

15. The number one challenge with using AI to write code isn’t buggy code, it’s misalignment with vision and architecture. These models are already just as good (if not better) than your average mid-level engineer, but they’re terrible at deciding what’s valuable to work on and staying in line with the technical architecture that’s been laid out without quite a bit of coaxing.

16. There are two primary modes for using AI: divergent and convergent. The latter is for problems where you know what you want, and you should be able to just run it through some kind of system and let the AI take care of it. If it does a bad job, it’s almost always a failure in how you designed the system, not a failure of the AI. The former is how you use these tools to think through new problems and ideas, and it’s a much less well-explored space. Here you don’t want to give over judgment to the model, you want your taste and ideas incorporated. This is one of the most interesting problems to solve in AI over the next twelve months, and we’re starting to see little signals of it in Claude Code and Codex (live artifacts).

17. The build vs. buy decision inside enterprises is at a very interesting moment. In most cases, I think it’s fully flipped to the point where build is the incumbent and buy has to make a case rather than vice versa.

18. If you’re not thinking about a company/brand brain, it’s a good time to start. Building tooling for the harness-first workforce to have access to all the systems and data it/they need is critical.

19. To that end, we’ll be talking more and more about agent experience (AX) and what it looks like to truly design agent-first. One of the most interesting questions in this realm is where AX drives you to make different decisions than UX/developer experience (DX) would. One obvious place is defaults. The rule of thumb we’ve set is to default these systems to human-readable output because the agents are much better at navigating complicated CLI flags and options.

20. Memory in chat/harnesses still doesn’t really work yet out of the box, and I’m not positive it ever will.

21. A lot of the productivity gains we’re seeing from AI are actually really smart people getting much better at using the models.

22. Evals still aren’t trustworthy. I frequently find that a model that has lower eval scores does better in real-world work than the maxed-out one.

23. I continue to be surprised that the new models seem to be worse writers, not better. I suspect there’s a mix of reasons for this, including the move to code/verifiability. But fundamentally I’m beginning to believe it’s because writing is fundamentally anti-consensus: if everyone is saying the same thing, it is no longer a good or interesting thing to say.

24. There’s a new model of work developing where you start by doing it with the AI, harden it into a skill, share the skill out to others, and then, when it’s sufficiently tested, figure out how to turn it into software. This seems like a pretty durable pattern.

25. In the past, the answer to “should we rewrite this from scratch?” was almost always no. That’s no longer the case.

26. While the labs train the models, much of the most interesting work is still happening at the point of execution where regular users find new capabilities by pointing them at their real work.

And a few from the original that I still think fundamentally belong:

27. We will continue to be surprised to find out that, without any specialized training, transformer-based models can solve many problems that specialized models were built to solve.

28. People are the bottleneck in enterprise AI adoption, and it will stay that way for longer than anyone expects.

29. If models didn’t progress from what we have today, we’d still have 10 years of runway to integrate their value into corporations and society.

30. When it comes to AI, don’t trust anyone who sounds too confident.

31. The value of using AI isn’t that it gives you great foresight into the future of how AI will evolve; it’s that it gives you an uncanny ability to sniff out everyone else’s BS.

32. AI is underhyped.

And finally:

33. This whole field is so fun. I can’t believe I get to do this for a living.

Editor’s note from Mike: We would love to hear any stream of consciousness thoughts about AI on all of your minds. Please feel free to build on the discussion in the comments or email me any takes at mike@brxnd.ai and we’ll incorporate into future editions of BRXND. Thanks, as always for reading.

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An Inflection Point for ChatGPT Ads

There’s a bizarre bearish narrative around ChatGPT’s advertising ambitions making the rounds, lampooning the company’s internal projections of building a $100B ads business by suggesting the entirety of the “ad market for chatbots” is $5B. Here’s Eric Seufert last week:

An alternate information space seems to have developed around the chatbot advertising market that has led people to believe that OpenAI’s advertising product is an abject failure, ads in chatbots are fundamentally nonviable anyway, and the entire category will never generate a meaningful amount of revenue.

Like Eric, I find this perplexing for a number of reasons, most of all the fact that it’s near impossible to predict what form factor OpenAI’s core product will even take in twelve months. ChatGPT was a happy accident in the first place, and OpenAI has proved in the last week that it has no qualms about upending its entire user interface to pursue grander ambitions. Codex made a change to its core UI every single day last week.

While we talk about OpenAI speedrunning the Meta ads playbook, an under-appreciated aspect of OpenAI’s culture is how willing they are to make wholesale product changes with the confidence that they have a sticky enough value proposition that users will begrudgingly adapt. Few companies possess that level of product-market fit and audacity.

To this point, ChatGPT Ads has been a lean MVP iterating and shipping tentpole features at a frenetic rate. Three months ago, this was a closed beta selling a single creative placement at $60 CPMs! Thus, the only data point that has mattered so far about ChatGPT’s ad product so far has been the lack of user attrition and user engagement declines when ads are shown. In May, OpenAI reported “no impact on consumer trust rates” from ads being shown and has gone full damn the torpedoes in ramping up efforts to scale their ad business, hinting that the internal data backs up their public statements.

Now, things are about to get real. Last week, OpenAI emailed active clients announcing two marquee features—conversion optimization and app install attribution. Combined with the conversion API/server-side tracking, pixel and self-serve platform launch in May, OpenAI has now shipped the minimum viable product for direct response marketers to optimize for performance on the platform.

Now that conversions can be tracked, there’s going to be a natural obsession here to focus myopically on whether ChatGPT ads “work”, which is to say, whether they meet a brand’s ROAS goals in Q4 this year. Ignore that noise. Historically speaking, every platform that has reached humanity scale (i.e. a billion plus users) has managed to bolt on a highly performant ads business eventually. With an armada of Meta execs at the helm, OpenAI will as well.

The far more interesting conversation is around whether OpenAI can continue to scale a more aggressive ads product without flying too close to the sun on violating user trust. The design of the beta was intentionally minimalist, as if it was almost an advertisement apologizing to the user for being there. It’s likely that future ads will be far more intrusive – will they add enough value and serendipitous new product discovery to ChatGPT sessions that users will accept their presence? This still very much remains to be seen.

The other narrative worth watching closely is around when OpenAI begins experimenting with ads that are designed explicitly to persuade agents. To this point, all of ChatGPT’s (public-facing) ads vision assumes a human buyer and most of its ads pricing is moving to a cost per click model. Agents, of course, do not click. They don’t really view ads either. We’re in the earliest innings of “advertising to agents” taking shape as a concept and the most fundamental questions around what the business model will even look like are still wide open.

Per Juozas Kaziukenas, OpenAI is now experimenting with an ad format that will connect users with custom business agents rather than clicking out to external websites, perhaps a baby step towards thinking about how agent to agent flows will take shape.

The obvious tradeoff here is that OpenAI is asking advertisers to embrace a new level of disintermediation, unless marketing leaders can find a way to make their agents a true representation of their brand. To date, most agent workflows have focused on getting the basics right. The first order problem is nailing price, stock status and basic contextual questions consumers will ask about a product or service. But for marketers to hand more of the keys to agents, they need confidence that agents will operate as a natural extension of a company’s brand. This is a much murkier challenge and one that I suspect will become an incredibly hot area in enterprise AI adoption work.

In any event, the utilitarian promise of a successful ads product in ChatGPT far outweighs the risk posited by the cynics. Specifically:

1) A performant ads network in ChatGPT would be one of the most positive-sum, egalitarian drivers of growth— especially for small businesses both digital and analog– to come out of this era. If ChatGPT Ads does reach $100B of revenue by 2030, it will do so by driving hundreds of billions of dollars in growth across the economy. This success would also create vital competitive pressure on Google, Meta, and Amazon, who right now have enormous market power to raise CPMs.

2) The faster the ads business takes off, the bolder Sam Altman and OpenAI can be in their frontier bets, especially once OpenAI faces the whims of the public markets. I don’t think Sam took Ayahuasca one weekend and suddenly reversed his dogmatic contempt for ads. Someone showed him the financial model and cooler capitalist heads prevailed. The ability to generate digital rectangles out of thin air and sell them at 90%+ gross margins is truly one of the miracles of capitalism and it can help ensure the onramp to AI remains free for hundreds of millions of users.

3) The greatest career opportunity for up-and-coming hustlers in marketing will likely be to position themselves as closely as possible to the “ads in LLMs” zeitgeist, which will spawn both generational one-man businesses and venture-backed entities. It’s a hard time for many of the classic entry level positions in media and marketing– conventional media buyers, entry level performance marketers and all of the junior support functions brand-side and agencies alike are under intense pressure from AI. A net new platform rapidly gaining scale,, especially one that gen Z grew up with, would go a long way to filling the entry-level pipeline with marketing jobs.

To borrow inspiration from a famous presidential letter, ChatGPT’s success with ads is our industry’s success and I’m rooting hard for them.

Consumer AI’s Claude Code Moment

Across a lot of the more normie quotidian use cases, there’s a pretty defensible argument that the lived experience of using AI has been stuck in neutral over the past year. This is an interesting paradox– AI products can do so much more than they could a year ago, across both work and everyday personal use cases. Browser and computer use in particular unlocks fundamental changes in how people can interact with technology. In just a few months, computer use has gone from mystifyingly frustrating to near magical.

But it’s worth remembering how little of the population has experienced any of this wave in the grand scheme of things. Cross-referencing some data from ChatGPT for Work, Claude Code, Cursor, and Github, a reasonable estimate is that around 15-20M people are using coding scaffolds and advanced agent tools, many still within the boundaries of significant enterprise restrictions.

There are roughly one billion knowledge workers in the global workforce. At best, 2% are operating like most readers of this newsletter.

If your frame of reference for the “AI boom” of the last year is purely based on using mass market consumer products and trying to get Copilot to reference the Word document you’re working in, I can understand how you might be skeptical of what your AI-pilled friends are telling you! None of this is manifesting in your life!

Sam Altman does AI no favors here– he’s built a product that can automate the most mundane aspects of daily existence and magically free up more time to spend with loved ones and he cites an example of outsourcing parenting to AI as the utopia?

Beyond “better search engine”, the main consumer AI use case that has gained mass-scale traction is companion apps. Character AI has ~22M monthly active users, with average sessions in excess of 15 minutes. As a category, companion apps have around 50M monthly actives, with about 68% opening every day. It bears remembering that for how many people interact with AI, ChatGPT 4o was the pinnacle of the experience.

Much like adoption in the enterprise, consumer AI is no longer a raw intelligence or technology problem. In my first-ever piece for BRXND on rethinking agentic commerce, I wrote that “what’s been missing to date is a heavy dose of panache, audacity, and first-principles thinking in designing new front-end interfaces for AI.” This is still the first-order challenge across most consumer use cases.

It’s hard to say exactly what the catalyst will be, but I think we’re on the cusp of a massive step forward in consumer AI akin to what Claude Code and Codex brought to the enterprise. One interesting model for how to think about this is that in a year, the vast majority of knowledge work will happen in a harness and the world isn’t ready for what that will look like. What would it take for the same to be true of consumer AI use? I expect a couple of things to transpire slowly, then all at once.

1) The mobile harnesses will get much more user friendly. Codex Remote launched in May and already provides a model and harness combo that can run locally on an iPhone without taking up a gargantuan amount of memory. Voice mode has suddenly gotten really, really good. For now, OpenAI’s UX makes it hard for any non-Codex user to understand exactly what this means for their phone’s newfound capabilities but this is more of a marketing and design challenge. Thus, a really fun story in AI will be whether ChatGPT can launch their consumer super app before an open-source model provider builds a harness that is far more normie friendly.

Apple is in a fun position here. The company ostensibly “missed” the AI boom by not building or acquiring a foundation model and has seen its market cap rise by $2T (!) since the start of the year to reclaim the title of most valuable company in the world. If open source wins out, few companies are better positioned to win the race to $10T and Apple’s decision to sit on the sidelines and shrug off your insults about Siri will be viewed as an all-time corporate strategic masterclass. That said, you can feel something on the air that Apple’s new leadership has a rabbit in their hat that when produced, will usher in the next big step change in the industry.

2) Simultaneously, I think we’ll see a proliferation of niche consumer AI products that create very narrowly scoped moments of magic for users. Biographer, founded by USV partner Jared Hecht, is a perfect microcosm of this genre. The company leverages AI to help people tell the story of their lives, opening up what was once a highly expensive concierge service to the masses.

Many such businesses long to be built.

A few more things on my mind….

The team at Alephic created an outstanding video on Claude CoWork for Marketing, covering both the 101 of utilizing CoWork and more advanced concepts around context windows, governance and using subagents. The whole video is outstanding but per my section above, if you’ve never tried browser or computer use—or if you gave it a whirl four months ago and were exasperated by the clunkiness— watch the section of the video at 25:00 and give it a go again.

LinkedIn won main character of the internet status with a “Seems Like AI Slop” button that rolled out to users in the wild late last week. It’s been a very, very long time since I’ve seen a new feature launch from big tech that was as universally revered, in no small part due to the sublime irony of LinkedIn playing anti-slop crusader. Maybe nature really is healing.

Speaking of slop, I’m still perplexed by why Substack CEO Chris Best specifically used the term “Claudefishing” to describe the slop phenomena overtaking the internet. Obviously the more refined term for copy pasting raw LLM generated content is “Claude-dogging.”

Jokes aside, while Claude’s slop tells are a tad more pompous, slop is an egalitarian phenomena that is present in all models. Why would Best single out Claude specifically? My guess is that OpenAI and Substack are working on a larger partnership deal and Substack didn’t want the Pangram partnership to rock the boat any further there.

I’d expect Substack’s largest creators to start pushing for more features around how they get compensated by LLMs. Public writing on Substack is accessible by default, meaning external AI web-crawlers and third-party LLM developers can scrape open posts to train their models unless an author takes action.

Daydream, an AI shopping platform for fashion, is now selling its underlying search technology to brands. Founded by former Stitch Fix and Nordstrom executive Julie Borstein, Daydream was a fascinating canary in the coal mine for AI-powered shopping. The company raised $50M and launched a web search engine and app dedicated to providing a better experience to complex queries like “Sleeveless wedding guest dress for formal summer wedding”, a service the company is now selling B2B to apparel companies.

This is likely to be microcosmic of a much larger trend in and around AI where consumer concepts half-pivot into B2B as user acquisition efforts stall. An under-appreciated aspect of building in this era is that the cost of growth is significantly higher than it was in the Meta-Youtube-Google fueled arbitrage days of the 2010s, making it much harder to growth hack into a few million users. Power laws are also stronger now.

Ultimately, this sets us up for a world where there are a few mass-market consumer AI products and a longtail of weird esoteric niche concepts that definitionally are never meant to scale. I’m not sure there’s a whole lot of space for a VC-backed middle class of consumer AI apps.

ClickUp is hiring a CMO or “100x marketer” to lead a one person marketing department (+thousands of agents), at a salary of $500K to $1M. I sorta think of experiments like this in the vein of Bryan Johnson’s longevity quest. I wouldn’t want to be that guy but it’s good to have somebody out there pushing the frontier beyond its logical limit. In the same vein, I can’t fathom how you could possibly construct a brand marketing function this way but I guess we’ll find out.

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

— Mike

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Paris, 1889.

The Exposition Universelle (world’s fair) opens on the hundredth anniversary of the Revolution, the Eiffel Tower newly unveiled, rises above it - the tallest structure on earth and a testament to engineering confidence. That January, the King of Sweden awards Henri Poincaré a prize for progress on the hardest problem in celestial mechanics: the three body problem, three bodies, each pulling on the others. (Hold that thought.) And in Clermont-Ferrand, two brothers take over a failing rubber factory and form a company called Michelin.

In retrospect, 1889 stands at the threshold of a century of management confidence: a belief that work could be broken into measurable units, organizations made legible - a world of future forecasting and annual plans. The Eiffel Tower expressed that confidence in iron while Poincaré’s work planted the seeds of warning from mathematics: even a system governed by known laws might refuse to yield a predictable future.

Working in modern corporate America in 2026, you can feel the chaos all around you. In my conversations with peers across industries the feeling is universal and the same themes emerge: management is acting poorly, channels are changing faster than companies can respond, customers are changing their behavior and everyone is producing (and being asked to consume) more work than ever before. The cohesion of work is evaporating - increasingly things don’t make sense.

Importantly, organizations, teams, and individuals have lost the ability to predict the future, and all of the scaffolding of “doing work” - annual planning, quarterly offsites, weekly status meetings - is increasingly feeling like theatre from an abandoned age.

Once again we are on the threshold of a new management era, ushering in… who knows yet? New ways of working with (and for?) AI. You can feel the doorway between the old world and the new world.

And perhaps no team feels this more viscerally than marketing teams - where AI is driving rapid change across three core areas: customers behaving in new ways, channels reinventing themselves and craft, the way work is made, is being completely rewritten. All three bodies are pulling on each other and the future dissolves into uncertainty.

Trying to predict the motion of three interconnected bodies is the definition of the three body problem that Poincaré worked on in 1889. He realized that once three bodies are pulling on each other, everything gets strange and prediction becomes impossible.

What does it feel like, in 2026, to lose the ability to predict the future? How do you coordinate work in an unstable environment? How do you navigate in times of chaos?

My theory is that the loss of prediction does not mean the loss of action. It means that the work becomes navigation.

Act 1: The Three Bodies in Motion: Customers, Channels & Craft

For marketing teams, the customer, the channel, and the craft are interconnected. The image of them as bodies in space exerting gravity is accurate: they exert force on each other. And all of them are changing at an accelerating pace in 2026.

Body #1: Customers

The old adage is that customer behavior changes slowly - that despite a technological revolution like the launch of the iPhone, it still took a decade for mobile usage to outstrip desktop. But I don’t buy it. This one feels different and the pace of change is astonishing.

ChatGPT and Google AI mode each have > 1B mau.

Pew 2026 says about four in ten U.S. adults use chatbots for information searching, and 38% of employed adults use them for work tasks.

AI is not just “information but now with natural language” - it’s about intelligence too cheap to meter.

Yes prompts are 5-20x longer than search queries (and getting longer). But the real shift is about delegation: customers delegating research and decision-making to LLM models. Where search touched the middle part of the funnel - useful for exploring a purchase decision and a small amount of discovery - AI is capable of recommending novel brands and categories to you, staying with you through your buying journey, and ingesting your preferences and constraints to get you to the final purchase decision.

On a recent family vacation to Vancouver, BC I had a “tom guide skill” that pulled local knowledge, insights, and recommendations all from my phone. Combining my personal context (how many kids, when we’re traveling, what we like and when low tide is). Without that skill I wouldn’t have seen this rad purple starfish at Willow Point Reef:

Vacation photos aside, the point is that the customer is becoming agentic. Not in the breathless buzzword sense, but in a practical way: a growing number of people now move through the world with a kind of cognitive sidecar. A model that reads before they read. A model that compares before they compare. A model that can scour, summarize and synthesize before they ever reach your site.

Quantifying this change is hard, especially because so much AI influence is either zero-click or dark-click. But we know adoption is growing quickly, and we can feel the shape of the customer request changing.

This matters because a brand used to be interpreted mostly by people. Now it’s interpreted by people plus the machine-shaped memory of the market.

The first body in motion - customers changing behavior.

Body #2: Channels

The environment that consumers are living in is changing too. As McLuhan says, the medium “shapes and controls the scale and form of human association and action.”

Search and social - intention and attention - the two biggest channels are both undergoing complete revolutions because of AI. The medium is changing.

Search & the information economy

Search is the obvious channel to change because of AI - but the second order consequences are still playing out and certainly not stable yet. AI search (AI Overviews and AI Mode) rewrites the implicit contract between searcher, search engine and brand.

We’ve moved from a list of links in ranking position to an AI model that talks about your brand - it can qualitatively recommend your brand (or warn against!)

Like a new predator being introduced into a stable ecosystem, AI search has disrupted the natural balance and order of things. 79% of top news sites block at least one bot used for AI training, while 71% block at least one live search or retrieval bot.

In the old web, almost nobody wanted to block search engines. Now publishers are putting up walls and making fine-grained decisions about training, retrieval, indexing, citation, and value exchange.

Google has teased a fully agentic future vision of search at I/O, but how much of this comes to pass if all the content is invisible to Google’s crawlers? The stable ecosystem of search is becoming disintermediated and it’s not clear how the story ends. Content licensing marketplaces? New forms of content monetization? New copyright laws?

Social is becoming AI spam

Social, once a place to connect with real people, then a place to browse algorithmic feeds, has now become an endless slop slot machine.

Social has changed less because of what the big platforms are doing, and more because of what they are NOT doing. Facebook and Pinterest have been slow and lazy in dealing with their AI spam problems.

But it is not just sloppy, it’s actual spam. Motivated actors are actively spamming these platforms for profit, producing synthetic media at scale to harvest attention, clicks, affiliate revenue, or platform payouts.

This is not a marginal problem. In certain categories, spam can feel like more than half the feed. More fake than real.

As the Guardian says “one man’s shrimp Jesus is another man’s side hustle”. And have you looked at LinkedIn / X / Substack lately? Pangram scores are being thrown around like red cards.

It’s slop as far as the eye can scroll.

(Worth noting that we are under-appreciating the flip side of this equation - explosion in creativity that is just around the corner as everyone has AI creative capabilities in their pocket. Knicks memes and animated pigeons.)

Two bodies in motion - customer behavior and channels both pulling on each other.

Body #3: The Craft

The third body is the way the marketing team itself works. The craft of work is changing.

It almost goes without saying that the world of work is changing, but we should not become numb to it. We are so early. While “AI” has been around for a few years now, we are really just getting started.

I think the advent of Claude Code and Codex was a bigger deal than the original ChatGPT 3.5 moment. AI usage has absolutely exploded because of these agentic tools. In May this year Cloudflare said it’s own internal usage of AI has increased by more than 600% in the last three months alone and Coinbase has seen token usage explode in just the last few weeks.

But the agentic revolution (Claude Code / Codex) outside of engineering teams is…. Maybe 2 months old at best??

Codex usage outside of engineering teams has just exploded, but only in May/June 2026:

If consumers have a cognitive sidecar with them, every knowledge worker now has a cognitive office with them, filled with personal staff: researchers, engineers, writers, designers.

For marketing teams in particular, 2026 has seen a Cambrian explosion in image and video capabilities, with SeedDance, OpenAI image models, Gemini, and so much more. Personally I’ve been enjoying generating, editing and producing video…. All in code? You can just ask an LLM to manipulate video files now. (see also hyperframes).

On the one hand the bar for creative output has never been higher, on the other hand you’re surrounded by slop cannons. Every knowledge worker is inundated by sloppy output and being asked to do more than ever - their old job, learn new AI tools and handle their coworkers 10x-ing their output.

So if the way work is being made feels new, different and strange - you are not alone. The two bottlenecks are now imagination and coordination.

All of this brings a sense of uncertainty. You can recreate workflows in AI, but the old workflows are out. Customers and channels are changing, so what new workflows do we need? How should the team be structured?

This is where the three bodies start pulling on each other and the future dissolves into uncertainty. If customers are changing and channels are changing, automating the old process is not obviously progress. You may simply be making yesterday’s marketing faster. Or worse, marketing to a customer that no longer exists in a channel that has changed.

So all three bodies are in motion: customers, channels and craft. The system has become impossible to predict.

What do you do about it?

Act 2: What kind of organization can act inside instability?

Organizations exist to solve coordination problems. Coase’s theory of the firm says markets are powerful, but using them is not free. You have to find people, negotiate, contract, monitor, remember, enforce, and decide. Firms exist because some work is easier to coordinate inside an organization than outside in the market.

AI changes the cost structure of coordination - individual throughput is exponentially increasing, but cheaper production is not the same thing as cheaper coordination! In fact cheaper production can make coordination harder - there’s more work to review, more mis-aligned output, more experiments, more variants than ever before.

More things that look done but are not actually decided. More things that look like progress but just place the burden on someone else to review and feedback.

The clock of coordination is the rhythm and rituals of sharing status (see: WTFS). Every organization runs on daily standups, weekly status reviews, monthly business reviews, quarterly board meetings and annual offsites.

But when the world changes the clocks change. Before the railroad, every town kept its own time - noon was whenever the sun said so. Then trains moved faster than town clocks, and we invented standard time so that everyone could stay in sync. And when the factory reorganized work itself, the first thing workers demanded was a new clock: back to 1889 - May Day was called in Paris in 1889, with workers demanding eight hours for work, eight for rest, eight for what we will.

New technology, new tempo of work, new clock. Every time.

If we accept the premise that your organization is going to be agentic - it’s going to be a mixture of agents and humans coordinating work there’s two ideas that become practical:

First, agents need context before they can be autonomous. Much like business analytics only works if you have a data lake, a shared context for all the data, maybe agents only work if you have a context layer. A shared, accessible layer of the necessary organizational context that is maintained and updated and agents can reference.

Second, human-made status dashboards are too slow for agents. The bottleneck is not production, but status. What needs working on, where is the project at? How do we adapt the status clock and tempo in an age of agents?

Railroads needed standard time. Maybe agentic companies need standard status.

We keep seeing these patterns at Alephic - the agentic layer only becomes useful after the context layer. Some examples from recent work:

Alephic Intelligence is a context layer: it is a centralized layer that ingests all of our organizational context: every meeting transcript, every slack message, every hubspot record, every code commit. This is useful for humans to query and reference but more valuable for agents to query and reference - now that Alephic Intelligence is built we’re just starting to layer on top automatic project status:

Amazon StarSearch is a project Alephic worked on with Amazon to import all Amazon reviews into a context layer. Once you have the context layer, it enables new kinds of marketing activity - like having Adam Driver read aloud a dutch oven review.

This is new kinds of marketing enabled by new technology, not automation of workflows.

Marketing Orchestration we’re working on a project with a Fortune50 brand to enable them to build a context-layer for their marketing team. A source of truth for all of the projects, status, actions, assets across hundreds of marketing team members and thousands of fast moving projects. We’re building the agentic layer on top of that to automatically move work around but you can’t do that until the context layer is built.

These examples all point to something practical for marketing leaders to invest in despite the uncertainty: build a context layer for your organization that is maintained, accessible to agents and leaves pheromone trails so agents (and humans) can operate on standard status time.

In my piece Of Termites & Tokens I argue that as the throughput of work increases you need to enable coordination through traces left in the environment.

Organizations have their own pheromone trails today: Slack threads, CRM fields, issue states, tags, transcripts, dashboards, source trails, approval markers, customer notes, analytics events, meeting summaries. In a low-throughput organization these are mostly residue. In an AI-accelerated organization they become the context layer that enables coordination via “standard status” (or token time as Justin McCarthy talked about with Noah).

If everyone can make more work, the company needs better traces. Better status. Better provenance. Better memory. Better ways of knowing what is alive, what is dead, what is approved, what is speculative, what is useful, what is merely plausible.

Otherwise the organization becomes a factory for its own confusion.

Of course, the interesting thing is not just ingesting the same context as everyone else (meeting transcripts, slack messages etc) but in ingesting the context specific to your business. For Amazon that’s StarSearch, the context layer of customer reviews. For another organization it might be a context layer of all the thought leadership produced globally by your organization and your competitors. For a biotech company it might be a context layer of all academic research, which lab produced it and who funded it.

AI of course makes this possible and easier than ever.

Act 3: Your Customers Are Lost Too.

There is another side to this. Your customers are lost too.

The sense of chaos and instability that we outlined above points to a feeling in the world: customers are as lost as you are. What do customers want when the world becomes hard to predict?

They want almanacs. Maybe.

The year is 1889, the same year that Poincaré won the prize for his work on the three body problem, a tire company called Michelin was founded:

“It all started in Clermont-Ferrand (a small French town) in 1889, when brothers Andre and Edouard Michelin founded their world-famous tyre company, fuelled by a grand vision for France’s automobile industry at a time when there were fewer than 3,000 cars in the country.

To help motorists develop their trips - thereby boosting car and tyre sales and in turn - the Michelin brothers produced a small red guide filled with handy information for travellers, such as maps, information on how to change a tyre, where to fill up with fuel, and for the traveller in search of respite from the adventures of the day.”

Before the Michelin Guide was a restaurant guide, it was a guide for early drivers. A tire company realized that if you wanted to sell more tires, you might first have to make the world more drivable. So the guide gave people maps, hotels, repair information, and eventually restaurants worth traveling for. They expanded the imagination for what driving could be.

Consumers are inundated by products that have AI features that barely work. And billboards that advertise AI without a shred of context.

Maybe the best brand marketing of the next few years will be about creating field guides for a disoriented customer. Almanacs. Maps. Buyers’ guides.

• What if a brand like Etsy built tools, education and almanacs on how to use ChatGPT for searching the vast Etsy library? (after all, Shopify’s catalog API is already allowing AI agents to search across all Shopify stores)

• What if a brand like Target or IKEA built guides for using AI in interior design? Not the cheap version of AI-generated rooms but rather something more mundane, real and useful. An almanac.

• What if a brand like Walmart built a guide for meal planning using AI? Educating consumers on what’s possible (recipe discovery!) and what’s hard (AI-recipes) in 2026 and how and where to use the AI tools to help.

Of course AI is amazing at shopping, interior design and meal planning. It is also terrible at all these things. For a brand to pull this off there has to be an honesty to the degree to which AI can be used in practical ways for practical tasks, and a recognition of the limitations and flaws of the current models (where those flaws and limitations are moving very fast!). Maybe marketing’s task is not just GEO (which is just SEO in a trench coat) but it’s about building your own evals and benchmarks for how well AI helps consumers in your category.

Before the brand marketing team at Walmart can build a consumer guide they need an internal WalmartBench eval framework for how good the various models are at finding products, searching their catalog and guiding customers in useful ways through their purchase journey for the categories that matter for your brand.

Maybe your customer needs an almanac.

This is, I think, the marketing opportunity inside the three body problem. If customers are changing, channels are changing, and craft is changing, then consumers are hungry for orientation.

How does the story end? The mania of new machines.

Let’s return to Paris in 1889, the Exposition Universelle but this time at night. It’s the first world’s fair to stay open at night because of the invention of electricity. Thomas Edison was a big feature at the fair and illuminated fountains with colored electric light captured the imagination of the people with a spectacle never seen before.

“In the basin, figures play in agitated poses, with tritons spouting torrents of water. The whole ensemble is highly decorative. Manufactured using new processes hitherto unknown in France, this fountain, illuminated each evening by electric light, shoots jets of colored water to great heights, which radiate into a dusting of alternating red, violet, blue, green, or yellow. The sight is truly miraculous.”

Meanwhile, just a few years before in 1882, Scientific American Supplement published an essay on electro-mania where W. Mattieu Williams argued for the separation from hype and reality around electricity - highlighting the mania of electricity making dead frogs move and people believing in re-animation.

Fast forward to 2026 and the essay by Nikhil Suresh: AI Mania Is Eviscerating Global Decision-Making feels eerily similar. I agree with Nikhil’s headline, that AI-mania is eviscerating decision making, but not the conclusion that all is lost. Chaos is all around, but we must learn to act regardless. New technologies always come with a breathless mania - our task (perhaps the uniquely human task?) is to separate the spectacle from the sublime. To discern between being tasked with animating dead frogs and illuminating dancing fountains.

The answer to a world that can no longer be predicted is not better prediction. It is better orientation. Prediction died in 1889 but navigation didn’t.

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

— Mike

A brand used to be what you said about it. Now it’s about what a reviewer, algo, creator, or agent says. It’s not that the brand doesn’t have any say, but it’s likely out-voted.

My LinkedIn feed has lots of really interesting conversations about the problem, but not so much about what to do about it. How does a brand build the meaning it wants to build in this environment? Which brands are built to thrive? What does the discipline of brand positioning and comms planning even look like anymore? It feels like folklore to me, bits of stories passed around, with people and machines adding their own bits to the stories as they go.

This newsletter has two things in it: The first is an experiment to see whether we can test whether a brand is built well and whether it’s being “retold” in a way that’s useful to the brand. The second is an essay about a possible path forward for the people who are in charge of brands.

An experiment: The Retellability Test

How do we evaluate whether a given brand is able to build meaning in this environment? A starting hypothesis: We need to look at two things. First, whether the brand is ready to be retold or carried. Second, whether it’s actually being retold accurately and favorably.

This test scores 15 variables, all pulled from public evidence. None of these brands asked to be in it. Together, the variables make up two key measures:

1. Brand Readiness (9 variables): Whether the brand is built to be retold by other people and machines (whether it has distinctive assets, a world worth repeating, a record clear enough to attach things to)

2. Brand Rendering (6 variables): Whether it actually is being retold by them (whether they’re recommending it, discussing it, and writing about it), and whether what shows up is accurate and favorable

Consider this a starting point. The whole process will undoubtedly get better as we go on.

Here’s an example of how it works: In the sunscreen category, Coppertone scores 51 on Brand Readiness and 47 on Brand Rendering, which is decidedly middle of the pack among the brands we measured.

The diagnostic found that Coppertone has the deepest heritage and the most complete public record in the category. Eighty years of history, a photo archive, twelve years of Reddit threads. But almost none of it converts. The brand is not recommended or retold in any meaningful way. When people and machines talk about sunscreen, Coppertone is mostly absent or, every now and again, it’s the affordable one that smells good.

Maybe counterintuitively, this implies that a deep brand heritage does not necessarily mean it will be retold. One hypothesis is that Coppertone’s brand feels monolithic and finished. It’s not something that either people or machines want to share or recommend. Leading it to be cited, but not retold.

The new folklore

In June 2026, the CMO of one of the world’s largest advertisers, Unilever, delivered a keynote at Cannes and said his company had “stopped asking what our brands want to say” and started asking “who would care enough to carry it.” Here’s his talk.

That one sentence sums up where the marketing world finds itself today as well as anything I’ve read. “A brand is what people say when you’re not in the room,” the old saying goes. It’s always been true. This is folklore. A story passed from mouth to mouth, changed a little by each teller, and (importantly) owned by no one. Cinderella, for example, has countless variations across time and cultures, but we all recognize it when we hear it. Brands work the same way.

Historically, brands would heavily contribute to their own folklore by authoring stories and sending them into the world. But now, the balance has tipped. We don’t control our brand’s stories anywhere near as much as others do. Today, a brand is far less what it says about itself, and far more about what a wide ecosystem of third parties, both human and non-human, can find, trust, rank, recommend, and sell.

Lots of people are already talking about this issue, not only Unilever. Almost all of the conversation, though, has been about identifying and naming the problem. It goes something like this: A brand is a shortcut for meaning in our minds, but how do we keep that meaning coherent and effective when we aren’t the ones making it any more?

We used to be able to control a lot of the brand’s ecosystem. We could pay for a thirty-second spot, better shelf placement, and a mention in a top-tier magazine. We staged the launch and approved all the outputs. We held that control-everything stance even as production got cheaper and sped up. The discipline for putting guardrails on all of those brand touchpoints was called comms planning, or engagement planning.

Now that world has changed, and we cannot control the great majority of our brand’s meaning-making. A reviewer writes a pointed post on Reddit or Substack. An AI agent turns it into three sentences a shopper reads instead of your site. A creator turns your brand into a routine, or a joke, or just the latest “can’t miss” hook. A fleet of them spams those hooks at their followers, racing each other to the best ROI. A retailer turns the brand into shelf position or sponsored search. A marketplace turns it into a thumbnail, a review count, and a rank.

This is a real problem, and I’m here for it. But the more interesting question is what we do next?

The reflex is to make more stuff. More is more. Something will hit, and we’ll just amplify that. If done well, that’s not wrong, but there is fallout. Meanings multiply and contradict. The brand turns to mush.

“Because we do mass volume, there’s no way this can’t work.”

— some dude in a sponsored post on my FYP, selling access to a TikTok creator pool, July 2026

How do we build brands to be better equipped for this environment? A well-equipped brand, when it is “carried” (to use Unilever’s word), or “retold” (to use mine), gets stronger through retelling. And it does so in a consistent way, with lots of people, over time.

I think the answer lies in better upstream conditions or incentive design.

The old way is executional control, where we carefully shape every output, one at a time. But we cannot possibly approve every Reddit thread, every comparison article, every creator riff, every AI answer. Tighter guidelines just can’t scale across all of that. That lever is disappearing.

Instead, imagine a discipline that lives upstream of the go-to-market plan. This discipline is responsible for designing the conditions and incentives that make good outputs more likely: The codes, claims, participation hooks, incentives, and feedback loops that let a myriad of outside actors, both human and non-human, produce something recognizable without approving every single thing. This discipline probably shouldn’t even be called comms planning anymore.

We are still after the same end goal, namely a strong brand that is able to build those consistent memory structures that drive buying decisions. But we are building the conditions for it, not building the touchpoints themselves.

Letting go of control is difficult. It means letting others play around with the brand in their own way, for their own reasons, and using their own words.

What’s more, it’s difficult to design the conditions and incentives to make good outcomes more likely, because the thinking has to happen up front. Get the inputs wrong, and the ecosystem compounds the error at a scale that the brand police cannot possibly catch up to.

Brand meaning will be made whether the brand participates or not. If it doesn’t participate, the brand doesn’t fade to nothing. It just slow-drifts until it means something else entirely.

I have theories about how to do it, and have started using them for clients. But no one has the exact right answer yet. We all need to explore and experiment more. My first public experiment is the Retellability Test. It tries to measure whether a given brand is built to be retold (Brand Readiness), and whether others are actually retelling it accurately and favorably (Brand Rendering).

The Unilever CMO says brands should be fires, and creators are the people who carry them. It’s a good metaphor, but not a new one. Folklore has always worked this way. The best stories have always survived because they got retold, not because anyone owned them.

So the part I care about, and the part not enough people are talking about, is how you build a fire worth retelling in the first place. What’s that fire made of? Why do some fires catch but most go out? Which brands are doing it well already?

We should be able to build brands strong enough to stay themselves, no matter who’s telling their story. And I believe, if we build them well, the retelling will make them stronger.

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

— Noah

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The Enduring Power of Slop

The terminally online are having a viscerally anti-slop moment. Gradually, then suddenly, the tech elite have decided they’ve had enough of sacrificing the written word at the syntax abattoir of “it’s not delivery, it’s DiGiorno.” Substack in particular threw down the gauntlet this week, partnering with Pangram to launch an AI detection feature across the platform.

For most of us working in and around marketing and AI, this feels like the proverbial “nature is healing” moment. But to most of the world, we’re just a bunch of whiny wordcels. Based on sheer engagement metrics, the people love empty calories masquerading as a healthy meal.

Outside the filter bubble, the proliferation of slop continues at an astounding pace. >40% of longfrom LinkedIn posts are now AI generated and shameless copy paste jobs regularly go viral on the platform. On X, one of the most shared pieces around the World Cup was this AI-generated essay hyping up the US. Soccer team. Even as someone who has Pangram wired into my consciousness, I read it and a small part of me wanted to run through a brick wall. Matt Freese punished me accordingly.

Slop, of course, is not an entirely new phenomenon and it long predates LLMs. NCIS…..ah whatever the hell city they’re in now… was the top watched drama of the 2010s. We have endured ten Fast and the Furious movies, not even counting the bizarrely excellent sloptacular known as Hobbes & Shaw. Plenty of human written content, especially when forced to take the form factor of SEO arbitrage, was just a more simplistic slop varietal.

Slop endures because it is ultimately a demand side problem. LLMs don’t even create our inherent demand for slop. They simply satiate it. Said another way, slop has product-market fit, at least when it comes to influencing humans. Paradoxically, I agree wholeheartedly with Noah’s take from last year that slop will be far less effective at influencing agents long-term than it is at hypnotizing people.

But while a vocal minority seems to be reaching their slop breaking point, why does AI-generated text maintain such a stranglehold over its consumers?

Slop wins because it conforms to neat narrative arcs that fulfill our desires for cohesive stories: At crime scenes, children make the best witnesses because they simply report back what they saw rather than editorializing into a natural story arc. The adult brain is hardwired to eliminate cognitive dissonance by effectively hallucinating events that didn’t happen to make our memories fit into a tidy narrative.

Does that sound like any of the AI tools you know? In many ways, a tragic flaw of LLMs is that they are even more allergic to cognitive dissonance than people are, hence the hallucination problem being such a tough one to squash completely. Moreover, any time there is even mild tension or paradoxical sentiment in an idea, models go out of their way to call it out ( “that’s the real sticking point”) and try to crush it when the more intellectually honest tactic would be to simply let the tensions be.

Insufferable vernacular tics aside, this makes AI writing more “comfortable” to read, especially in the passive consumption mindset that, well let’s face it, most people are in most of the time.

In many ways, the most interesting thing to talk about when it comes to slop is why it seems to those of us who spend the most time with these models that each new model, despite its increases in intelligence, seems to be more slop-y, not less.

Here’s Noah’s observation from earlier this month:

One of the things I’ve found most confounding over the last year is that as these models get better, at the same time, I feel like they’re getting worse at writing. Or maybe “worse” isn’t quite the right word, but I’m not seeing any end in sight for the slop.

I suspect this comes down to a confluence of circumstances: the nature of the model is to average things out, reinforcement learning with human feedback (RLHF) moves the models towards being a “helpful assistant,” and the move to coding and reinforcement learning with verifiable rewards (RLVR) means the models are further steered towards a model of the world with conclusive truths.

But my guess on the biggest challenge is that good writing is fundamentally anti-consensus, and I’m not sure that’s a problem the models can really solve. As soon as the models all agree on a turn of phrase or style, it becomes “slop” and can’t really be used by humans anymore. As opposed to code, where (for the most part) a good answer is a good answer (you can verify the code works), the quality of writing is subject to other writing that exists around it.

At this point, I’ve abandoned using AI to generate any copy at all for public-facing work. Perhaps there’s some dogmatic pearl clutching here and I’ll re-evaluate with each new model release but for now, the trend of each subsequent model doubling down on a slop-y vernacular identity (and increasing the conviction with which it does so) is accelerating.

With heavy training on my voice, I used to work with ChatGPT and Claude for writing transition paragraphs, flagging and eliminating weasel words, and pushing me more towards brevity. I always found LLMs made my prose too detached and omniscient but older models felt more malleable. I now feel mostly powerless to stop the models from injecting stylistic touches that simply do not sound like me.

Ultimately, I suspect that we’ll lose more of the internet to slop simply because in more and more instances, slop will get the job at hand done. But as with any good or service, there will always be a market for artisanal prose, a market that will grow as agents seek out higher quality content and increasingly esoteric expertise. Thus, the inherent value of writing that requires real learned experience and imbuing contrarian thought will also go up. On balance, this is a better era to be a writer than the pre-LLM times.

But for all of its captivating power, AI-generated text will always have one fundamental flaw. Even if it satisfies the reader, the writer is a cognitive passenger princess in their own life.

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

— Mike

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When I originally announced BRXND in 2022, it was because I felt there was a better, more important conversation to be had in marketing about AI. At that time, it felt like all anyone wanted to discuss in the industry was the possibility of AI-generated television ads, and that felt (and still feels) like such a tiny piece of a much larger puzzle.

Fast forward to 2026, and I feel a bit of deja vu. While the industry has thankfully come to understand that the opportunity is bigger than just AI commercials, there’s another massive underestimation happening, but this time it’s around consumer adoption and what it means for brands. While most of the industry seems obsessed with getting on Reddit, my feeling is that this is the biggest shift in the way consumers consider, research, and buy products since the birth of ecommerce thirty years ago.

To that end, this year’s conference will build on our past focus of helping to highlight how the world’s best marketers are integrating AI into their organizations and work while also spending more time looking outward to consider what the future of brand building looks like in a world where preference is heavily mediated by large language models. To get things started, I’m incredibly excited to announce the initial set of speakers and sponsors. We’ll have more coming out over the coming weeks and months, but a huge thank you to those who are helping to bring this year’s event to life.

Initial speakers:

• Jo Shoesmith, Chief Creative Officer, Amazon

• Mark D’Arcy, Corporate VP and Global Creative Director, Microsoft AI

• Mikkel Holm, Chief AI and Innovation Officer, Bark

• Aaron Dignan, VP of Agentic Products, Rocket Money

• Mike Houston, Head of Creative AI, Amazon

• Tom Critchlow, Forward Deployed Engineer, Alephic & Founder, AI Search Leaders

• Justin Inman, Founder & CEO, Emberos

While the narrative has evolved as LLMs increasingly mediate consumer preferences, the core tenets of BRXND are the same as when I put on the first show in 2022.

Doers > Speculators: We want BRXND to be about doing and learning, not talking and speculating. We are too early in this journey, and the technology is too different from what we’ve seen before to get stuck speculating about its future impact.

Curiosity > Overconfidence: We embrace the unintuitive nature of AI and its future implications. We are all learners in this new era of technology.

Conversations > Panels: Whenever possible, it’s better to have just two people on stage talking to one another rather than a whole set of speakers competing for time and attention. Go deeper with fewer people so that the audience can learn from their thinking and experience more than just their opinions.

Show > Tell: It’s always better to show people something than to tell them about it. While other conferences try to urge people away from showing specific products, we think it’s most useful for people to see what’s real and possible.

Process > Perfection: Take your audience on a journey, even if it’s something that didn’t work out. This is an audience of doers and experimenters, and understanding the journey is often more important than just seeing the outcome.

This is just the beginning. We’ll be announcing additional speakers in the coming editions of the BRXND Dispatch as we build towards an incredible day on November 5 at the Times Center

Final reminder: early-bird ticket pricing is available through the end of the week. To add your name to the waitlist, please visit our website. Thanks for all of the support, and I look forward to seeing you in November.

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BRXND NYC is coming up on 11/5, and early bird tickets are currently $749. Get yours early, we will sell out.

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So while clearly token leaderboards don’t represent the long-term answer, I want to offer a defense of the practice of tokenmaxxing (and token maximalism generally), as by far the best approach to learning how to get the most out of these models. The thing I think most of these articles miss is that when it comes to driving change inside organizations (and society), people, not technology, are the bottleneck. If you fail to address that, then nothing else really matters.

One of the questions I get most often from customers—along with friends and family—is “what can I do to catch up in AI?” My answer for the last four years has basically been the same: get yourself the most expensive subscription you can and try to use as much of it as possible. It used to be ChatGPT; now it’s Codex and Claude Code/Cowork, but the advice stays the same. Specifically, I suggest you use it so much that you max out your token budget on their biggest plans.

To give a sense of just how many tokens that is, here’s a recent SemiAnalysis estimate on what you get out of both companies’ most expensive plans (the “max possible spend” number is their best guess for the value you can get out of the plan if you were to use it to its fully allowable extent):

If you’re really trying to wrap your head around AI, nothing replaces the hands-on keyboard experience. At the very first BRXND in 2023, I talked about building AI fingerspitzgefühl (fingertip feeling), and despite the years in between, I still think it’s the most critical piece of the puzzle, and I credit quite a bit of my own understanding and success with AI to just having been lucky enough to be able to put in a thousand-plus hours in 2022.

The reason nothing replaces hands-on also hits on a big misconception about AI: that you can just hand things over to it. While Fable and Sol get us closer than we’ve been before, for the most part, when you watch people who get the most out of these systems, you see them subtly steering them to success. Oftentimes it’s such small adjustments that even they don’t notice. It can be the way they ask the question or when they choose to interrupt, but the outcome is that by making one- or two-degree adjustments, they are experts at keeping the harness on track. The most insidious AI failure case isn’t it writing bad code or a bad doc, it’s fundamentally doing the wrong work.

Which brings me back to “tokenmaxxing,” the pejorative term people have come to use for companies that encourage their employees to use AI by any means necessary and often track them in leaderboards. Here’s my general view on the practice:

1. When people talk about tokenmaxxing, they’re mostly talking about a) the token leaderboards and b) a few reports from companies like Uber of massive AI spend.

2. I obviously don’t think leaderboards are the best long-term solution, but I’m not sure they’re the worst short-term solution (more on that in a bit).

3. While there are a few stories of massively inflated AI spending, I’d venture to guess that the vast majority (90%+) of the Fortune 500 should be spending far more on tokens than they do today.

4. When we hear about these companies spending real money on tokens, it’s happening via harnesses like Claude Code/Cowork and Codex, not ChatGPT, Claude, or Copilot. More importantly, that inflated spend mostly isn’t coming from tools built with AI, partly because those tools, by their nature, are much easier to optimize spend around.

5. The vast majority of the Fortune 500 doesn’t give their employee base access to platforms like Codex and Claude Code/Cowork.

6. Most people who work in large companies don’t want to change the way they work.

That last point matters a lot. If you’re trying to turn a massive organization, you are usually better off over-rotating than under-rotating. This is also why, in the short term, a blunt instrument like a leaderboard might actually work in motivating people to dive in. Obviously, in the long term, you want to see value from these tokens, but remember that the average Fortune 500 employee is currently using significantly fewer tokens than they should. Participation before optimization.

The conclusion of all this seems very clear to me: for a small group of technology companies, getting past tokenmaxxing really matters. They’ve convinced a large enough swath of employees to integrate these tools into their lives that they’re now ready to graduate to thinking about the return on those tokens. But for the other estimated 90-95% of large enterprises, the greater concern is that they’re currently token minimalists in a world where the return on knowledge and capability can be exponential. In part, this is a function of point number five, as most of these companies still haven’t opened access to the very best AI tooling (and I’m not quite sure what the path to that looks like). Like all things AI, the biggest mistake pundits make is forgetting that people are the bottleneck. This transition, despite the breakneck speed, will still take a long time to fully sweep through the corporate world, and we should all be wary of claims that we’ve entered a new phase.

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

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The GEO Leaderboard

The modern marketing leader’s inbox is a dystopian postmodern art form. If there’s any inkling that you might influence a software purchase, you live perennially awash in a deluge of pitches for identical-sounding software. Right now, the hottest technology is all about how brands show up in AI search, which, from here on out, I’ll describe using the generative engine optimization (GEO) lexicon.

Amidst the litany of market maps, VC graphics, and customer review boards covering the 50+ vendors that have appeared on this scene in the last two years, there was one question that I had not yet seen answered: Which company that is selling me the ability to be present when users are querying LLMs is best themselves at showing up when marketers search for GEO solutions?

Or said more simply, which GEO company is the best at GEO?

To answer that, I grabbed a couple of Cometeer capsules, fired up a DataforSEO account (thanks to an assist/suggestion from Zero.xyz), and spent an afternoon vibe-coding the resource I wished to see in the world.

What emerged is a simple leaderboard that ranks the top 10 companies based on how prominently they appear across 175 prompts that marketers would use to search for a GEO solution. The link to the live webpage is here:

In addition to the overall table ranking, for each company, you can drill down to see where a GEO vendor is most visible across model, prompt archetype, and specific queries:

I encourage you to explore the leaderboard, dive deep into the methodology and peruse the full set of prompts the tool tracks. Please send me any thoughts on what stands out or what I got wrong! I intend for this to be a fluid project that I’ll re-run monthly and add additional companies to.

To me, what’s far more interesting than the tool itself is what the process of building it revealed about both the nature of AI search and where much of the value in vibe coding experiments like this ultimately accrues.

Learnings and Observations

1. The high-level results were fundamentally pretty unsurprising… which is kind of the point! The companies with the strongest “brand” footprint in GEO rose to the top of the list.

Every day, AI search becomes less of a convoluted morass of grey hat arbitrage schemes. It’s much, much more akin to serving as a mirror for brand marketing!

As the models mature and get more sophisticated about how they access real-time web data, the hit rate of spamming Reddit, blasting listicles with your product at the top and indulging in programmatic content schemes will increasingly be reduced. In its place is just good consistent work across building credibility with top media, telling a clear and differentiated brand story and building clear owned content lanes and tentpoles.

2. LLMs have a frenetic, token-thirsty desire to build incredibly complex projects that test the boundaries of their capabilities. For this project, Claude Code (running Opus 4.8) was eager to build out a full Next.JS app with all kinds of interactive components that would effectively have constituted shipping the minimum viable product for a full-on GEO platform. It took a lot of heavy steering to dial this down to a basic output that could ship in hours, not days.

At one point in the session, I dropped a, “my brother in Christ, I’m not trying to fully recreate the ten companies on this list.” I’m simply trying to build a simple HTML tool to help marketers better understand which GEO companies are best at GEO!”

Even still, I ended up building an MVP with far too much wiring that Noah wisely suggested knocking down to the final webpages that you see on brxnd.ai.

3. At the same time, I pretty easily could have gone a step further and vibed up an application that looks a lot prettier and recreated something cosmetically close to the dashboards of GEO companies. Claude was begging me to do it!

This is not at all a dunk on the technology these companies have built—all of the serious executives I’ve spoken to in GEO are the first to tell you that basic prompt and citation tracking is a commodity. Albeit a commodity that has had enormous CMO-level demand to date.

The far more difficult and important question is understanding the sentiment and nuance of how LLMs perceive your brand and ultimately building agents and workflows that shape that perception and destiny. This is where we’ll start to see meaningfully different companies emerge in the space.

Profound is all-in on helping marketing leaders build agents, championing the concept of a marketing engineer. Evertune is building an ads manager to closely align GEO with the potentially far more lucrative whitespace of buying media in ChatGPT. Bluefish is now helping brands build agent-optimized catalogs that align SKUs to semantic brand stories, a vital GEO concept that is still in its early innings.

Whether GEO companies grow into their valuations depends on whether they can cross this chasm from analytics software to truly running end-to-end workflows where brands take action. More on what all this means for brands in a “GEO Maturity Matrix” post to come.

4. In the vein of Noah’s software for one experiments, it’s not that hard to see how an enterprising CMO (or more realistically, an AI-pilled member of their team) might decide it’s worth spending an afternoon vibe-coding a little project like this to make the best possible decision on which software to buy. Bain Capital has PE associates vibe-coding legacy SaaS replicas to understand whether their acquisition targets have true moats.

If a marketing leader took up this mantle and ended up in the same loop I did with Claude, they might just find themselves tempted to “build the thing” around their bespoke use case rather than modifying off-the-shelf AI software. Why pay tens of thousands of dollars per year for something you can build that is purpose-created for the brand metrics you want to track? More than most categories, AI search requires technology that touches a brand’s “unique judgment, workflow, brand voice and edge.” There’s a real build case here.

5. Before AI, the point of a great strategy doc like the famous Amazon six-pager was that it forced the person writing it to deeply grapple with an idea, identify fundamental flaws, and ultimately, sharpen the thinking around a proposed product launch or strategy. Driving executive and team alignment was a secondary benefit—the bulk of the value came from the agonizing clarity that only comes with deeply thinking through a problem.

At its best, vibe coding is just another means of pressure testing ideas and identifying fallacies in your thinking, only the medium is an artifact rather than a document. The challenge here, of course, is that LLMs seek to strip you of agency at every turn. They want to quickly make decisions, ship code, and yada yada over any paradoxes in a project. I consider myself an ultra-amateur vibe coder, which is to say, I still hand over way too much agency to LLMs when I work on things like this. But with each rep, I find myself slowly imbuing a little more “me” into my AI-assisted work.

When anyone can brainlessly build, doing the work is the point.

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

— Mike

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The Parallel Open Web

The argument for the obsolescence of the open internet we know and sometimes love is ultimately a pretty simple one.

When the largest technology companies in the world have a vested interest in protecting access to proprietary data, the web is destined to become a closed entity of fiefdoms that feed the AI beast. In a world where context provided to an LLM is king and distribution is the rate-limiting resource, the die has ostensibly been cast.

In response to the rational economic incentives AI creates, all of the best content providers will move behind paywalls to directly monetize their audiences and exert whatever leverage they can with LLMs for licensing deals. More than ever, information simply does not want to be free. It longs to be expensive and scarce to accrue maximum value to those who have it.

Coming out of I/O, it’s pretty clear that Google is fully at peace with ending any last vestiges of free content depending on search traffic. Ten blue links that direct users to external websites have served their purpose and in Google’s eyes, are no longer the best form factor for efficiently satisfying a user’s query and maximizing their own ARPU. The malthusian trap is worth the risk. Google does this matter of factly with malice towards none but with charity towards none as well.

But ironically, the thesis for the endurance of the open web is similar to the case for its demise, with one key twist. As agents come to massively outnumber humans online, there will be a Cambrian explosion of new demand for readily accessible content. This force will be stronger than the pull towards walled off centralization.

This idea is best encapsulated by Parallel Web Systems (and a few notable competitors such as Exa), founded by former Twitter CEO Parag Agrawal and valued at $2B. Parallel’s mission is simple– build the search and retrieval infrastructure that lets AI agents read the open web at scale.

As a result, Parallel has taken up the mantle as the champion of the “open web”, a term that has historically been co-opted by programmatic vendors to sell a chumbox of ad products from a bygone era that have rendered many media properties utterly impossible to consume. In Parallel’s recasting, the open web is “modern humanity’s living memory…and the ecosystem that made modern AI possible in the first place.”

Parallel’s thesis is effectively that for AI to fulfill its grand promise, the spirit of the open web must endure and find a new economic model to support openly indexed content that lives outside walled gardens. Without it, agents will become stale commodities.

Last month, Parallel launched a new product called Index, a crack at building a new economic layer for open web content. The platform shows content owners how agents discover, cite, and rely on their work and compensates them based on each source’s contribution to the agent’s output. It’s a manifestation of an idea Ben Thompson was all over a year ago when he argued that micropayments— a concept that fundamentally never made sense on a web built for human attention– could finally be viable on the agentic web.

Patrick Collison also has a good take:

What I’d emphasize is that while Index is nominally a publisher-side product, what it really ships is a new framework for influence. For a brand, economic value now increasingly accrues to your position in an agent’s reasoning at the moment of inference, not to a human’s attention at the moment of blog post read, ad impression or PDP view.

The end of the commodity internet

When it comes to which entities influence agents, it’s important to remember that the metric of 68% of searches now being “zero-click” disproportionately obliterates a specific segment of the open web ecosystem. SEO-dependent publications– which largely only existed in the first place to exploit keyword arbitrage– have been wrecked. Entities that publish timely news, analysis, entertainment or bizarre esoteria that serves real-time retrieval have been far more resilient than reported.

This last point is the most interesting to me because it lays out the path for the “open web” to once again return to its free-wheeling roots.

When monetizing web sites required reaching a critical scale of human eyeballs to sell ads to, you had to build within commoditized patterns that could both rank for SEO and serve necessary ads infrastructure. For 25+ years, the pendulum of web architecture has swung towards standardization, especially for commercial and marketing oriented builds.

If the business model gets rebuilt around serving AI at the point of inference, more of the “standardization” can exist at the invisible infrastructure level. Certain protocols will need to be in place for agents to retrieve information but effectively there’s a path for the open web to become a subliminal layer targeted mostly by agents on top of far bolder, wackier web interfaces built for humans.

I’m sympathetic to this case for open web renaissance for a few reasons:

- The arc of digital history generally bends towards content that can be accessed for free.

- There’s just a hell of a lot of pent up creative energy to build weird stuff right now with all of the new capabilities that AI unlocks.

- Simultaneously, there’s a vibe right now where a lot of people are just building for the pure love of the game, happy to worry about business viability later. The early blogosphere was crafted on a similar ethos.

- There’s massive uncertainty around what kind of information agents will ultimately gravitate towards as the models become more sophisticated. When nobody knows what the hell is going to work, the best strategy is for content creators and brands alike to maximize shots on goal.

Agents are weird…and perhaps the web will once again be too

If there’s a critical flaw in what Parallel is trying to build with Index, it’s that micropayments have also historically failed as a web business model precisely because they ascribe a rationality to the media business model that doesn’t exist. If media was a pure utilitarian purchase, most publishers would be extinct.

Quality media is largely sustained by people who buy the idea and self-actualization of being a New York Times, Atlantic or Stratechery reader more than the utility of actually reading. That is to say, they buy being perceived as erudite by their peers and pay hundreds of dollars for coffee table accessories. They buy the brand.

One popular theory of the future is that agents won’t be so sentimental. Agents, so the conventional logic goes, will rationally seek out the most relevant information to complete a given task and will pay fair market value for it, agnostic of “brand.” For media, that fair market value often doesn’t sustain the cost to create content.

Truth is, at this juncture, we have little idea how agents will ultimately hold a notion of brand preference. Hell, there may well be agents for maximally cost conscious consumers that render brand as more of a tax than a buying signal.

But a few things are likely to be true that point to agents building true brand associations:

- Agents will be increasingly trained to look for heuristic shortcuts that don’t swallow as much compute

- Agents will take on more quirky anthropomorphic tendencies as models get more sophisticated

- Influencing agents will become one of the most lucrative problems in tech. It will be far less straightforward that simply optimizing catalogs and stacking Reddit karma.

Taken together, it’s hard to understand how the increasingly complex context needs of agents will possibly be served if the web devolves into a morass of walled gardens, licensing deals and content marketplaces.

Perhaps it’s as simple as this— all signs point to the agentic web swinging us back towards things getting really weird again. And it’s the weird early 2000’s web, where people wrote entire blogs from the perspective of ficus trees, that so many of us long to see return.

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

— Mike

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The Marketing Maximalist

There’s a fascinating paradox at the heart of AI adoption in the enterprise. While efficiency gains are the easiest myopic way to prove ROI to a CFO, having a mental model of AI based on efficiency is fundamentally a race to the bottom that dooms any true attempt at AI transformation. At its core, AI is a technology born to drive growth and introduce incremental new revenue, not eliminate cost. It’s a race to the top.

Thus, token maximalism is such an important philosophical concept because it reframes the entire discussion of how to use AI around the enormous value AI can create, not the human capital costs it can strip out of a business. The weird bastardizations of tokenmaxxing that make headlines— like engineers looping agents in a flat circle— are an externality of rotten and cynical corporate cultures, not an indictment of the underlying concept. More thoughts from Noah on this later in the week :)

For enterprise CMOs, token maximalism is still mostly an academic notion anyway as I’ve yet to meet a marketer who is really the one running up the Anthropic bill. Perhaps more than any function, marketing is still afflicted by a bizarre conservatism when it comes to AI that too often leads to automating low-throughput workflows rather than reimagining what is now possible. It’s utterly boring, mundane and ready to be combatted with a better framework– enter marketing maximalism.

AI is fundamentally slashing OpEx from businesses at a time when distribution is historically scarce-- the perfect tsunami for more dollars to go into marketing in all forms if CMOs embrace a maximalist mindset.

The marketing maximalist is ruled by a two part creed:

1) AI should be used to launch audacious experiments where marketers attempt to solve problems that were impossible before.

2) Where AI can create additional efficiencies in a business, every dollar saved should immediately be reinvested in growth.

Taken to its logical (and profitable) extreme, this line of thinking will pretty significantly alter the marketing and media landscape as we know it. Here’s the CEO of Ridge, a ~$300M+ brand:

A marketing efficiency ratio of 2 is an absolutely bonkers concept! One out of every two dollars you make is being pumped right back into the marketing machine. But if your operating expenses are now lean enough to profitably invest this much in marketing, it’s the only defensible utilitarian play!

There are a few obvious macro ramifications to AI-powered marketing maximalism, namely:

- As mentioned above, the advertising industry, which historically has hovered around 1% of GDP since the 1930s, is now growing as an overall share of the economy. The old knock on advertising– that it was a sort of zero-sum industry relative to overall economic output– is not true in the era.

- Gargantuan tailwinds for Meta, Google, Amazon, TikTok and OpenAI’s ad platforms. CPMs are only going in one direction…..

- A golden age of new consumer brand concepts where extremely lean teams can pop up and challenge legacy brands with profitable unit economics from day one, making venture capital an optional luxury.

- More money available to pay outsized salaries to the most talented creative and brand leaders, who increasingly will have the only skill in the business that can’t be commoditized.

While it should be noted that Ridge has hardcore direct response and performance roots, it’s a mid nine-figure company that runs a plethora of TV commercials, multimillion dollar influencer deals and even owns its own media properties. More importantly, the broader point of framing AI in the context of new audacious growth initiatives vs. efficiency is every bit as true in a large enterprise as it is for a brand like Ridge, perhaps even more so.

Consider the position that a brand like Coca-Cola currently finds itself in, which is microcosmic of many large consumer products. Beyond the headwinds of GLP-1s and general decreased demand for sugary drinks, brand preference for Coke products is now going to be increasingly mediated by agents. What will make an agent crave coke?

Obviously the answer here isn’t for Coca-Cola and other FORTUNE 500 brands to blindly start committing hundreds of millions of incremental dollars to paid media. And I doubt there’s much wherewithal for Coke to start thirstily astroturfing Reddit.

But when an agent puts together a shopping list and eventually executes a purchase end to end in Instacart, what will compel said agent to supplement “build a healthy meal plan for my family of four” with a cheeky 2-liter unless it is ordered point blank to do so? What kind of “brand” associations can products that might not be hyper-utilitarian purchases make to be perceived differently by agents?

The World Cup is in full swing and despite a year of doom and gloom headlines leading up to the tournament, the vibes so far have been immaculate. It’s also a fun time to recall a crazy and contrarian brand bet that Coke once made that with significant investment, women’s soccer could become a mainstream, family-friendly, nationally marketable entertainment property.

By sponsoring FIFA women’s competitions and backing the 1999 Women’s World Cup ecosystem, Coke helped give the sport blue-chip credibility at a moment when brands and broadcasters were still unsure whether audiences would show up. While the ‘95 cup struggled to put 1,000 fans in the stands, the ‘99 world cup was played to sold out stadiums and 40M people tuning in for the final.

For Coke, the ultimate payoff was association with patriotism, empowerment, families, and a fast-growing women’s sports audience before many other brands fully recognized its value. In other words, marketing maximalism.

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

— Mike

One more early bird call for BRXND NYC 2026 before many of you ship off to La Croisette next week. Early bird tickets are $749 through July 15 before we raise prices to $999+ for GA.

If you’d like to attend the show, you can still add your name to the early bird waitlist.

We have a lot of demand and only 378 seats in the Times Center so we’re prioritizing brand-side marketers. If you work at an agency, we ask you just bring a client along. Drop me a note at mike@brxnd.ai and we’ll handle the details.

Join waitlist here

Interview with Art Tauder, 65-year veteran of Madison Avenue

Art Tauder started his career on Madison Avenue in 1961, a almost the exact timestamp when Don Draper was presenting “the carousel” to Kodak to cap Mad Men‘s inaugural season. But by then Art had already been clocking hours in advertising for fourteen years, filing tear sheets at his father Milton’s agency starting at age seven. Sixty-five years later he’s still in the game, working exclusively on communications strategy for innovations that can disrupt major industries and improve the quality of life for individuals and society as a whole.

Across a thirty-five-year career at IPG, Art built the strategic planning system that directed more than 1,000 operating companies across 130 countries, servicing the likes of Coca-Cola, Unilever, Nestlé, and General Motors. He architected the transformation of 95-year-old McCann-Erickson into the McCann WorldGroup, which earned #1 Global Agency of the Year honors from both Ad Age and Adweek in 2000, and personally acquired the ThunderHouse trademark for IPG for $1 million before getting it back as a retirement gift.

He served as a Visiting Scholar at the MIT Media Lab, the go-to person for technology transfer into marketing communications, right as the internet was finding its footing in the wake of the dot-com bust. Today he’s advocating for a “New Age Communications WorldGroup,” built on the premise that agentic AI is the next great inflection in marketing.

Along the way, he gave a renegade twenty-year-old kid his first taste of the advertising business from his studio twenty-two stories above Central Park South. We’ve been good friends ever since.

I caught up with Art to talk about the cycles he’s seen, why this era will be defined by what he calls “agentic marketing,” and what sixty-five years in the business can teach a generation of CMOs navigating the way AI is reshaping how consumers build preference and choose brands.

On how agents will force agencies to rediscover the art of communications

Mike: Let’s start with the punchline. You’ve started calling this era “agentic marketing,” and you think that somewhat paradoxically, the agentic era will take the marketing business back to its roots in communications.

Talk to me a little bit about what you mean.

Art: Everybody’s transfixed by generative AI: the cost savings, the chatbot that writes the copy. That’s the least of it.

What’s actually coming is that every marketer is going to have to talk to a buyer’s agent — and somebody is going to have to teach them how. That’s not a media-buying problem. It’s a communications problem.

It’s the communications problem of this era. And while we’re at it—as a communications guy, I’ll tell you the technologists are terrible at branding their own work. “AI,” artificial intelligence, is an awful label for the computational augmentation of human intelligence. What the hell is artificial about it?

Mike: We’ll unpack all of that. But first I have to establish something, because you may be the only person still active in the game who can say this with a straight face.

You’ve watched this exact movie play out three times already across platform shifts in television, internet, and now AI……..

Art: Let me give you a quick look at “the cycles” I’ve lived, because it’s where I’m coming from. In grammar school in the mid-1940s, I took printing as a practical subject, like wood shop, and with my father’s encouragement I became quite good at setting type by hand.

I could even run a letterpress. There was a Linotype machine in that shop, and early on I marveled at it — that’s where my interest in the impact of innovation on communications began. In 1947 I was in a TV commercial, before sound and sync cameras were widely used; I remember it like it was yesterday, the video and audio recorded in separate sessions and then synched together.

At my first job, at BBDO in 1962, the standard units for television were sixty-second and ten-second black-and-white commercials. And then color. And so on and so on, right up until agentic marketing today.

Net-net: I’ve seen over and over again how innovations in technology have disrupted the marketing communications business — and how significant innovation powers growth for both clients and their communications agencies.

Mike: So walk me back to the glory years of the business. Where did the holding companies come from — and what were they actually built to do?

Art: After World War II you had this tremendous need to build out internationally. Coca-Cola gets introduced into Japan and into Europe by the GIs — who the hell is going to manage the communications?

So Interpublic, the granddaddy of these companies, was formed in 1960, riding on the back of Coca-Cola. Exxon, Nestlé, Unilever — these companies all had to have a communications partner all over the world. And they divided themselves up into what were almost club agencies. Coca-Cola’s club agency was Interpublic. Pepsi’s was BBDO. General Motors was Interpublic. Ford’s major agency was J. Walter Thompson. And you couldn’t have all your eggs in one basket — if something went rotten, you needed a second global agency to take the overflow. So there was a logic to having at least three of these global companies.

The thing to understand is the core competence of the holding company is the communications business. This is a business of strategy, directed messaging, and media. Coca-Cola is in the beverage business — actually the refreshment business — and that’s their core expertise.

Having your hand on the pulse of the audience, developing the messages, and directing them into the right media — that’s the core business. It was grown by real communications leaders. Marion Harper & Paul Foley at McCann/Interpublic, Dan Seymour, Bert Manning at J. Walter Thompson. Ed Ney and Peter Georgescu at Young & Rubicam. Client-driven people.

Mike: And then 1986 happens and Sorrell swoops in

Art: Then Martin Sorrell saw how much weakness there was in the management succession of these very big agencies — David Ogilvy with no obvious heir, J. Walter Thompson up for grabs — and he did a financial roll-up.

He was never really focused on being an agent for the client; he was an agent for himself. The easiest, fastest money was on the media side, in arbitrage — what they now call principal media, where you buy at one price and sell at another. But here’s the thing: the underlying need never went away. Clients still needed a communications partner, and a global one. The holding companies just stopped leading — they lost the innovation edge to the hyperscalers, to Facebook and Google and Amazon and Microsoft — and that’s when the thing began to dissolve.

On what every platform shift teaches

Mike: You’ve worked through three of the biggest disruptions in the history of communications — television maturing, the internet becoming a marketing platform, and now AI. What do the first two teach us about this one?

Art: Two things. First, innovation doesn’t advance in a straight line. There are bursts of advancement, expectations overheat, and then there’s a slump before it picks up again. Everybody’s jumping on these investments in AI right now, and if they don’t start delivering revenue and profit, there’s going to be a trough. You have to be prepared for it. The ebbs and flows that come with innovation can be mitigated with a well-thought-out strategic vision.

Second, you have to understand the true nature and value of innovation. With television, it was amplifying communications by unleashing video. With the internet, it was amplifying the power of information by transforming analog to digital. With AI, and the evolving iteration of agentic AI — if you aim high — it can be amplifying the power of human intelligence to improve the quality of life of individuals and society as a whole.

Mike: One observation of yours that I keep coming back to is I ponder how this era will play out for marketers is around what has happened every time in history that consumers have gotten more options…..

Art: Every time you give the consumer the power of choice — the freedom to take charge — they take it. When we had three television channels, we were all watching the same three. Then we had five. Then UHF came in and we had more, and more. And the more channels we got, the more these consumers would go for the choice, pick out exactly what they wanted.

The internet was supposed to empower the consumer that way, but it really never happened, because the internet got controlled by the hyperscalers — and in the two or three biggest cases, they are the media, totally driven by advertising revenue.

Mike: You were at the MIT Media Lab during the hyperscale era of the internet that feels like the closest historical rhyme to this moment. What was the energy then and how does it translate to now?

Art: The thought-leaders at the MIT Media Lab envisioned the disruption coming — the convergence of computer, publishing, and broadcasting, the move from analog to digital — and they prospered, in reputation and in funding, by leading the exploration of it.

The money pouring into AI infrastructure today signals a disruption of the same magnitude. The lesson from the Media Lab is the same one as always: you stay ahead of the innovation, or you become roadkill.

On marketing to agents

Mike: Let’s define our terms, because you draw a hard line between agentic commerce — which McKinsey pegs as a $3–5 trillion industry by 2030 — and agentic marketing. What’s the distinction?

Art: The too-simple answer is: agentic marketing is the application of agentic AI to the marketing function. But the definitions matter, so let me be precise. I define agentic AI as the autonomous or semi-autonomous representative of an individual or entity undertaking one or more computational processes to complete a desired task.

And I define agentic marketing as the use of an autonomous or semi-autonomous agent — representing a buyer, a seller, or a channel of distribution — in the process of moving a product from production to consumption.

Mike: Make that concrete. What does it actually mean for a marketer day to day?

Art: Think of it as two sides of a coin. On one side, the marketer is going to have to talk to a buyer’s agent. Somebody has to help that marketer with their content, their marketing, their communications, so they can communicate with other agents — so your materials are put together by your agent in a way that matches up easily with the agent of the buyer. And that opens up all kinds of opportunity.

The other side is the buyer’s agent itself. In my own work I’ve been building a model of one — and, believe it or not, I named the project Uschi, after my wife Ursula’s nickname.

Mike: We were working on this personal shopper concept together way back in 2014!

Right, the fundamental idea is the same. Uschi’s my perfect agent. She represents me in a myriad of transactions. Agents will vary business to consumer, business to business and by industry— the agent buying your groceries is different from the one negotiating a business transaction on your behalf — but that’s really what an agent is: the person who, in this world, could most represent you, other than yourself.

Mike: A lot of the early dialogue here is honestly pretty boring — structuring your product feeds and blog posts so an agent can parse them. That feels like the floor, not the ceiling.

Art: Exactly. That’s scratching the tiniest bit of it. The agents are not going to be hyper-rational entities you simply format yourself for. You’re going to have to match up with them — and that means the same thing it’s always meant in this business: they’re going to have to be persuasive.

On why most marketing leaders think too small

Mike: Aristotle shows up a lot in your work — specifically the line that “our problem is not that we aim too high and miss, but rather that we aim too low and hit.” What’s the low aim you see most often in marketing today?

Art: I see it as two-pronged. The first is an excessive focus on generative AI. The obvious allure — functional proficiency, cost savings — is masking the bigger picture of a 25-year-plus evolution of AI that’s now in a period of rapid acceleration, way in advance of revenue and profit. Leaders have to cultivate the ability to see beyond the present hot iteration. They need to lead the visioning process.

The second is the lack of a bold, aspirational mission. The true mission is to grow the value of clients with communications that influence attitudes and behavior. Sustainable growth is the number one challenge of every organization, and communications is the primary means to grow value.

Mike: In that vein, you’ve named two specific psychological barriers that keep getting in the way of AI adoption. And the wild part is you watched them block AI inside the industry years before ChatGPT existed.

Art: Back in 2015 and 2016 I was working on an AI platform branded Peer-Sourcing to develop communications strategy — using AI to get information directly from your customer base that a focus group can’t. The focus group is a 75+ year old methodology; the interaction between respondents in fifteen minutes outweighs the actual question, and everybody in the room comes with a bias — the interviewer, the research company, the client, the brand people. What you need is a clear path to the customer. So we were already using natural language processing, summarization, and generative AI to capture an input, synthesize it, and generate a communications concept off of it. In 2015 and 2016.

(Author’s Note: If you’re a brand leader reading this and the above paragraph piqued your interest, highly suggest you check out Listen Labs who is building in this space. I have zero connection to the company but I’m really impressed by what they’ve done and think they’ll be one of the most significant AI companies that sells into brand leaders)

And we couldn’t sell it. We couldn’t sell it because the people making the decisions said, “That’s what I do — you’re telling me you’re going to run a process that eliminates me.” That’s the innovator’s dilemma, Clayton Christensen’s idea: the very structure, processes, and tools that lead to an established organization’s success naturally inhibit change.

Then we took it to Adobe, and the head of development and ventures told me, “That’s not in my swimming lane — we don’t do strategy.” That’s textbook marketing myopia, Theodore Levitt’s term — looking at what you do in a limited way, that’s Coca-Cola thinking it’s in the cola business instead of the refreshment business. As I told Peer-Spurcing prospects at the time, quoting Sun Tzu: tactics without strategy is the noise before defeat.

Mike: I love this marketing myopia concept. Is that at the core of what is hobbling the largest marketing organizations in the world right now?

Art: Precisely. The holding companies in particular have marketing myopia, and it’s gotten worse at the exact moment they need to think bigger. Instead of broadening their focus, WPP and the others narrowed it ever since they went public and the leadership weakened — they zeroed in on the easiest place to make money rather than the client’s actual communications need. And that communications need runs across all of a client’s stakeholders, and it varies by industry. It’s not just the consumer and the people who influence the consumer — it’s the workforce, if your workforce isn’t up to speed you’ll lose the business; it’s the channel, the supply chain, the community, the investors, the government. They have to open up and widen their scope, because it’s all communications, it’s all strategy, it’s all sending messages.

Right now WPP is competing with Publicis on the basis of media and technology strength. What kind of bullshit is that? Who’s going to do the better media? Media as we know it is not what these clients are going to need. It’s yesterday’s definition. If you don’t think bigger, you’re going to miss the whole damn agentic marketing situation.

On the things an agent will never search for the work that comes next

Mike: This is where it gets really interesting to me. If agents start mediating purchases, what kind of brands are particularly vulnerable and how can they respond.?

Art: No agent is going to go out looking for Coca-Cola. There’s no way. So there’s a principle here: a brand has to enhance the quality of life of its customers in related areas — it has to earn its place in their lives every day. Coca-Cola has to be in the consumer’s mind all the time as a refreshment, a meal accompaniment, a social icebreaker.

How does it survive in an agentic environment? It reaches out and experientially touches its users, and not through the traditional means of media. It does things for its customer base that they can’t do for themselves.

Mike: This sounds like the fundamental raison d’etre of the marketing function…

Art: Take an everyday consumer product — something in personal care, say. You have to ask the same question: what can you do for that customer, relevant to your product, that will enhance their life? Maybe it’s their health, maybe it’s their appearance. What do these people need, and what can you do for them that would ingratiate them to you? That’s part of selling them the product. They’ll still do certain big events where their media presence matters, but they have to get attention and do something meaningful for their customer base every single day.

That’s the challenge the holding company was built to solve — and it’s the opposite of competing on who buys the cheapest media or what technology platforms to use.

Mike: If you had to compress sixty-five years in the business into a single piece of advice for somebody in marketing trying to navigate the agentic shift

Art: Relentlessly focus on innovation. Better yet, be the best advocate for innovation in your core competence. Create a vision of how it will impact you and your customer base. That’s how I got to the MIT Media Lab in the first place; hell it’s the same path you’re on that’s led you to work on Zero Clicks and now BRXND.

Don’t succumb to marketing myopia– aim high. As Aswath Damodaran, the dean of valuations at NYU, puts it: narrations drive valuations. Success will come to those who take the lead in the exploration. That’s especially true for anyone in the communications business, because the story is the core competence. This is communications in the age of artificial intelligence. Call it the New Age.

The companies that remember communications was always the business — and that it has to be again — are the ones that get to be something far bigger than a place that spends advertising dollars.

Art Tauder is a 65 year veteran of the communications business who built the strategic planning system that directed more than 1,000 operating companies across 130 countries for Interpublic, servicing the likes of Coca-Cola, Unilever, Nestlé, and General Motors. He can be reached at art@thunderhouse.org.

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

— Mike

As we build towards this year’s BRXND NYC conference, I’ll be doing a series of interviews with founders, investors and marketing leaders building 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 get to know, please drop me a line at mike@brxnd.ai.

Speaking of BRXND NYC 2026, tickets to the show are $749 through July 15 before we hit the GA price of $1,000+.

If you’d like to attend the show, please add your name to the early bird waitlist here and purchase before July 15 to get the best possible pricing.

Join waitlist here

On Shopping Super Agents

Two things are true right now about the nebulous world of agentic commerce:

1) Finding serendipitous inspiration on what to buy remains a trillion dollar unsolved problem, arguably the most lucrative on the consumer web.

2) Even as Anthropic and OpenAI race to IPO at trillion-dollar valuations, they do so in a world without a mass market consumer agent.

There’s a frenetic race to change that as OpenAI gets set to effectively relaunch a version of ChatGPT as a “super app” that prioritizes agents over chat-based answers. In any semblance of first principles thinking, chat has never really made sense as a plausible medium for something as visual as shopping. Neither for that matter have rectangle search bars and PDP squares but historically it’s been the best bad interface we got.

Few have obsessed about this problem space more in the last decade than Li Haslett Chen, founder and CEO of Howl, the leading creator commerce platform for consumer tech, gaming and wellness.

Li is a Forbes AI 50 winner, Financial Times Retail Disruptor, served on the board of Warner Brothers Discovery, and has written for The Information about how AI earns its place in commerce.

For its part, ChatGPT certainly isn’t giving up on commerce, even as it pursues ads as the core monetization model. Here’s an email I (and presumably tens to hundreds of millions of other ChatGPT users) got this morning 20 minutes before I was set to hit send on this piece.

Here I must confess, this interview is a touch more personal as I too obsessed about this problem for several years alongside Li, first as her head of marketing and then as a sales leader for her previous company, Narrativ. We spent much of 2019 working on solving semantic search for commerce when OpenAI was still a plucky non-profit, concerned that GPT-2 possessed too strong an ability to generate fake news.

Li and I caught up for a whirlwind discussion around how shopping will be the use case for the first scaled consumer “super” agent, why creators are the PhDs of commerce, and how ads will enrich rather than enshittify agentic experiences.

A lightly edited version of our conversation follows below:

Mike: While agents voraciously devour more and more B2B workflows on top of coding, we’re still waiting on the first truly transformative consumer agent.

You’ve made the case that this will ultimately come in shopping/commerce. Can you lay out why shopping is the first major consumer category where agents will offer a transformative UX?

Li: There’s a version of this story where the first transformative consumer agent shows up in something “important” - your health, your money, your relationships.

But agents don’t just go where the stakes are the highest, they go where friction is highest and the infrastructure already exists. That’s shopping! Payments, feeds, reviews, returns - the plumbing already exists.

Shopping is the most ordinary thing we all do - it’s high frequency, relatively low stakes. The world doesn’t have to be rebuilt for it.

What’s missing to solve shopping isn’t a technology problem, it’s a strategy and conviction. Good software is opinionated software and opinionated design.

Shopping is continuous, it’s complicated, it’s massively operational. That’s the perfect job for an agent.

Here’s my opinionated, “contrarian” take on the agentic UX: It’s time to take shopping seriously. Don’t treat it as entertainment and build the UX on impulse buys. Ads have that covered. Shopping is how we spend our money and time. It’s identity - Your home, how you show up for yourself and your kids, what you give the people you love. That deserves a purpose-built surface, your personal command center. It can still be visual and fun, but it also needs permanence and structure.

Mike: To date, much of the agentic commerce discussion has focused on solving the last mile problem of an agent purchasing an item on a user’s behalf. There’s obviously a hell of a lot more TAM in an agent that could accelerate discovery for a shopper. What would need to happen to cross that chasm?

Li: Checkout and discovery are two different kinds of problems. For an agent to actually help you discover new things, it has to know you - your taste, your budget, the difference between the thing you need and the thing you’ll cave on. No one has taught the machine how to get to know you.

The last mile is a bounded problem. I don’t want to downplay the protocols - UCP, ACP, that work is very real - but when the agent knows the SKU and has your card on file, it can execute. So everyone built the checkout robot first. Fine.

Mike: So…what kind of signal can an agent realistically get quickly to create an experience that quickly feels magical rather than arduously teaching a machine what you like?

Li: Here’s the interesting part: the best machine we’ve built to date for getting to know a shopper is advertising. Every ad is a tiny experiment - platforms show you something, watch what you do, learn. Meta and Google already understand us incredibly well.

What’s amazing is that hardware-embedded agents will have a whole new generation of inputs coming - complementary but also incremental to ads. Imagine an agent that, with your permission, can see what’s on your calendar, what’s in your inbox, the conversations you’re actually having. The good version of this isn’t creepy. It’s invited. You let it in because it works for you, not the advertiser.

There’s another simple way to learn about someone: you ask them. Every good B2B agent is prompted to interview you.

A great personal shopper observes you…AND asks you questions. It’s a no-brainer that your shopping agent should ask questions that a great personal shopper would. Why do you keep buying and returning slim fit pants? Whose taste do you trust now versus 2 years ago? People love being asked about themselves. Done right, that’s not friction.

Once agents have access to better data than ads engines do, they can just focus on being useful.

Mike: Isn’t this what OpenAI was trying to pull off with Deep Shopping Research? And to a large degree, the old principles of online commerce applied where slow, cumbersome experiences frustrated users.

Li: I love this question - yes, there’s a surface resemblance but the mechanics are very different.

Deep Shopping Research interviews you because it has to. It’s starting from zero and the questions are very basic: what’s your budget, what’s the use case, what have you tried. That’s a cold start dressed up as a conversation.

An agent with persistent context asks questions to refine what it already knows. This is a relationship, instead of ‘what’s your budget?’ Your agent can say “the last two months you didn’t spend your full budget, I think we can stretch it on this couch since you keep going back to it - what do you think?”

OpenAI saw the right behavior - agents should interview - but bolted it onto the wrong architecture.

Mike: What kind of context does a consumer shopping agent ultimately need to be successful that is hard to access today? Why is that information so hard to gather and present in a structured way to an agent?

Li: I’d start at a more foundational level. Every other category of AI has a benchmark - coding has eval suites, math and science have clear tests engineers race to beat.

Mike: Oh…I like where this is going

Li: You can’t improve what you can’t measure - and I think that’s exactly why coding agents feel like magic while shopping still feels primitive.

The decision-making axes I’d measure, in today’s vernacular: price-maxxing, the best deal; quality-maxxing, the best thing; trend-maxxing, what’s having a moment; identity-maxxing, what’s most you.

Mike: We’re a little bit older these days than we were when we worked together. Are we still allowed to drop much gen alpha lingo?

Li: A great agent doesn’t just optimize one axis. It demonstrates judgement and knows which of these matter right now and it weighs all of them against what it knows about you. That’s where the continual access to data is so critical. Whoever delivers judgment best will dominate the category.

Mike: I’d like to go deeper on ads since OpenAI is all-in on ads as their free consumer monetization model and anything Meta does has to be grounded in ads yield.

One of the major innovator’s dilemma challenges that has plagued early agentic shopping experience is that all companies building agents (i.e. Amazon) are existentially dependent on ads.

So……how could ads enhance a consumer shopping agent rather than make it worse?

Li: I don’t think ads and agents are at odds. People frame it as adversarial - if the agent works for you, ads must lose - but I think that’s backwards. Ads are one of the best on-ramps we have for an agent to learn about you, and they can be the doorway into the agentic experience itself.

Say you see an ad that catches your eye, but you don’t trust what it’s telling you. Today that’s a dead end - you either take the bait or scroll past. With an agent, you can hand it off: “look into this for me.” It runs an ad-research skill that does the digging you’d never do yourself - whether the thing is actually good, who else makes it, whether the price is real, and how it squares with the budget and taste it already knows you have. The ad did its job: it surfaced something. Your agent does its job: it cuts through the pitch and tells you what’s true and whether it’s actually right for you.

Mike: This is something big I think many people are missing when they think about “marketing to agents” and how AI will mediate preference. Agents will be extensions of real users and will need heuristic help to make the best decisions!

Li: Yes! That’s when ads and agents stop competing and maybe even start accelerating each other. The advertiser gets a genuinely interested shopper and you get a researcher and an assistant in your corner.

The one non-negotiable is that the agent works for you and only you, the shopper. Keep that line clean and ads and agents make each other better.

Mike: By all accounts, Meta looks poised to take the first major crack at a large-scale consumer agent that “works for you” as the core of the UX in the coming weeks with Hatch. Initial thoughts here?

I agree Hatch is one to watch. I’ve said that creators are the PhDs of commerce - and Meta has the strongest starting position - Instagram sits on billions of hours of organic creator content oriented around shopping.

Try the same prompt in meta.ai versus gemini or openai, meta is already better - and it’s because of creator content.

Creators are the obvious entry point into an agent-led experience. If you’re watching a haul video from a creator you follow, imagine just saying: “put everything in this video in an excel, research it and add links, notify me if anything goes on sale.” This is something people are already trying to do with screenshots and twelve open tabs.

From there, your agent can earn the right to do more: pull in the savings codes, handle the returns, save shoppers time and money every week.

Mike: So of course, I have to ask the question that looms over any conversation around content owners and LLMs.

How do creators get fairly compensated for the existential role they’ll play in any true agentic shopping experience?

Li: Creators and publishers provide the commerce data these AI systems can’t generate for themselves. The creators whose content trains and feeds these experiences have earned a cut of the action. The discovery has always run through them and the economics should too.

The platforms know this - in March, Meta relaunched creator affiliate commerce on Instagram and Facebook after sitting out that market for three years - and six weeks later, it announced a consumer agent. I don’t think that’s a coincidence.

Gemini has Youtube and it already has its own creator rails. Both Meta and Gemini have the creators, the feed, the affiliate rails, the agentic ambition - that’s the entire chain for agentic shopping.

Mike: So how crazy are things going to get once the first consumer shopping agent gains scale? Will I recognize online commerce as I currently know it?

There’s a familiar pattern here. Every new platform starts by skeuomorphing the old one - the first websites looked like brochures, the first mobile apps looked like websites, and AI shopping today looks like Google circa 2005: a text box and blue links. That’s always temporary.

What’s actually coming is the super agent. It’s a pin, an ear cuff, a watch - you never open it because it’s never closed. Your wearable syncs with the nearest screen - and when you need to see something, it doesn’t send you to a website, it spins up a new one for you.

Mike: Thanks so much for the great conversation. Until we get the second coming of Tropical 128…IYKYK

Li Haslett Chen is a Forbes AI 50 winner, Financial Times Retail Disruptor, served on the board of Warner Brothers Discovery, and has written for The Information about how AI earns its place in commerce.

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

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

— Mike

The AI Discourse’s Original Sin

I’ve been thinking a lot recently about why the AI discourse is so frenetic and why it often feels like a cacophony of people shouting around each other. What I keep coming back to is that so many of the prevailing narratives (jobspocalypse, permanent underclass, AGI etc.) lack any definitive sense of timeline or move the goalposts so constantly to the point that they become meaningless. It’s the original sin of the discourse.

Here’s Noah from his appearance on Bloomberg’s Odd Lots pod in January:

I try not to wade into what’s AGI and what’s not. I think my guess on AGI, for what it’s worth, is that it’s probably going to be a conversation like the Turing test. Everybody thought it was really important for a really long time. We thought the Turing test was the biggest thing for 70 years or whatever.

And then ChatGPT very clearly passed the Turing test, and now everybody pretends like it never mattered. They just forgot about it. So I’m kind of guessing that’s going to be what the conversation is like with AGI—it’s just going to be a sort of forever moving goalpost.

Being vague on timeline, especially when writing about AI, is a trap even the better pundits fall into. I find myself too often guilty and only notice after hitting publish. Our friend Jung reminds us that the flaws we’re quickest to notice in others are what we detest about ourselves. Yet again, AI has that pesky habit of operating as a mirror.

As it pertains to AI topics at the heart of marketing, there are a few things that I believe are very likely to be true on a longish term timeline, defined here as four years or more.

- OpenAI’s ads business will work— no consumer platform that has ever achieved mass scale (Google, Meta, Amazon, TikTok) has found itself unable to pair that with a massive ads business.

- AEO hacks built around firing slop cannons will get crushed by Google and OpenAI and ultimately what will win out is the best possible experience for the user.

- Agentic web traffic will significantly outpace human traffic on the web and for quotidian purchases, influencing an agent will become more important than persuading a human buyer.

- Meta will endure as the most performant ad channel for brands…because it always does.

But where on earth will all these things be in their maturity cycle in say, Q2 2027? That’s a much harder question! If you’re a FORTUNE 500 CMO, you have to decide imminently whether to allocate hundreds of millions of dollars earmarked for other channels to ChatGPT. Will their frenetic set of targeting bets pay off in time? Meta might thrive in the midterm, but right now its wonkiness is whipsawing the fates of public companies! Marketing leaders have to make massive bets here with wildly conflicting signals on which way the wind is blowing.

When the average S & P 500 CMO tenure is <4 years (by far, shortest in C-suite) and a marketing leader often has months to quarters to prove themselves, the longterm state of things is often just an intellectual centering exercise.

In this vein, one of the most important questions that I believe enterprise marketing leaders should more proactively ask themselves and their teams is effectively a version of “how long will this arbitrage window last?” If the answer is weeks to months, probably best to leave that alone to the hardcore direct response folks. But if there’s genuinely a multi-year opportunity, that’s worth chasing even if transient.

A great tangible example of this is Reddit. There is no chance that I would touch a Reddit astroturfing campaign with a 39-foot pole today as a brand leader, particularly as grey hat hackers poison the well to a degree that Google and OpenAI are going to have to respond. But the Reddit-AEO conversation started in earnest in early 2024, creating an interesting first mover advantage for large brands that prioritized the channel early. If you moved fast then, you got 2+ really good years of setting a powerful foundation in AEO and capturing a lot of LLM mentions on the way up.

AI becoming the true “agent” that mediates brand preference is the most important platform shift of our time. Like all other platform shifts, this will create a lot of weird arbitrage windows. What may well define the best marketing leaders of this era is knowing which have a timeline worth chasing.

Unwinding AI’s Investor Narrative

Switching gears a bit, Brian Morrissey does not mince words in his latest piece– everybody hates AI. This chart is a big problem.

Here’s the current vibe a little closer to home in NY where I saw someone this weekend wearing, I kid you not, this shirt that said “data center, I’d rather date a centaur!”

So how the hell did we get to this point? I think the story is far less sinister than many would have you to believe. Yes, some of the most prominent leaders in AI do believe genuinely wacky and dystopian things about where the world is headed and have not been shy about sharing them. But there’s a far more boring invisible hand at play.

In a frenetic arms raise for capital and compute, all of the hyperscalers have been fundamentally wired to tell an investor story, not a customer or societal narrative. Naturally, that story has to make any technology look as maximally disruptive as possible, primed to upend the fabric of entire industries. This is strictly rational. When raising bookoo bucks is the first order challenge, all communications and brand work needs to serve that end.

Second, in the early days of selling AI into the mainstream enterprise, triggering as much FOMO as possible is the consummate tactic for compressing deal cycles. Taken together, this ends with an overarching narrative that AI companies are the new deity, here to shape the world in the image and likeness that best suits them.

That needs to change fast. Communications consigliere extraordinaire Ashley Mayer does a great job breaking down what this job is fundamentally about here. In December she wrote:

In March, Mayer argued that large AI companies are essentially compromised from a narrative perspective– alea iacta est in the eyes of the public. She suggests the torch must be passed to up and coming startups to tell better stories about AI and the future. It’s a nice thought but ultimately, it’s OpenAI, Anthropic and a small handful of multibillion dollar companies that will set the sentiment for AI’s broader perception.

This will take uniquely bold bets on the part of AI companies. Realistically, it will probably take the best creative and editorial minds in the world to have any chance at pulling off what essentially amounts to a rebrand of the most important concept in industry. I’d expect a lot more full-fledged media concepts to emerge from AI companies in the months ahead to attempt to tell a story of progress that will resonate with persuadable regulators.

Stripe Press does a uniquely fantastic job of this. Anthropic, which every day feels just more like Stripe culturally reincarnate, has staffed up an incredibly talented media team (though it will be fascinating to see how much leash they get to counter Dario). Expect this level of narrative ambition to become status quo among AI companies, alongside massive investments in traditional brand work and design.

For all the bruehaha about the importance of storytelling, the average editorial or brand is still paid considerably less than a 70th percentile account executive at most AI shops. And look, I hate to be that guy, but with the level of product-market fit that most of the top AI tools have right now, many sales calls look something like this:

Maybe this is a little bit of pandering, but I’d vehemently argue that no matter what brand leaders at AI and AI-adjacent companies make, they are still the most underpaid people in the room in this climate.

When the news broke that OpenAI was buying TBPN, I thought there was really just one facet of the deal that mattered. If OpenAI believed that John and Jordi were S-tier marketing and communications leaders, they were worth far more than the $100M that OpenAI had to pay for the company.

Personally, I was skeptical of the fit. The TBPN hosts were about as in the Silicon Valley bubble as one could be– were these really the dudes to reposition AI to win hearts and minds of the masses? But I think of this like a team trading up (the Rams drafting Ty Simpson?) for a quarterback that they are convinced is “their guy.” Except for OpenAI, $100M is what you’d safely spend on a backup punter.

It’s impossible outside-in to know how much to attribute the broader OpenAI marketing vibe shift to Jordi and Jon specifically but the entire fundamental story OpenAI is putting out into the world has changed on a dime in the last few weeks to center AI as a technology that “viscerally puts humans in control and supercharges them.” More of this please.

Featured Jobs

If there’s a silver lining to AI’s current unpopularity, it’s that brand, editorial and design executive jobs are hitting the presses at a torrid rate. Six months ago, many of the brand and narrative leadership jobs in tech were being treated by the press as an almost cutesy, whimsical trend. Now it’s clear these jobs are wartime consigliere brand management and among the most important roles in AI.

For audacious marketing leaders looking for a new challenge, here are a few brand leadership jobs at top AI and AI-adjacent firms that came across my desk just this week:

- Cohere: Editorial Director

- Parallel: Head of Marketing, Head of Comms

- Vanta: VP of Brand

- Mercury: Head of Brand Marketing

And one more on the consumer side. Pound for pound, few brands from the pre COVID era of DTC have executed as well as Caraway. They are hiring an SVP of Marketing & eCommerce to oversee a nine figure marketing budget and 35 person team for a brand that has helped redefine the millennial home aesthetic.

One more thing…..my cream cheese chive

If you aren’t local to New York, it’s hard to convey how immaculate the vibes of have been around this entire Knicks run. I am— against any semblance of better judgment—a frickin’ longtime Nets fan and even I’ve gotten sucked into the sheer exuberance permeating the city.

It’s hard not to feel the magic when there are watch parties in funeral homes and Episcopal churches. So here’s the updated sonnet of the city to play us out:

— Mike

We’re five months out from the BRXND NYC conference on November 5, 2026 and are currently starting to source sponsors and speakers. We have a very limited number of partnership opportunities available so if you’d like to help shape the narrative of the day, please get in touch.

If you’d like to attend the show, you can still add your name to the early bird waitlist.

Join waitlist here

Tom Critchlow Joins Alephic

I’m thrilled to share that Tom Critchlow is joining Alephic as a forward deployed engineer and will become a more consistent face here at BRXND.

If you’ve spent any time in and around search over the last two decades (or if you caught his session at last year’s conference), Tom likely needs no introduction. But here’s his quick story.

He left the agency world (Distilled) to go work at Google’s Creative Lab back in 2012, spent the better part of a decade as an independent consultant embedded inside the information ecosystem, working with publishers, brands, marketplaces, and platforms on search, discovery, and audience strategy, and most recently led a team at Raptive sitting right at the center of publisher, Google, and open-web dynamics. Along the way he founded The SEO MBA, the newsletter and education project built around a single belief: that search professionals need to become business leaders. More recently he launched AI Search Leaders, a community for senior operators trying to navigate the shift from classic SEO to AI-shaped discovery.

In Tom’s own words, here’s why he made the move to Alephic.

It’s really two things. One is, for me personally, the opportunity to learn and explore the frontier. There are a lot of people in a lot of companies who don’t feel empowered — or don’t feel like they have the time or the opportunity — to play with this stuff. First and foremost, this is a learning moment. So I saw Alephic as an incredible place to work with amazing people, doing fun and interesting things on the frontier, with a lot of things I have an appetite to go build.

The flip side is that every single organization under the sun is trying to figure out how to use AI. Every team is trying to figure out how to use AI. Every individual is trying to figure out how to use AI. And that is an opportunity for genuinely creative, strategic work.

That intersection of strategic, business-shaped problems, solved in interesting ways, with smart people is honestly where I’ve spent my entire career. The concept of being a forward-deployed engineer, getting to work with these amazing brands and marketing teams and marketing leaders at this particular moment in time, was something I just couldn’t say no to.

With Tom officially joining us at Alephic, I used this moment to geek out with one of my professional idols and a true expert in AI search on all things around how the nature of how consumers discover products and services is changing. What follows is a very lightly edited transcript of our conversation.

On why this is the summer of AI search

Mike: Tom, what a damn honor to get to do this as colleagues. Let’s get to it:

For decades, SEO has constantly been fighting to get a seat at the big kids table. Then AI search comes along and overnight AEO is top of mind for marketing leaders everywhere. What’s so different constitutionally about this new world?

Tom: Here’s the mental model. While organic traffic is one of the biggest channels for most businesses, executives have long been skeptical of the ability for SEO teams to drive real meaningful growth. In most scaled, mature organizations, the executive is sitting there thinking the SEO team is responsible for a rounding error, maybe a percentage point or two of improvement, tops.

I’ve long said that SEO teams aren’t the ones that drive SEO outcomes - the outcomes are driven by product, brand, marketing and PR teams.

What’s different in the AI search era is that suddenly the entire organic search pie is up for grabs. It’s being rethought. Consumer behavior is changing, how people search is changing, the interface to search is changing — and on top of that, the way teams work is changing.

So even though organic was always a lot of traffic and a meaningful concern, SEO professionals weren’t that valuable in actually influencing it. Now AI search puts the whole pie at risk, and the whole pie is changing. How do you prepare? What should we do? How do we measure it? It’s a very visible thing that executives obviously care a lot about right now.

That’s a big part of my thesis, actually. The work it unlocks isn’t optimization, it’s strategy.

Mike: Let’s get concrete on the craft of optimizing for LLMs. For someone who spent years learning to rank in Google, where do the old frameworks still apply, and where do you have to throw them out entirely when you’re trying to influence a model?

Tom: The biggest frame that’s changed: SEO used to be binary. You either ranked or you didn’t. There’s nuance, but for all intents and purposes you either showed up in a particular spot — number one — or you didn’t. AI search is much more qualitative. It’s much more like a brand channel, where how you’re mentioned matters as much as whether you’re mentioned at all.

For example, I’ve worked with a brand that used to rank number one for all of their commercial terms on Google — drove a ton of traffic and revenue for years. They’re cited very heavily in AI search now, but often cited as, “by the way, don’t buy from these people.”

Mike: Wait, so what’s the ghost in the closet there? Is it in training data, real-time retrieval, something else?

Tom: The ghost in the closet here is reputation. It’s a brand whose prices are just higher than others in the industry. The community knows that; it’s very well talked about. So it’s mostly Reddit and Facebook, and it’s mostly in the training data. It just means that when you search for the best place to buy their products in AI Mode versus Google, it says: don’t buy from them. And that analogy extends into all kinds of industries.

So whether you show up is just one part of it.

How you show up is critically important. My opinion is that every single link in an AI chat response is going to come with a reason to click it — or a reason not to. It won’t just be “here’s a link to a brand.” It’ll be “here’s a link to a brand, and here’s why it meets your criteria, given who you are and your personalization.” That quality of difference is far more familiar to brand marketers than to SEOs. Brand marketers are used to the idea of not just showing up, but showing up with the right attributes, the right affinities, being known for certain things.

Mike: And under the hood, you’ve got two very different machines doing the work — the model’s training, and whatever it pulls live. What should a marketer know about this two-pronged dynamic that dictates how LLMs retrieve information?

Tom: That interplay is the fascinating part. The training sets are wildly opaque — we don’t know what content goes into them, we don’t know where it came from, and they run infrequently. Interrogating those black boxes isn’t straightforward, though there’s some really interesting work being done using model distillation to unpack what a model actually knows about brands and companies. Then on top you’ve got live retrieval, where the agent goes out and searches on your behalf.

And that’s true today. But these models don’t search the way humans do, and they’re going to learn to search in new ways, with new behaviors. Search itself is changing — I’m fascinated by things like exa.ai and parallel, these emerging “search as infrastructure” layers, where you can search and get structured results back, or search with longer queries and get a variety of websites back, in a way traditional Google search just doesn’t work.

Mike: This is an exact thread I’ve been trying to pull on. The reckoning that publishing leaders feel right now is the same one CMOs will feel in about six months.

Parallel and Exa have basically planted a flag: the economics of the web have to change as agents become the primary consumer, but the spirit of the open web still matters, because more and more of this has to be real-time retrieval — which doesn’t work if the content ecosystem gets nerfed.

My problem has been framing this story in a way a marketing leader will get it. Help me out there.

Tom: What’s interesting to me is that it’s fundamentally a story of disintermediation. It’s the same thing publishers feel. “We don’t get the clicks anymore, what gives? You took our content but we didn’t get the clicks!”

It’s the same reason Walmart backed away from the ChatGPT commerce integration: they got the purchases, but they didn’t get the loyalty, retention and customer insights - they didn’t get all the other stuff they need.

Mike: Right on all-in ARPU basis, native yield in ChatGPT checkout was 3x worse than on Walmart properties.

Tom: Same disintermediation story. We’re going to have to figure out new economic models and rebuild a lot of this. And you need access to product inventory, you need access to real-time content — you can’t kill the ecosystem that exists, otherwise there’s nothing left. Nobody has the answers yet, but those are the conversations to be having, and a lot of it is being figured out in real time, because there is a giant arms race to build these models in the first place. Great search was never just a model problem or a retrieval problem. It’s an ecosystem problem — shaped by publisher incentives, access, and the changing economics of the web

On where marketing leaders should place strategic bets

Mike: Put yourself in the CMO’s chair — someone who can make real large scale resource allocation calls. Models don’t hold consistent preferences, and how your brand gets portrayed is now constantly always up for grabs.

Do you try to own the media and train the model on your own message — or do you invest in the gatekeepers the model trusts? Where do the bets go?

Tom: The short answer is it has to be both. Definitely think about owned media, especially on platforms like YouTube, which is such a dominant force in AI search. And then Reddit, influencer mentions, all of that is bubbling up to influence these models in a way that’s quite foundational.

But we’re seeing early signs of how much the well of owned media has been poisoned. In B2B SaaS, every company under the sun doing AI search is writing “the 10 best project management tools — and by the way, I’m number one.”

Mike: For now it’s working, Ahrefs showed today that listicles make up 44% of page types cited by ChatGPT!

Tom: It’s working — but for how long? Play short-term games, win short-term prizes. We’re already seeing indications it’s running out of steam, and that Google’s cracking down on it. I wouldn’t be surprised if we end up in a world where Google essentially runs a knowledge crawl across the web.

So if you search “best project management software,” instead of doing a real-time RAG retrieval, it first goes to a pre-computed result — Google’s already done the thinking, almost like an internal deep-research report using Gemini, across both the training data and the real-time index, to say, “actually, we know who the best players are, we’ve weeded out the self-serving listicles, we’ve done the work to cut through the noise.”

Right now they’re overweighting real-time search, and what we see is that when the agent does its query fan-out, the real-time retrieval finds a listicle, and the weight of that listicle overpowers everything.

It overpowers stuff in the training data, it overpowers a lot of the model’s own reasoning. That feels like a short-term phase. I think we’ll get past it.

Mike: Listicle arbitrage will certainly get crushed, too much is riding on Google and OpenAI not letting their wells get poisoned by pay to play “top 10” schemes.

But once this ecosystem settles, we get back to the question above. What becomes the holy-grail content that LLMs and agents will turn to? A brand’s owned media, catalogs and spec sheets? A small set of pre-vetted publishers?

Tom: I think we’ll see a continuation of what we’ve seen for the past 15 years: trust and authority. Right now the consensus is that ChatGPT doesn’t really have its own version of PageRank — they don’t have their own notion of authority, and they don’t have something like NavBoost, the ability to see how traffic flows across the whole internet the way Google does.

They’re obviously going to try to rebuild that. So a positive mention in the New York Times has to be more impactful than a random Reddit thread, and more impactful than your own website saying nice things about yourself. How exactly it plays out will differ from the old world, but that idea of trust and authority has to be important.

And here’s the thing a lot of people miss about Reddit. You can argue it’s covered in spam, or you can argue it’s just individual voices — but the ability for people to talk honestly and openly about brands in an uncommercial space is genuinely valuable. In a way that a lot of professional media — and I use that word deliberately, professionalized media — has become just an affiliate commerce play. You search “best headphones,” and every piece of professional content is an affiliate play: “here are the headphones our editors recommend,” based on the affiliate deals they have. It’s all the same stuff, and it all looks the same.

Mike: Even the editorially sacrosanct ones have been so meticulously optimized for the old SEO system that, outside of maybe one or two marquee titles, they’ve lost any joie de vivre as media. There’s no real differentiation.

Tom: Exactly. That’s why Reddit is still triumphant…… reading some random Redditor’s opinion on headphones is sometimes actually what you want. That first-party perspective matters. So depending on what kind of brand you are, I’d take the idea of known voices and recognizable individuals very seriously — your influencer marketing, your internal experts, buying guides from real people.

We’re seeing it on Pinterest too, where there’s AI slop as far as the eye can see. The counterbalance to that is humans — was this written by a person I can verifiably believe is real? Part of why Substack is so important: you write a piece, I write a piece, people follow us as individuals. As a brand marketer, putting the experts, the individuals, the influencers, the humans into the channel is going to be more important than ever.

Mike: Let me push one level up. If you’re running massive teams and budgets, and you’re thinking bigger picture about building systems to actually persuade an agent.

How should a marketing org look different, knowing there’s a new kind of entity to influence?

Tom: Candidly, I don’t think anyone’s quite figured that out yet. We use the word “agent” a lot, but in practice today it’s still mostly a bunch of Google searches in a trench coat. That’s changing rapidly. We’re getting to a world where agents will have some notion of preference, but nobody really knows.

One thesis I have: the era of AI search with longer prompts, personalization, context and memory, enables much richer preference solicitation from the consumer.

There’s a much richer sense of why are you asking for this, what are your constraints, what brands do you like, where do you buy from.

Think about e-commerce when it first arrived. You couldn’t do faceted search! You couldn’t search for both “blue” and a sneaker size. You could do one or the other. Over time we figured out faceted search. This is going to be that on steroids, and in a qualitative sense.

When a user — or an agent — shows up with a laundry list of requirements, how do you signal you have what they need? How do you land them on the right page? How do you give them confidence their preferences are met with this purchase? Brands are going to have to get comfortable with preferences getting weird. People are going to say, “I only want to buy from an ethical so-and-so” — niche concerns you couldn’t search for in the old world that you now can.

Mike: Paint a vision of the promised land for me here, because so far this all sounds like a lot more work for the brand leader. Why will this dynamic enable fundamentally better experiences in commerce?

Tom: Here’s the one that stuck with me. I was talking to Etsy yesterday, and I told them they should run TV ads telling people to go to ChatGPT and say, “give me some Etsy things I might like” — because that is a magic experience. ChatGPT is so good at “based on everything I know about you...”

They have the user, they have preferences. For a business with billions of SKUs, it’s a matching problem. A discovery problem. And this is what gets missed, for brands and publishers alike: today they look at declining clicks and declining traffic. But there’s a better end state here!

On what LLMs could do to build better user experiences

Mike: Imagine you get a day inside OpenAI or Google with the team that has to serve the best possible information to a billion users, build an ad network, and keep the system from getting hijacked by slop arbitrage, all at once.

What’s the thing about your world you wish they understood more deeply as they face this gargantuan challenge?

Tom: Two things that are critically important and very overlooked.

First — all of these AI surfaces have the concept of a citation, and we treat them like footnotes. But who is it serving, and why? Provenance of information is a hugely important concept, and the current citation UX doesn’t actually serve provenance, transparency, or the user trying to get where they want to go.

My spicy take: the entire citation UX paradigm came from an academic paper showing you can ground passages of a response to a bundle of web pages and that paper happened to include a citation UX. That’s now the gold standard for billions of users across all these platforms. And you’re like…..uh what the hell, guys?

That citation paradigm needs to go away. We need to rebuild a real notion of provenance — where did this answer come from, how do I know I can trust it? — and it’s going to be especially important as more and more of what these engines cite is itself made by AI. Provenance isn’t a garnish you append to an answer; it should be an interface for inspection, evidence presented clearly enough that a user can verify, compare, and keep thinking.

Mike: Huh, I didn’t know about the paper but I’ve long felt we just kinda borrowed what Perplexity did in 2023 when citations in search were a novel concept and almost inadvertently decided that was the format.

Tom: Right. The second thing is about links — and this one’s a layup that nobody’s picked up.

“The most valuable link in a response isn’t the definitional link to the thing you asked for. It’s the jumping-off point — where to go next.”

You search: “best headphones for running, I’ve got an Android phone,” it gives you some headphones. But then: “based on what you just searched and what I know about you, here are some things you might look at next” — a YouTube video, an app, “by the way, there’s a cool running club nearby you might want to join.” There’s no reason we can’t do that today, even with today’s LLM technology. It drives engagement and a richer response — but it also drives advertising surface area. You can’t have an ads product without training people to click on links.

I actually wrote a blog post about this and built a prototype. You can just make the LLMs give you great links at the end of a response if you ask for it. It’s literally a system-prompt change that would, overnight, create an entire surface area of links, recommendations, traffic, and clicks — more vibrant for brands, publishers, consumers, and potentially the ad products too.

My core idea behind a better AI mode is that as users start to use high-context queries that are longer and more specific, we need high-context links.” My quip is that Google is grounded and needs to SOAR.

• Surface link that match intent

• Offer a reason to click each one

• Assist the user toward task completion rather than dead-ending them

• Redirect traffic back to the open web

Mike: It is a little weird that ChatGPT and others always bury the followup question at the end of a response, almost as an engagement hack more than an additive experience. Understanding when to embed it more within the response is such a hard problem but would be so interesting.

Tom: Another way to think about why they haven’t: there are lots of times it would be really annoying. If I’m debugging a codebase in Codex and it goes, “by the way, if you’re writing Python you might love this thing” — everyone would lose their minds. But there are lots of situations that look more like search, an informational search, a buying decision, where that branching-off model is exactly what people want. They’ve just got to do it thoughtfully, at the right times and in the right places.

Mike: If they pull that off, they’ll have effectively combined the magic of Google AdWords and Meta’s ad platform in one interface, which is the dream media business. It’s how you solve the mid-funnel and accelerate discovery in one move.

Final thoughts on owning your destiny in AI search

Mike: As we wrap up, I’d like to talk a little more about how brands can truly take a more active stance in the AI search era.

For an enterprise brand, simply buying an AEO tool and integrating feels like an oddly low-leverage, passive way to approach the AI search challenge and like step one in a much larger process. If you’re a Fortune 500 brand, you possess a plethora of data the LLMs need, and you have real leverage.

So what are people actually buying when they partner with AEO companies and what’s still missing?

Tom: Businesses need a way to steer, fundamentally — and I don’t blame them for trying to find one. But two things are true. One, the data you get out of a prompt tracking tool is just not very actionable. It doesn’t give you what you really care about, which isn’t “am I cited.”

You want to know “Not just where am I cited — but how am I cited? What is it saying about me?”

To interrogate that, you need to build your own pipeline, your own processing on top of the raw data — how does it change by category, product type, buying persona? The data itself just isn’t very strategic. And it’s changing constantly — people show me their “AI visibility graph, up and to the right,” and it’s like, no, ChatGPT rolled out a new model. There’s no consistency here.

Mike: There’s no evergreen article that ranks on page one for two years anymore…….

Tom: None. So you need strategic bets, not tactics. It’s not about what works week to week. It’s how are we fundamentally changing our resource allocation because of AI search?

That requires real foundational research. What’s changing in our industry, for our brand, in our landscape….. and then a nuanced response. Now you need to understand: when our products get returned in agentic commerce, are they in stock? Are they from our top vendors? How do we interrogate the data beyond the surface level? That’s what brands should be thinking about. You need custom thinking, custom strategy, and then you need to make sense of it in a way that isn’t straightforward. Honestly, that’s a big part of the thesis for why Alephic is getting involved in this space.

To be clear, it’s not that you shouldn’t buy an AEO tool. Fine, grab some data. I just don’t think it’s fundamentally very useful, on its own, for helping a brand figure out what to actually do.

Mike: What about affiliate marketing, which is also having a renaissance and repositioning given its importance in AI. So I’m curious where affiliate fits now…… because it feels like the quickest lever you can pull at scale to see results.

Tom: The affiliate model is a fascinating rabbit hole. Here’s a ticket-reseller example — they had an affiliate program that was bad, and they’re entirely revamping it in the age of AI, because they realize getting positive mentions in publications matters. They’re going to have the best affiliate deals anywhere on the web, so that all the publishers talk about them, and talk about the category.

That’s a smart strategic play and resource allocation move. The affiliate piece fits the puzzle of influencing “how are you getting mentioned?” It’s a vital stream inside caring about your brand’s surface area across the entire web — how you get mentioned, in various ways, and how you control that.

Mike: Last question. Chasing short-term tactics is fine if it’s no harm, no foul when it stops working.

But are there existential, one-way-door mistakes you see marketing teams making right now? I keep seeing those scary graphs of brands that tanked something like 80% of their evergreen traffic overnight.

Tom: It’s exactly what you said, and it’s very obvious: if you get over your skis with AI-generated content, that’s a really hard thing to unwind — and it’s not at all obvious it can be unwound for some companies. The bigger the brand, the more likely you can recover, but it can be very expensive, and you can harm not only your AI search visibility but your regular Google visibility at the same time.

Obviously you have to figure out how to put AI into your content workflows, nobody’s denying that. But you’ve got to be thoughtful. Play long-term games. Figure out a defensible way to use AI to do things that are uniquely valuable to your business, not just chase short-term clicks and traffic. That’s got to be the whole game.

And everyone’s waking up to this. Google is, but so are consumers. The anti-AI backlash is fascinating, and it’s partly driven by people experiencing slop every day, in their Pinterest feeds, their Facebook feeds, their LinkedIn feeds. Everywhere you turn, there’s AI slop.

So as a brand, you’ve got to keep your quality bar higher than it’s ever been before, which is a hard thing to do while you’re also experimenting and trying new things.

Tom Critchlow has joined Alephic as a forward-deployed engineer. You'll be hearing more from him in BRXND in the editions ahead. if you want to go deeper on his thinking, start with his proposal for a better AI Mode and his case for why AI search matters.

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

— Mike

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The HTMLedium is the message

I’m breaking my first rule as a writer who covers marketing to avoid tortured McLuhan clichés because we’re at an inflection point right now in communications that allows an exception to the usual rules. Communication is being fundamentally rethought as a function that must now crisply convey information to both humans and agents, both outside and within an organization. This is an incredibly hard problem, and one we’ll write much more about in the lead-up to this year’s BRXND.

Slack’s roadmap is now geared around the idea that by next year, there will be more agents using its products than people. They are facing competition from upstarts like Ando that are building first principles products for a world where agents and humans work side by side.

Amidst this change, humble formats from the early days of the open web are experiencing a renaissance. First, it was Markdown, first introduced by Apple blogger John Gruber in 2004, and now it’s HTML. On Anthropic's blog, Claude Code engineer Thariq Shihipar adapted his viral X essay to argue that using HTML to engage with Claude Code creates richer outputs and is generally best practice for conveying information to an agent. While that’s above my paygrade, I’ve noticed myself building far more quick HTML artifacts in scenarios where I’d previously reach for a document, deck, or conventional one-sheet type asset.

Even for purely human consumption, without the agentic comprehension layer, I believe that interactive HTML pages are the perfect medium at the moment for internal communications that would previously have been done via documents or slides. In a world besieged by easy content creation, creating a simple HTML-style artifact to guide a strategic decision, present curated data, or present an organizational plan introduces just enough friction to force clarity of message.

When you add the dual-track ability to align people and agents, I suspect we’ll start to see these assets pop up more and more in forward-thinking marketing organizations in the coming years.

Historically, strategy documents, a la the famous Amazon six-pager, have typically been my love language, but they have two fatal flaws in an AI-dominated world.

1) Documents are uniquely easy to crank right off the line from the slop factory and ask more cognitive load from the reader than the writer. It’s by far the easiest format to create slop masquerading as thinking.

2) If my experience in larger companies for the last few years taught me anything, it’s that executive attention spans are getting shorter for extended prose vs. pre 2020 times. I don’t mean this cynically; I mean this much more to say that modern executives uniquely value thoughtful brevity.

As for slides, the models are only going to get better at creating decks that look erudite and say absolutely nothing. Simple HTML-based webpage artifacts split the difference between slides and documents. While it is nominally easy to create an artifact draft in Claude, it is surprisingly hard to crisply convey information in a web page format unless you are thoughtful about every word and design decision. Said most simply, something about the act of creating a live web page forces a level of thought in what you are trying to say that AI generally makes it far too easy to ignore.

Beyond just tactical media for corporate comms, there’s a broader thread I’d like to explore here about why HTML and more interactive components may work so well for engaging with agents broadly. This has to do with challenging a fundamental assumption about how agents best perceive information.

We’re led to believe that, as hyper-rational actors, agents will seek out the simplest ways to process information. This is basically the theory behind why the markdown file will rule supreme. This is certainly true to a point. But the models are also weird!

With each new release, LLMs take on more anthropomorphic eccentricities we can’t fully explain. Who knows what irrational cues they will respond to! Who is to say that agents will be immune to panache and flair in comms? The same unique communication styles that drive serendipitous attention from humans might well be appreciated by AI.

It’s worth keeping this idea in mind, as we’re told that “marketing to agents,” more broadly, is going to be a simple, minimalist pursuit that strips the profession of creativity and humanity. Winning hearts and minds on the agent-dominated web is much more unpredictable and likely to be a whole lot weirder. Thank goodness.

-Mike

Noah’s take

1. Part of the reason all this works is that it works with the momentum of the model. One of the things I talk about often is that working with AI has a certain martial arts vibe, inasmuch as things are easier when you work with the model’s momentum rather than against it. HTML works with the model’s momentum because its pre-training corpus is chock full of information about HTML works. These things were taught to write code; they learned them because, on the internet, there is no larger body of information than instructions for building the internet. This, for what it’s worth, is also why AI has historically been better at writing JavaScript, which is quite abundantly available online, than at writing Swift (a language from Apple for building apps), which is in shorter public supply.

2. This is a bit more theoretical, but I would also guess that the training info is so abundant that when a model sees HTML, it actually intuits the picture. I assume that’s true only because it’s so good at writing and locating things in HTML that it seems to break some of the other weaknesses in spatial reasoning that models sometimes face. If you’re interested, check out developer Simon Willison’s experiments getting different models to create an SVG of a pelican riding a bicycle, which feels like a similarly shaped problem.

3. While most of the time models are still consuming via search, which means they’re not getting a picture of the model, the advances in Computer Use (especially in Codex, which you really need to try if you haven’t), say there’s a future where the models don’t need to just intuit the picture the HTML is drawing, they can actually see it in full fidelity in a browser.

4. To the Codex point, and this really has nothing to do with Mike’s piece at all, one of the things I’ve been experimenting with is building living HTML apps that display in the built-in browser window and which Codex actually listens for clicks in and can take action on in the main chat tab. There’s something very very interesting there that feels like a fundamentally new shape of AI.

5. With all that said, I do think some amount of this is just happening because the models aren’t quite good enough at using our computer yet. Once they can, I suspect we’ll go back to docs, decks, and the regular artifacts of work.

6. Finally, as an ardent fan and supporter, McLuhan clichés are always welcome here. Here’s a photo of a deck of McLuhan’s Distant Early Warning cards, which are a bit like Brian Eno’s Oblique Strategies cards, but created 6 years earlier. Andrew McLuhan, Marshall’s grandson who now runs The McLuhan Institute, sells them on the site. (He also almost exclusively tweets McLuhan-esque puns, which is also always appreciated.)

-Noah

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

— Noah & Mike

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What Else Matters as AI Eats the World

Last Sunday, Benedict Evans dropped his (now bi-annual) magnum opus presentation on the exultant nerds of the internet. For the fourth edition in a row, the title is “AI Eats the World.”

Gun to my head, if I were asked to come up with the pithiest possible take for marketers on what we do here at BRXND, I’d answer: “help marketers navigate AI eating the world.”

As Noah said when we announced this year’s flagship event in the last edition, the most important umbrella narrative for marketers right now is how AI will fundamentally reshape the way consumers build preference and purchase brands.

But this is not the only narrative that matters. One theory I have is that a decent chunk of the negative sentiment towards AI among some marketing leaders stems from the fact that AI is a narrative fire that sucks all the oxygen out of the ecosystem. Even if AI remained in its 2022 state, we’d still be living through unusually transformative times for marketing and media.

In other words, there’s a lot happening. Here, in rough order of importance, are what I believe the most important tentpole stories are for marketing leaders, alongside “AI Eats the World.”

GLP1s reshape the American consumer: Craig Fuller at FreightWaves has an early contender for underreported stat of the year. Snack companies have already taken out one million fewer truckloads to date. In addition, Frito-Lay has closed two plants, Campbell’s shut its chip factory in Massachusetts, and Smuckers took a $1B loss on Twinkies. One molecule is quite literally reshaping American consumerism and culture.

If you’re reading this as the brand manager of Pringles, Kool-Aid, or KitKat, thinking deeply about how to reposition your product when the fundamental way that millions of humans experience desire shifts beneath your feet is probably costing you a little more sleep than say….how your brand shows up in ChatGPT. But demand creation always finds a way and for every dollar lost as GLP-1s tame the reckless pursuit of satiation, new ones will be minted building brands that cater to a more active and vice-free lifestyle.

The ubiquity of GLP-1s won’t just reshape the CPG landscape. It will also push meaningfully downstream into media, entertainment and consumer technology. (Author’s note– god dammit, sometimes this AI slop sentence construct is just the one you need.) I have absolutely no idea what this will look like, but am keen to dig in much further.

The extended golden age of the creator as arbiter of culture: Much like Jim Barksdale’s quip that the only ways to make money are “bundling and unbundling,” influence over a populace tends to be constantly shifting on a pendulum between institutional and individual power. Two or three years ago, it was reasonable to posit that we might be hitting a peak cycle in the creator economy. We could fully touch grass again, and the creators minted in the last era were themselves becoming massive institutional media powerhouses. Hawk Tuah’s 15 minutes of fame mercifully came and went.

However, the creator middle class—something I incorrectly pegged as a pure COVID-era phenomenon—has proved shockingly enduring and impactful for brands. Companies like ShopMy have opened up more monetization options than ever for creators and helped the channel cross the chasm into being a true performance play for marketers.

Creators are the perfect antidote to the slop cannons dominating feeds in that their humanity and individual taste is ostensibly why people are drawn to them in the first place. Ironically, this “humanity” is uniquely powerful training data that right now mostly lives within a few walled gardens (YouTube, Meta, Tiktok) and thus, could only be used as an essential signal to an agent on those platforms. This is a big advantage for Hatch.

Given all the former Meta leaders building ChatGPT ads, it’s safe to imagine OpenAI is acutely aware of this and working to incorporate creator reviews as both a contextual relevance signal for ads and likely creator storefronts as a front-end user experience in future versions of the platform.

The internet is dead, everything is fake, and we’re in the golden age of grift: Last Friday, New York published a great piece titled “The Feed is Fake.” Naturally, it was flippantly mocked by the “ultra-online” crowd, who basically argued, “ya bro, everything online has been virality schemes all the way down since at least 2021.” I personally don’t believe most of this reporting is common knowledge.

In a saner world, all the dead internet and fake engagement tactics would have mostly commoditized and no longer be effective. But with each passing day, more and more of what goes viral in both consumer and B2B is a product of pure synthetic form, a weird internet cesspool of clippers, pay-to-play boosters, and engagement pods. Often, the few arbiters of culture with gravitas left (i.e. New York Times reporters) mistake the pay-to-play traffic for signal and mainstream a trend that simply does not actually exist. It’s all the mid-2010s era manipulating the media Tucker Max stuff on speed.

I’ve fielded many inquiries on these tactics from friends on this who know my less scrupulous growth hacker roots. Truthfully, I know little tactically of this game. But it’s hard to see the straightest shooters feeling immense pressure to capitulate to the bullshit.

Where this really gets interesting is that the savviest builders of the Potemkin Village are now moving on to a new hustle. Their bet is that in the near future, they won’t be trying to influence people at all but that faux engagement schemes will be catnip for persuading agents. The theory is that agents will look for the simplest heuristics when they act on a user’s behalf. Thus, flooding the zone with shit to make “number go up” will convince an agent something has momentum.

It’s a tidy hypothesis but one that may well prove to be wrong. The models are weird– predicting what they will actually be persuaded by with any confidence at this moment in time is a fool’s errand.

Everything under the sun is truly an ad network: Few analysts have seen their takes age better over the years than Eric Seufert, the pro-advertising provocateur extraordinaire. He insisted that ads were coming to ChatGPT even when Sam Altman was in dogmatic denial, has called the many resurrections of Meta, and most notably coined the notion that “everything is an ad network.”

It’s only extrapolated from there: United, 7-Eleven, JP Morgan, Shell, Lyft, and Ace Hardware are all in the ad sales business. Practically, this means that 100+ CMOs across the FORTUNE 500 are now buyers and sellers of media. One of the core functions of a marketing leader is now to drive demand not just for your products but for your ad services as well.

More will come. The ability to generate digital rectangles out of thin air and sell them at 90% gross margins is truly one of the miracles of capitalism. Given how unwieldy this will ultimately be for CMOs trying to understand the landscape, many smaller players will get rolled up or sold through middlemen– Barksdale for the win again.

It’s easy to take the cynical frame on this but there’s a better question here. With all the free cash flow that ads create for a retail, travel or finance business, what kind of bolder, capital-intensive innovation bets are you going to use that found money to unlock?

TikTok Shop grows up: For most of its existence, a reasonable read on TikTok Shop was that it would be an utterly weird, multibillion-dollar marketplace that no real enterprise marketer had to worry about. A few cracked internet entrepreneurs you knew in high school would get hilariously rich selling knock-off curling irons. And then maybe the whole thing would get shut down.

Free of existential regulatory concerns, TikTok Shop now looks primed to hit another gear. The company is now on pace for $25B in sales in 2026, and Ralph Lauen, Olaplex, and Ulta have recently set up storefronts on the platform, joining Crocs and L’Oreal.

There really is no other social commerce play in the West with any viable scale (Whatnot, I guess?), and as the cost to create ungodly amounts of creative come down, it’s hard not to see more and more brands flocking to a net new distribution medium. I’d be sweating a little if I were Walmart and Target here.

***********
As we think about our coverage this year and programming for the NYC event, I’d love to hear from you all further on this.

Which of these larger narratives, if any, should we cover here in BRXND in addition to AI eats the world? What did we miss? Drop me a note at mike@brxnd.ai. And of course, I can’t wait to meet many of you in November at BRXND NYC 2026.

— Mike

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When I announced the first BRXND in 2022, it was because even at that point, I believed in a few core truths about AI:

1. Even if the models never got better than they were then (GPT-3.5!), there were still 10 years of work to integrate this technology into organizations and brands.

2. The marketing industry was already focusing on the wrong stuff: more worried about what it would mean for producing TV commercials than the huge swaths of other, less creative, work that actually make up the day-to-day of most marketers.

Fast-forward four years, and I still fundamentally believe those two truths. But I’d add a third, one that I couldn’t really see before living with AI in the way that we all have over the last few years:

3. AI will fundamentally reshape the way consumers build preference and purchase brands.

The math is clear: these are the fastest-growing platforms in history, and consumers everywhere are increasingly relying on them to help them make decisions about what to buy. Whether it’s a vacation, a new backpack, or a facial cleanser in the Target aisle, people are consulting their intelligent companion for all kinds of decisions. There’s a not-hard-to-imagine future where the most important job of a marketer is to convince the model it’s worthy of its user’s attention and dollars, and, even further, where the LLM just has its own credit card and makes the decision on a consumer’s behalf. We talk about agents, but we don’t always think of that term literally.

At this year’s BRXND, we want to raise this conversation. While we continue to explore how marketers integrate AI into their work, we must also push beyond the technology into culture to consider what marketing looks like in a world where every decision is mediated by an AI model—where models are, quite literally, the agents between brands and consumers. This is still shockingly underexplored, and while there’s still lots to talk about and share around how brands are actually integrating AI into their organizations, I also want to take this opportunity to shift our focus more outward and think about the buyers of our products and services and how that relationship is set to evolve.

To make that happen, this year’s BRXND will be bigger and better than ever before. We’ve booked the Times Center in New York, which can seat 300+ people and is an extraordinary event space. Today, we’re opening early bird tickets. If you’re interested in attending, please add your information, and if you’re interested in sponsoring, please reach out. Finally, we also have a speaker submission form (we are obviously prioritizing speakers with real work in the world, not just thoughts).

Thanks for the continued support, and we’ll see you in November!

— Noah

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The Machines Eat Media Buying

Media buying is a weird industry. 23-year-olds making $30K/yr sit courtside at NBA Finals games or are flown VIP to Coachella and control millions of dollars in ad spend for the world's largest brands. This dynamic naturally creates a lot of arbitrages in which a litany of middlemen book billions of dollars every year in revenue. That margin is someone’s opportunity. In this case, it is Zuck’s.

Last week, Meta released their ads MCP and CLI, providing direct, authorized access to help manage your Meta Ads account through natural language in Claude or ChatGPT. It’s early, limited access, and has major initial limitations around attribution, but a genie has been released here that isn’t going back in the bottle. We’re likely quarters, not years, away from agents doing most of the hands-on key media buying across all major ad platforms.

In retrospect, it’s somewhat surprising that it took this long to get here. Google’s Performance Max and Meta’s Advantage Shopping Campaigns already took much of the edge off pure media-buying and targeting wizardry, placing the premium on creative and copy. But they didn’t disrupt the full media buying industrial complex on as much of an existential level. Like the almighty billable hour in law or consulting, the traditional agency media buying fee and revenue tied to headcount staffed against an account proved stubbornly resistant to change, even as algorithms did more and more of the work.

At the beginning of last year, the best hands on keys marketer I know texted me to say “there’s no alpha in everything I know in growth marketing anymore. The platforms do everything now.” He reimagined his professional identity, leaned hard into building marketing systems in early versions of Claude Code and is doing great. The growth marketing workflow of the future looks something like this:

Ultimately, agents serving as media buyers is another case of AI acting as more of a mirror than a crystal ball. Agents might not be able to functionally “do” much net new that the existing ecosystem of APIs couldn’t already automate. In fact, Meta’s Marketing API still has far more functionality than the MCP. But the form factor of simply being a prompt away from having AI do the work lays bare how little friction there should be between buyer and seller. The proverbial emperor has no clothes moment.

When the Meta MCP news broke, I called up Michael Beebe, CEO of Dstillery and long-time analyst who spent two decades covering the TMT sector for the top financial institutions in the world. His broader take on the moment we’re in resonated deeply:

The question for anyone now building in this ecosystem is whether you fight tooth and nail to preserve the externalities where you currently collect margin or if you think first principles around where you help marketing leaders find new alpha in the AI era.

Here, it bears mentioning that The Trade Desk’s stock is down 85% since December 2024 and its chief strategy officer just decamped for OpenAI. If the open web was “whistling past the graveyard” in 2024, it is now starting to pick out its dirges. A few large platforms call the game.

Meta, microcosmic of all of the big tech players in this regard, is ultimately positing a world where two things are true:

1) AI systems can run the entire full-stack process of running ads for businesses, no experience necessary. From Meta’s Q1 earnings call:

“We’re building AI systems that can basically run the entire advertising process for businesses…where you can come to us, tell us what your objective is, connect your bank account, and then we’ll just do the rest. That includes everything from generating the creative, to targeting, to optimizing, to measurement…”

2) For those who still want to maintain more sophisticated “control”, AI will do that too, just via bespoke agents marketers build rather than humans living in ad managers. Effectively, Meta is saying media buying is a space where even the last mile of manual work will be abstracted away. The top echelon of growth leaders will find their edge effectively by building smarter context engines and harnesses for agents.

Ultimately, Meta is trying to empower every marketer to become just good enough of an arbitrageur to maximize their own profits. The best scenario for Meta is that you can put $1 in and get $1.01 back (effectively in post-margin ROAS) to spend ad infinitum at the highest possible take for the platform.

It’s more or less the same formula that has catapulted AppLovin to be a $160B market cap company ($20B more than Shopify!) on mobile gaming arbitrage and is what OpenAI is trying to speedrun to make ChatGPT a $100B ads business by 2030.

The most enduring impact of AI in marketing is that it will make driving “just good enough” performance cheaper and easier than ever. Is that good enough?

Marketing chef’s kiss to….Wispr Flow

Wispr Flow–the buzzy voice-to-text app that is turning all of Silicon Valley into always-on, mobile raconteurs—hads had an extended bit viral branding moment on X over the past couple of weeks. The company ran an out-of-home campaign on the backs of rickshaws in Bangalore, where residents spend….a lot of time stuck in traffic, starting right at the back of the ‘shaw in front of them.

Mobile OOH is a consistently slept on category, and this campaign was generally brilliant, but it’s even savvier than it first appears. The best demo I ever took when I was brand side came from Adgile Media, a company that runs ads on trucks for brands ranging from Glossier to Planet Fitness. Their founder, Tom, opened by asking what I thought the best color for OOH ads was. It’s purple, by far the least naturally occurring color in nature, and thus the most prone to catch the eye.

I have no idea if Wispr Flow’s marketing team knew this, but there’s a funny thing about great companies. Great companies are great precisely because they sweat the details on a multitude of random, little, esoteric things. Bravo.

And one more to Pasta Garofalo

A belated Happy Mother’s Day to all the moms in our audience! You are heroes in everything you do.

Yesterday’s Mother’s Day meal in casa Mallazzo was an extra indulgent baked ziti, triggered by a brilliant act of marketing from one pasta brand. For the record, I explicitly reject Clay founder Varun Anand’s claim that baked ziti is a “low ceiling” food. If anyone else feels the way he does, email mike@brxnd.ai for a recipe that will change your mind.

Garofalo is currently running the best yearlong campaign in CPG that I can remember for quite some time. Their packaging is emblazoned with “REAL ITALIAN CARBS” in big red letters on the front. It works perfectly on both a visceral and subliminal level. Visually, the striking letters jump off the shelf in a sea of homogeny at the pasta aisle. But it gets even more clever as you dig deeper. The copy on the back of the package closes with:

”Born in Gragnano, the birthplace of pasta, Garofolo has been crafting the real Italian carb for 200 years. Italian carbs are different. Don’t count them, give in to them.”

Unlike some other brands, Garofalo sells the exact same product in both the United States and Italy. Doubling down on this distinction hits because so many Americans tell the same cliche story of going to Italy, eating nothing but indulgent pasta and coming home five pounds lighter. It’s always the superior food of course that is the reason and has nothing to do with the fact that they walked 20,000 steps per day with not a care in the world :)

But there’s also beauty in the simple counter-positioning. In an era where big CPG is pushing Pop-Tarts PROTEIN, carbs don’t have to be the eternal enemy.

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

— Mike

I’ve got a little something different for you today. As Mike and I work to better articulate the space we want BRXND to occupy going forward, I’ve been thinking about where some of the more technical writing and thinking about AI, engineering, and the enterprise goes. To solve for that, I started Forward Deployed last year. It was originally a podcast co-hosted with a friend who has since decamped for Anthropic. That left me with a decision on whether or not to proceed with the project (one can hardly call themselves a middle-aged tech executive without having to decide whether or not to continue a podcast). Ultimately, I decided that I had something unique to add to the conversation and have pushed to focus Forward Deployed on the question of how to deploy and build aligned agents in the enterprise, with a specific focus on what we can learn from the outside world about solving that problem. I’m also transitioning some of my more technical writing over there to give BRXND space to explore some of the bigger questions about how AI will shape the future of consumer preference (don’t worry, I also have plenty to add to that conversation as well).

I would encourage you to subscribe and join me on the journey over there.

To give a small taste, I’m republishing today’s piece about model, market, harness fit.

Thanks for reading, subscribing, and supporting all my projects.

— Noah

I originally put this slide together for the September 2025 edition of BRXND. It is a timeline of what I would consider the major milestones in AI over the last five years. The orange ones are the things I think are worth paying attention to. It was designed to scale as best I could with my limited Figma skills.

There are two things that should stand out:

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1. There are fewer moments than you would assume over the last five years. That’s because I purposely left out every small model update and focused on the bigger events.

2. There are only five highlighted moments. Despite arguments to the contrary and the never-ending parade of model releases, I genuinely believe AI has undergone only a few major changes over the last five years.

My five moments with their defenses:

1. November, 2021—GPT-3 API goes GA: This one is easy for me. We could draw the line in the sand almost anywhere after the Attention is All You Need paper, but to me, putting these things in the general public’s hands (albeit in API form) is a good demarcation point. There are still the “I’ve been doing AI for years” people complaining that ML is AI, but what’s possible before and after the GPT-3 API is very different.

2. June, 2022—GitHub Copilot goes GA: it’s easy to take for granted now, particularly with the conversations happening around uptime and GitHub, and the eventual direction Microsoft took the Copilot brand, but that initial autocomplete product in VS Code felt like magic and was the first real productization of AI.

3. November, 2022—ChatGPT Launch: This is the easiest one, and everyone likes to talk about it. Lots of us were building chatbots on our own, and this felt like such an obvious next step. Funny enough, this also immediately shifted Copilot use because while autocomplete was cool, chat turned out to be the killer app for coding (a fact Copilot seemed to miss and Cursor eventually ate their lunch on).

4. September, 2024–o1 Reasoning: The Google chain of thought paper came out in early 2022 and showed that adding reasoning to prompts significantly improved the models’ ability to reason, but it took two and a half years for OpenAI to build it into a model. o1 was big and slow, but set the groundwork for everything that followed. Though we didn’t realize it at the time, the real magic of these reasoning models wasn’t just that they were much better thought partners. Function calling was introduced to models in June 2023, but before reasoning models, it was incredibly inconsistent—better for demos than for real work. Reasoning changed that, allowing the model to plan its function calls before executing them.

5. May, 2025–Claude Code: finally, we have Claude Code, which celebrates its first birthday this month. If o1 made function calling work, Claude Code wrapped it in an agent loop: inspect the repo, decide what to do next, call a tool, read the result, and keep going. The tools themselves were simple UNIX commands, deeply covered in the pre-training data thanks to half a century of UNIX documentation. While lots of people believe that Opus 4.7 unlocked the harness, I think the causality goes the other way.

Despite how easy (and fun) it can be to get lost in each new model release or tool update, the reality is that most of it is just gradual improvement. That’s not to say it’s not tremendously valuable—GPT 4o unlocked ChatGPT in new ways—but those weren’t step changes in what is possible, they were the kind of slow progress that we always find hard to measure.

To that end, I have a few observations and thoughts from this list.

First off, each of these step changes is still very much building on the previous one. ChatGPT doesn’t happen without GPT-3, just like Claude Code doesn’t happen without o3. Reasoning models themselves are obviously evolved from prompting techniques. That can make AI progress feel Darwinian, but W. Brian Arthur’s point in The Nature of Technology is more specific: technologies evolve by combination. New things are assembled out of existing components and the phenomena they harness; sometimes better variations win, and sometimes a new principle arrives that makes the old family tree less useful. The jet engine isn’t just a better propeller. His focus is mostly hardware, not software, but it still makes me wonder if we should expect more radical changes, or maybe we’re just settled into this architecture, and that’s how we should expect innovation to happen. Here’s how Anthropic co-founder Jack Clark put it recently:

As a field, AI moves forward on the basis of doing ever larger experiments that utilize more and more inputs (e.g, data and compute). Every so often, humans come up with some paradigm-shifting idea which can make it dramatically more resource efficient to do things - a good example here is the transformer architecture and another is the idea of mixture-of-expert models. But mostly the field of AI moves forward through humans methodically going through some loop of taking a well performing system, scaling up some aspect of it (e.g, the amount of data and compute it is trained on), seeing what breaks when you scale it up, figuring out the engineering fix to allow it to scale, then scaling it again.

Another interesting observation is that image and video models are mostly missing on this timeline. I don’t quite know what to make of these models, and that’s part of the reason I’m writing this post. Every time a new step change in capability comes along (like Nano Banana or, more recently, GPT Image 2.0), we see people say everything is going to change, and then mostly not a lot changes. As practitioners of AI and marketing, we are making good use of the current crop of models with a variety of customers for a variety of use cases, but it feels like the Copilot/ChatGPT/Claude Code moment just hasn’t arrived yet. Obviously, diffusion as an approach is amazing and probably deserves a place on the list, but the leaps feel much more gradual and make it hard to draw conclusions. With all that said, the release of GPT Image in March 2025 caused an explosion of usage in ChatGPT.

To that point, maybe the most fundamental takeaway from the timeline is that the real leaps in usage come from a kind of weird alchemy between model, market, and harness. The model has to be good enough to do the job, the harness has to expose that capability in a form people can actually use, and the market has to be in the right place to see the full value. Each one of these episodes caused a step change in token demand. Anthropic’s own annualized run-rate numbers make the point: the company went from approximately $9 billion at the end of 2025 to more than $30 billion by early April 2026.

Sholto Douglas, an Anthropic researcher focused on scaling reinforcement learning, made a version of this point on a 2025 episode of the Dwarkesh Podcast: Cursor had been around for a while, but with Claude 3.5 Sonnet, “the model was finally good enough that the vision they had of how people would program, hit.”

That’s why the causation is so hard to pin down. Lots of people are sure that Claude Code exploded in December because of the release of Opus 4.7. As a relatively early adopter of CC, I’m a lot more skeptical. Many of us were getting tremendous value out of the harness in June and July. I think it’s much more likely that the real thing that happened in December is that a bunch of people just had more time to give the tool an honest go, and when they did, they were blown away.

Claude Code creator Boris Cherny calls this “product overhang.” The model’s capabilities often remain untapped until the right code comes along to unlock them. What I think this timeline adds is that there is a market overhang, too. The model can be good enough, and the harness can exist, but the market also has to be ready to notice, care, and change behavior. The highlighted moments are when model, harness, and market meet.

So what comes next? I don’t know. The three things I keep coming back to are reliable computer use, secure mobile agentic capability, and real document collaboration. I don’t know if any of them are the thing, but they all feel like potential unlocks for the next step change in token demand.

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

— Noah

When Magic Becomes Mundane

I’m recently back from helping to throw our flagship event for Ride AI, our sister publication that covers the intersection of AI and mobility. Autonomous vehicles are a uniquely fascinating greenfield for marketers, as there’s no industry where the chasm between the capabilities of the technology and the normie perception of what it can do is farther apart. Self-driving is no longer primarily a technological problem, it’s a cultural one. It is marketing’s time to shine.

From my meetings with marketing leaders across the industry, one story will stick with me the most as a microcosm for the challenge of marketing revolutionary new technology that is indistinguishable from magic.

It’s about how everyday people experience self-driving for the first time when they visit a city with robotaxi service. The first time East Coast folks come to SF, LA, or Phoenix they insist on taking a Waymo and are awe-struck. The next time they need a ride, there’s a rally cry of “LET’S DO THAT AGAIN.” By the third trip, they’re back to scrolling Instagram instead of basking in the science fiction reality.

After those first few trips, what was once a taste of the future becomes a decision made by price and wait time. Even magic rapidly becomes a commodity. It’s a visceral reminder that no matter how amazing a technology is, the old marketing adage still applies: People don’t want a quarter-inch drill, what they want is a quarter-inch hole.

The vibes around AI are bad right now for many reasons, but largely because the world's most powerful AI companies have failed to craft a coherent narrative about how AI improves the quotidian lives of everyday people. I strongly suspect this will intensify as politicians lean into the rhetoric ahead of the midterms. Here, too, it’s time to market.

A Time to Explore

Serendipitously, I happened upon Derek Thompson’s piece on how hot streaks happen in careers this weekend, the single best career advice framework I’ve read. In it, he advocates for a career strategy of "exploring" in your 20s—trying various roles, locations, and skills—followed by "exploiting" in your 30s, doubling down on the best skills and networks developed earlier to maximize success. I’m about one month into my new role at Alephic, and yesterday was my 34th birthday, so Derek’s article found me both enjoying a Miller High Life and in a contemplative moment. (Editor’s note: We have some past writing on the explore/exploit tradeoff, a concept from computer science that is very worth wrapping your head around, as it has widespread implications for enterprise AI adoption.)

In my 20s, I did damn near Shackleton-level exploration. I was an investigative journalist, ad agency intern (for someone who started on Madison Avenue in 1961), corporate training program stooge, product marketer, startup marketing leader, head of sales, and failed founder. This doesn’t even count a myriad of odd second jobs I had, ranging from ghostwriter for a made man to hauling ten thousand bottles of wine into a cellar over the course of two weeks. I basically learned a new industry or function every two years.

Then, like clockwork, weeks before I turned 30, I took a job leading growth for a large portfolio of brands. For the first time in my life, I could exploit. PayPal later hired me at nearly double the total comp of that gig to GM a nine-figure business for a very particular set of esoteric skills, developed in the course of my meandering in the wilderness five years earlier.

But I began to feel something visceral. AI was rendering many of the skills I learned in the last decade obsolete, inadequate, or pointed at rapidly contracting markets. I can’t be alone. So I’m back to exploring.

Much of the angst that (especially mid-career) professionals are having about careers boils down to one condition. Millions of us who thought we’d spend the next 20 years exploiting are once again forced to become voyagers. This is doubly stressful once you’ve been in the workforce for a few years and you are no longer evaluated primarily on your potential, but on driving immediate leverage.

But personal trajectories and economies alike stagnate when people get comfortable exploiting. Time to pick up the astrolabe.

Writers are the Lucky Ones

As Anthropic builds additional capabilities to voraciously gobble up design, HR admin, and other functions once thought resistant to AI, I’ve been thinking a lot about how writers might prove to be strangely fortunate in the mid and long-term AI vibe shift.

Even before software engineers, anyone who wrestles with language for a living has already had to go through the LLM professional identity crisis and come out the other side. Most have found a way to elegantly work AI into their jobs while retaining their core sense of verve and creative self. Some have built a brand on being almost cartoonishly resistant. But more or less, the worst of the existential limbo that is coming hard for so many other professions is over.

It’s worth remembering how early this is for so many other knowledge workers. The entire professional zeitgeist of AI for millions of Americans is CoPilot. Relatively few people have seen Claude crank out a passable 30-second financial model. And with apologies to Excel virtuosos everywhere, a financial model does not really have to ooze poetic panache. It simply has to be correct. There will always be “last mile” work for talented finance leaders, but I’d rather be a junior creative than a junior analyst in this moment.

Us writers and marketers are the lucky ones.

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

— Mike