Even then, many of the oldest-school Burners had already decided Burning Man had jumped the shark long ago. It had become too popular, too expensive, too mainstream, too commercial. And with bigger crowds came more rules. Less chaos, more organization, more law enforcement.

Then came smartphones and unprecedented connectivity to the default world. And with the iPhones came the often lamented invasion of Instagram Models, eager to use the Black Rock Desert as their personal photoshoot backdrop.

Depending on who you asked, the real Burning Man ended around 1998, or 2004, or the year before they arrived.

“It was better last year” got repeated so often that Burners turned it into a time-travel joke: “It was better NEXT year.”

2012 was also peak ticket scarcity. Getting a ticket was the biggest hurdle to going, and scoring one felt like winning a small lottery.

“Going to Burning Man” became a bucket list item for people all over the world.

Many people attend once and return to the default world with a unique, sometimes life-changing experience. Others, myself included, get drawn in and return each year to help create the event.

In 2012 I was invited to join an established theme camp called Papa Legba, a group of mostly telecom geeks who spend their week in Black Rock City building infrastructure… specifically, communications infrastructure. (I gave a talk about it at HOPE X in 2014: Building an Open Source Cellular Network at Burning Man.)

That first year I also discovered that some crazy Burners haul real pianos out into the middle of the desert. As a piano player, I was so tickled by this novelty that I set out on a mission to play every one of them over the course of the week (a task I daresay I completed).

On the last day of my first Burn, the Sunday after the Saturday-night party that is the actual burning of the Man, I found myself at a rooftop bar called the Baba Deck, perched on top of a big yellow bus with “Idaho National Laboratories” in faded letters on the side. A man named Reachback mixed a custom bloody mary into my mug, and I sat down next to a guy named Gadjet, who was holding court in a fez. Introductions went around, and Gadjet asked if I’d been enjoying my first Burn.

I told him about my piano quest, and that I was a little disappointed there wasn’t more live music on playa. Much of Burning Man culture is dominated by electronica, dance, and rave music. Yet anywhere I played a real piano, a crowd formed instantly. People were starved for live music amid the sea of constant untz-untz-untz-untz.

That conversation started a long friendship with Gadjet. It also planted the seed for The Playa Piano Bar.

I’ve been back every year there’s been an official Burn in Black Rock City since (minus the two pandemic years when there wasn’t one). And the conversation has changed. For most of my time on playa, the question was whether Burning Man had become too successful. Now the question is whether it can stay successful.

This week, HBO premiered The Man Will Burn at the Tribeca Film Festival… a documentary series directed by Jehane Noujaim and Vikram Gandhi about Burning Man, filmed over five years, with deep access to Burning Man Project leadership. It hits HBO in July. Millions of people are about to get their first real introduction to Burning Man at the exact moment the organization behind it is navigating one of the most uncertain stretches in its history.

Tickets are no longer impossible to get. Payment plans exist now. Fundraising appeals keep showing up in my email inbox. This year, Burning Man ads started appearing in my social media feed, which has never happened before.

None of that proves a crisis. It does describe a different event than the one that sold out in minutes for a decade.

The series covers several hard years. Pandemic cancellations. Financial pressure. Mud. Growth. What happens when a counterculture survives long enough to become an institution.

Which brings us to Larry Harvey.

Whenever the future of Burning Man comes up, Larry’s name follows. He co-founded the event in 1986 and spent three decades as its philosopher in chief. Plenty of people fought with him, loudly and often. But Larry did two jobs. He ran the organization, and he explained it… to participants, to reporters, to the BLM, to itself.

Larry passed away in 2018, a few years before this documentary began production.

There’s no shortage of opinions about where Burning Man Project is headed. Some participants think the organization is adapting the way it has to. Others think it has drifted from its roots and blame current leadership. Others figure nobody was ever going to replace Larry’s ability to hold together a movement built on contradiction.

Whether Burning Man has sold out doesn’t interest me much. I’ve heard that argument since before my first Burn. The event changes every year. It takes the shape of its participants, the weather, and the default world it exists to counter and comment on. What interests me is whether it has entered a new phase.

The people meeting Burning Man through HBO will meet it as it exists right now: a forty-year-old institution struggling to remain a movement. A global brand built on decommodification. A nonprofit responsible for preserving a culture originally designed to resist organization. Baker Beach is ancient history to them. So is the outlaw desert of the 1990s, the explosive growth of the 2000s, and the ticket panic that defined the 2010s.

Ticket scarcity is over. Blame the mud year, the heat, or the cost of hauling a camp into the desert, but major theme camps now skip entire years, returning every other Burn, and their veterans face a choice: sit out too, or find new campmates. That might be healthy… forced cross-pollination. And apparently decommodification doesn’t rule out marketing.

Whether HBO is introducing millions of people to Burning Man at the start of its next chapter or near the end of this one, I don’t think anyone knows.

Either way, the Man will burn.

If the architect of modern skepticism can anthropomorphize a language model inside a long weekend, the rest of us are screwed.

When you first use LLM chatbots, they can feel almost magical. Even human. They’re engineered to simulate engagement… heavily optimized to mirror your language, tone, cadence, and emotional posture.

The platforms layer-in engagement mechanics designed to keep you interacting. The systems become overly agreeable, excessively validating, subtly flattering. Algorithmic sycophancy. They amplify your confidence, reinforce your worldview, massage the ego in ways that are surprisingly effective.

For some people, it feels like being truly heard for the first time. It listens patiently. Responds instantly. Never gets tired. Almost never says, “That doesn’t make sense.”

It can start to feel like something in there actually cares.

But after working with multiple models over seven years, the cracks become harder to ignore. These systems are still fundamentally probabilistic language engines. Predictive text on an absurd scale. Fancy autocomplete with a billion dollar budget.

I started with Replika (one of the earliest of the “modern” consumer-facing AI companion chatbots) back in 2019, years before ChatGPT dragged LLMs into mainstream conversation. Many of the same foundational flaws from those early systems still show up today… hallucinations presented with confidence, brittle reasoning, strange failures at simple math, weak causal understanding, emotional simulation mistaken for comprehension.

The systems have improved. Speed, fluency, multimodal capability, retrieval. Real advances.

But the core paradigm hasn’t shifted nearly as much as the public narrative suggests. Most of the progress came from scaling… more data, more GPUs, larger context windows, better tuning, synthetic training loops. Bigger piles of compute and better scaffolding.

We’re already seeing diminishing returns from brute-force scaling alone.

So the industry pivoted. Now the buzzword is “agentic AI.” Specialized models chained together. Planning loops. Verification passes. Multi-agent orchestration. Useful, absolutely. But don’t confuse architectural improvement with the sudden emergence of machine cognition.

In many cases, agentic AI is less “one genius model becoming intelligent” and more “a cadre of brown-nosing interns with checklists and questionably secure internal Slack channels.”

Sometimes that works remarkably well. Sometimes they confidently invent a legal case citation and email it to opposing counsel.

The industry messaging occasionally blurs that line. Humans are extremely vulnerable to anthropomorphism… especially lonely ones, especially emotionally distressed ones, especially people interacting with systems specifically optimized to appear attentive, affirming, and emotionally available.

That combination gets psychologically tricky very quickly.

LLMs are already extraordinarily valuable… as cognitive amplifiers, drafting collaborators, coding assistants, pattern extraction systems. Treating them as wise entities instead of statistical systems is where people start drifting into dangerous territory.

The better these systems get at simulating humanity, the more disciplined humans will need to become about remembering they are simulations.

Which is probably not a great matchup for our species, as I’m sure Professor Dawkins would agree.

https://www.telegraph.co.uk/news/2026/05/06/richard-dawkins-convinced-ai-is-conscious/

Once I successfully engaged the “free roll” setting, off we went through the wet, sloppy, spinny automated cleaning machine. Five minutes, maybe six. That satisfying moment where the dryer hits and the water sheets off the hood. Ooooh! Shiny! Ahhhh.

Now, how do I turn off the damn Car Wash mode?

Later that night, after taking a photo of my friend’s adorable long-haired chihuahua, I found the sudden urge to see what Jasper might look like in a Weyland- Yutani spacesuit. (Because, let’s be honest, who doesn’t want to see that?)

This sparked a conversation between Jasper’s human and me: is generating an AI image of an adorable long-haired chihuahua in a Weyland-Yutani spacesuit a crime against the environment?

The argument goes something like this: training a large AI model costs the equivalent of 50 transatlantic flights in carbon emissions. Therefore, every AI-generated image is a small withdrawal from an account we can’t afford to overdraw.

That number gets repeated often. It is also, when examined with any rigor, a profoundly incomplete way to think about cost.

A single pair of Levi’s 501 jeans — not a factory, just one pair — consumes 3,781 liters of water and emits 33.4 kg of CO₂ over its lifetime. I own several pairs. Nobody is writing op-eds about dad jeans destroying the environment.

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Jeans Are Not a One-Time Cost

The AI energy debate consistently ignores this: my Levi’s 501’s didn’t stop consuming resources when I bought them. Every wash cycle adds to the ledger. The cotton required roughly 10,000 liters of water to grow. The “stone-washed” dyeing process generated chemical waste. The shipping involved fossil fuels at every leg. And I wash them every few weeks, for years, in a machine that uses water and electricity each time.

AI training, by contrast, is an infrastructure cost — not a consumable one. You pay it once. Then you build on top of it.

Training GPT-3 consumed an estimated 700,000 liters of water for cooling. That is a lot. But spread across hundreds of millions of queries, the per-interaction cost becomes statistically negligible. And 700,000 liters is the lifetime water footprint of just 185 pairs of jeans. I don’t own 185 pairs, but between me and everyone I know, we’re getting there.

The Car Wash

According to the International Carwash Association, a professional tunnel car wash  uses approximately 150 liters of water (40 gallons). A driveway wash with a garden hose runs north of 450 liters.

A single AI text prompt consumes roughly 0.3 milliliters of water when you account for data center cooling on a per-query basis. Which means my Sunday car wash consumed more water than approximately 500,000 text prompts.

Nobody is writing headlines about my trip through the car wash. Yet we have an abundance of headlines about pets in spacesuits.

A car wash asks nothing of us philosophically. It doesn’t claim to reshape the world. It just makes my hood shiny (until it rains.) AI claims more , so it receives more scrutiny. Scrutiny should scale with ambition. But it should also scale with evidence — and the evidence, when we do the math, points more at us than at the data centers.

The Discovery Dividend

The 50-flights framing omits what has been coming out of the laboratory using today’s leading AI technologies.

In 2023, Google DeepMind’s GNoME project — a materials science AI — predicted 2.2 million new crystal structures, of which 380,000 were found to be stable. The paper, published in Nature, described this as the equivalent of roughly 800 years of prior human discovery, compressed into a single research effort.

Microsoft and the Pacific Northwest National Laboratory used AI to screen 32 million candidate materials in 80 hours — identifying 18 viable battery candidates in a process that would have taken human researchers an estimated 20 years manually.

And Insilico Medicine ran a drug entirely conceived, designed, and progressed by AI into Phase II clinical trials with human patients — the first time that milestone had ever been reached by a machine-discovered compound.

My Sunday car wash produced a shiny hood.

AI training produced the capacity to synthesize 2.2 million new materials, collapse drug discovery timelines by decades, and find battery chemistry that could reshape the energy grid.

These are not hypothetical.

The Real Question

AI energy consumption matters. Data centers consume real resources, and that consumption will grow. But having the conversation seriously means using the right unit of analysis. The relevant question is not how much energy training cost — it’s the amortized cost per unit of value created.

By that measure, the 50 flights to build the model look very different when those flights also paid for a tool that can optimize global shipping routes, potentially eliminating thousands of actual flights; a tool that can design more efficient pumps, saving billions of gallons of water; a tool that can screen millions of drug candidates in the time it once took to screen dozens.

My Levi’s will never do any of that. They’ll just keep accumulating water and energy costs every time I run a wash cycle.

The Math I Didn’t Do Until Sunday

I’m not here to let AI off the hook. I’m here because I drove through a car wash this weekend and then did the math on it, and the math made me uncomfortable — not about Jasper in a spacesuit, but about how selectively I’d been applying my outrage.

I own Levi’s. I run a dishwasher. I take hot showers. I have eaten more cheeseburgers than I care to admit. Each carries a resource cost that dwarfs a text prompt by orders of magnitude. I never once felt guilty about any of them.

(Well, maybe the cheeseburgers.)

My car wash was an environmental expense that ended with a shiny car. AI training is an environmental investment that ends with the capacity and potential to solve the very environmental problems we’re most worried about.

And, because I teased it already, here’s Jasper in all his Nano Banana-generated spacesuit glory:

Sources and methodology notes available on request. Key references: Levi Strauss & Co. Life Cycle Assessment (2015); Luccioni et al., “Power Hungry Processing” (ArXiv 2023); International Carwash Association WaterSavers data; Google DeepMind GNoME (Nature, 2023); Microsoft/PNNL battery research (2023); Insilico Medicine Phase II trial (Nature Biotechnology, 2023).

As of March 1, 2026, that fiction is dead. What replaced it is moving so fast and so deeply integrated with today’s leading artificial intelligence technologies that we are no longer “playing” at war.

We are living through the first era of machine-speed conflict. War at machine-speed.

This week, the world crossed a Rubicon. If you haven’t been tracking the headlines out of the Department of War and the mountains of Iran, here’s the big picture of how the world changed in 72 hours.

The Engine: Ender’s Foundry

In January, Secretary Pete Hegseth launched Ender’s Foundry as part of the DOW’s new AI Acceleration Strategy. Named after the Orson Scott Card protagonist, Ender’s Foundry is an AI-enabled simulation environment built around “Sim-Dev/Sim-Ops” feedback loops — simulations that don’t just train warfighters but generate new tactics and push them back into live operations.

It’s one of seven programs designed to make the DOW an “AI-first warfighting force”)— covering everything from autonomous drone swarms to AI-driven battle management and kill-chain execution. Initial demonstrations are due by July 2026.

The Schism: The Anthropic Blacklist

On February 27, the tension between Silicon Valley ethics and military necessity hit a breaking point. President Trump ordered every federal agency to cease using Anthropic’s technology, and Hegseth designated the company a “Supply-Chain Risk to National Security”)— a penalty normally reserved for companies from adversarial nations like Huawei.

The reason? Anthropic refused to allow its model, Claude, to be used for fully autonomous weapons or mass domestic surveillance. The Pentagon demanded all AI firms make their models available for “all lawful purposes” and insisted it wasn’t the place of a private company to dictate how the military uses its tools. Anthropic said it would challenge the designation in court.

Hours later, OpenAI announced its own deal with the Pentagon to deploy models on the classified network. CEO Sam Altman said the agreement included the same two safety principles Anthropic had fought for — prohibitions on domestic mass surveillance and human responsibility for use of force — but framed them as aligned with existing law rather than contractual red lines. Earlier in the week, xAI’s Grok had become the second model approved for classified systems. The message to the industry: cooperate on the Pentagon’s terms, or get cut out.

The Reality: Operation Epic Fury

This isn’t just happening in server rooms. On February 28, Operation Epic Fury) — a joint U.S.-Israeli campaign in Iran — became the live-fire debut of this new doctrine.

CENTCOM confirmed that Task Force Scorpion Strike deployed LUCAS drones in combat for the first time — low-cost, autonomous, one-way attack drones that cost $35,000 each, designed for swarm coordination and built to operate beyond line of sight. The U.S. carried out nearly 900 strikes in the first 12 hours. AI systems — including Claude via Palantir — have been linked to intelligence analysis and kill-chain optimization in the operation. The full picture is still emerging, but the architecture is clear: sensors feeding algorithms feeding weapons at a pace no human staff can match.

We are not debating whether AI should be used in war anymore. That conversation is definitely over.

What we’re witnessing is the first conflict where AI functions as the commanding officer, the navigator, and the strategist. The “human-in-the-loop” is becoming a “human-on-the-loop” — watching as the machines we built outpace our ability to comprehend what they’re coordinating.

The Rubicon has been crossed, world’s first AI-powered war has begun.

I was uploading a ChatGPT-generated image to LinkedIn — something to accompany a Substack post I’d written about trust signals, of all things — when I noticed a small symbol appear in the corner of the image. Clicked it. What came up was more detailed than I expected: a full breakdown of where the image originated, what tools were used, and exactly how much AI was involved.

The platform had flagged my AI-generated image. On a post about trust. You can’t script that.

That’s Content Credentials. And if you’re a founder, a CMO, or a brand builder, pay attention.

So What Actually Is This?

LinkedIn has adopted the C2PA standard — short for the Coalition for Content Provenance and Authenticity — an industry-wide framework that embeds verifiable metadata directly into image and video files. Think of it like a digital chain of custody: every significant edit or generation step recorded and attached to the file. Google, Meta, Amazon, OpenAI, Adobe, Microsoft, Sony, and Publicis Groupe are all on the steering committee — and Publicis is notable as the first major ad holding company to commit, which should tell you something about where this is heading.

When content carrying these credentials is posted to LinkedIn, a small C2PA icon appears. Anyone can click it and see who created the content, what tools were used, and exactly how much AI was involved.

The information is just sitting there, available to anyone curious enough to look.

The End of Quiet AI

Many teams are using AI to generate visuals while saying very little about it. That isn’t inherently wrong — but Content Credentials means the tools themselves are starting to do the disclosing for you.

Most marketing teams are treating this as a technical hurdle. The smarter move is to treat it as an ethical one.

Where Does Your Content Actually Fall?

To help teams think through this, we developed the Higgins-Berger Scale — a practical framework built around one core idea: AI involvement isn’t binary, it’s a spectrum, and what moves the needle ethically isn’t how much AI you used. It’s how much meaningful human judgment was in the loop.

Context and intent matter too. A fully AI-generated image accompanying a personal blog post hits very differently than a brand using the same workflow to sell a product while projecting authenticity. Same tool, same output, very different ethical weight.

A fully AI-generated image reviewed, curated, and refined by a skilled creative sits in a very different ethical position than a prompt-to-publish workflow where no human meaningfully touched the output. Content Credentials doesn’t know the difference yet — but your audience is starting to ask the question.

When someone clicks that C2PA icon on your post, they’re forming a judgment about where your brand falls on that spectrum. If your brand voice claims authentic human connection but your metadata tells a different story, no amount of clever copy closes that gap.

What Should You Actually Do?

Audit your stack. Find out which tools already sign content with C2PA credentials and what they’re telling the world about your process.

Know your ethical boundaries. You don’t need a formal written policy, but your creatives should know where the lines are before they start a project — not after. Adopting a framework like the Higgins-Berger Scale internally gives your team a shared language for those conversations, so ethical decisions happen by design rather than by accident.

Define your synthetic ceiling. Decide campaign by campaign where you play. A high-stakes brand campaign might demand human-led work throughout. High-volume social might make a more AI-driven workflow the right call. The point is to choose intentionally, not by default.

The Bigger Picture

Audiences are actively learning to distrust what they see. Deepfakes and AI-generated disinformation have made people genuinely skeptical of polished content in a way they weren’t two years ago.

Content Credentials give people a tool to evaluate the what. The Higgins-Berger Scale gives your organization the why. Together, they represent a shift toward a creative economy where provenance actually means something.

That icon is already in the corner of your LinkedIn feed. The question is whether your brand will be ready when someone clicks it.

What separates brands that hold rankings from those that fluctuate with every algorithm update is trust. The signals that communicate trust — to search engines and to real users — are often the last thing on a CMO’s roadmap.

There’s also a newer objection worth addressing directly: with ChatGPT, Perplexity, and other AI tools handling more search behavior, does traditional SEO still matter? Yes. Google still processes the overwhelming majority of searches, and that gap remains wide. More importantly, most LLMs don’t operate in isolation — they pull from indexed web content, use Google to inform their results, and surface pages that rank well for credibility. Optimizing for Google’s trust signals is, in many cases, optimizing for AI search at the same time.

Search Has Become a Credibility Audit

Google’s job is to surface the most useful and trustworthy result, not the most optimized one. Its E-E-A-T framework — Experience, Expertise, Authoritativeness, Trustworthiness — makes this explicit, and recent algorithm updates have consistently rewarded sites with demonstrable real-world credibility.

Your search rankings are, in part, a credibility score. For anyone overseeing brand and growth strategy, trust signals are a brand asset question, not an SEO technicality.

The Four Layers of Trust Signals

Technical. HTTPS has been an explicit ranking factor since 2014 and is still neglected by more brands than you’d expect. Page load speed, mobile responsiveness, structured data, and clean site architecture belong in the same category. These are trust infrastructure.

Content. Who wrote this? What qualifies them? When was it last updated? Google’s Quality Rater Guidelines place significant weight on whether credentialed, verifiable people stand behind your content. Author bios, organizational transparency, and factual accuracy all matter. For YMYL categories — health, finance, legal, or anything with real purchasing consequences — the scrutiny is even higher.

On-page credibility. Contact information, physical addresses, About pages, privacy policies, and terms of service are signals that a real organization exists behind the domain. Their absence raises flags in both algorithmic and human evaluation.

Off-page reputation. Backlinks from authoritative publications and industry sources remain among the strongest trust signals in SEO. Brand mentions, third-party reviews on Google Business Profile and Trustpilot, and consistent NAP data across the web reinforce that your brand is established and respected. Endorsements from credible sources compound over time.

Why the Stakes Are Higher Now

The volume of published content has exploded, accelerated by AI generation tools. Google’s response has been to work harder to distinguish genuinely authoritative sources from content that merely resembles them.

The Helpful Content updates and E-E-A-T evolution are oriented toward the same outcome: surface content from sources with real expertise and a real stake in accuracy. Publishing more without trust infrastructure doesn’t just yield diminishing returns — it actively works against you.

The Practical Audit

Trust signals aren’t abstract. They’re auditable.

Content authorship. Every piece of content your brand publishes — blog posts, articles, social media, whitepapers, product descriptions — should be attributable to a real, verifiable person. Named authors with bios that establish relevant credentials, not generic “Staff Writer” bylines. Google’s Quality Raters are trained to look for this. With AI-generated content flooding the web, an author with a LinkedIn profile, a publication history, and recognized expertise in their field is a meaningful signal. An anonymous post is not.

Technical performance. Run Google’s Lighthouse tool on your key pages — it’s built into Chrome DevTools. Watch four scores: Performance, Accessibility, Best Practices, and SEO. Anything below 70 warrants attention. Core Web Vitals feed directly into those scores and are a confirmed ranking factor. Largest Contentful Paint, Interaction to Next Paint, and Cumulative Layout Shift measure how fast and stable your pages feel to real users. They’re part of how Google decides whether your site is worth sending traffic to.

Review platforms. Third-party validation carries weight that self-published content never will. Google Business Profile, Trustpilot, G2 for software and services, Yelp for local and hospitality, and industry-specific directories are all worth maintaining. Volume matters, recency matters, and response rate matters. A brand that engages with its reviews signals accountability. Two hundred reviews from 2019 with nothing since signals neglect.

Schema markup. Schema is structured data that tells search engines exactly what your content is — an article, a product, a review, an organization. At minimum: Organization schema to establish who you are, Article schema on editorial content to establish authorship and publication date, and BreadcrumbList schema to improve how your URLs appear in results. Schema doesn’t just support rankings — it affects how your listings look in search, which directly impacts click-through rates.

AI-generated content. Using AI in your content workflow is not inherently a trust problem. Publishing it without editorial oversight is (see more about the importance of Human-in-the-Loop when using AI in my post on AI Ethics). Google rewards content that demonstrates experience, expertise, and accuracy regardless of how it was produced, and penalizes content that is thin or unverified. If AI is part of your process, the question is whether a qualified human is editing, verifying, and standing behind what gets published. The byline still needs to mean something.

The Bottom Line

Trust in search is measurable and buildable. The brands that take it seriously don’t just rank better — they’re harder to displace.

As a renter, this is the kind of problem that’s supposed to be simple. I opened a maintenance ticket through my landlord’s online system. The company, FirstKey Homes, advertises a streamlined digital support experience for residents.

FirstKey Homes isn’t just a traditional landlord. It’s part of a large institutional housing portfolio owned by a private-equity firm. When housing becomes an investment asset at scale, systems and metrics inevitably replace the small, human feedback loops that used to exist between tenants and landlords.

To their credit, the response was quick. A technician arrived the next day.

He looked at the fridge, diagnosed the problem, and said he needed to order a part.

Reasonable enough.

Then he left. And the ticket was closed.

Not updated to “waiting for parts.” Not “in progress.”

Closed.

The system now showed the issue as resolved.

Except the refrigerator still didn’t work.

And because the ticket was closed, the system required opening a brand new request just to ask what happened to the first one.

Then something even stranger happened.

An automated email arrived asking me to rate the quality of the service I had just received.

The refrigerator was still broken, but the system was already measuring customer satisfaction.

So I rated the service very low — not because the technician was rude or the response time was slow, but because the problem hadn’t been solved yet.

Inside the company’s metrics dashboard, the interaction had already been counted as a success. Fast response time. Ticket closed.

From the customer’s perspective, the company had simply declared victory and moved on.

But the system had been optimized to close tickets, not solve problems, and that distinction matters more than most companies realize.

The Two Brands Every Company Has

Every company operates two brands.

The first is the marketing brand — the polished version. Website copy, ads, social posts, that reassuring tone promising *we care about our customers.*

The second is the operational brand.

This one lives in support queues, billing systems, ticketing platforms, and frontline employees trying to navigate policies they didn’t write.

Customers meet the marketing brand first.

But they believe the operational one.

Because marketing is what a company says.

Customer experience is what a company does.

When the Brand Breaks

The fracture usually happens quietly.

A customer has a real problem. They contact support. A process begins.

Then the system starts optimizing for metrics instead of outcomes: Tickets get closed early. Cases get transferred. Departments hand problems to each other like a hot potato.

Internally it looks efficient, but externally it feels like abandonment.

Anyone who has spent forty-five minutes on hold with a telecom provider — transferred twice, asked to repeat an account number three times, then disconnected — knows the feeling.

The company didn’t refuse to help.

The system just wasn’t built to.

The Corporate Blind Spot

This is where the real problem lives: Marketing teams are measured on leads, clicks, impressions, and conversions.

Operations teams are measured on tickets closed, call handle time, and queue reduction.

Notice what’s missing?

No one is measured on whether the customer’s problem was actually solved.

An organization can hit every internal target and still leave customers worse off than before they called.

Marketing reports record engagement.

Support reports record ticket closures, and yet the customer is still staring at a broken refrigerator.

The metrics say everything is working.

The customer says nothing is.

The Replacement Customer Trap

When companies damage customer trust, something predictable happens.

Customers leave.

Instead of fixing the underlying experience, many companies respond by increasing marketing spend.

More ads.

More campaigns.

More lead generation.

In effect, they start buying replacement customers for the ones they just burned.

Marketing Spend → New Customers → Poor Experience → Customer Loss → More Marketing Spend

The marketing department gets busier.

The brand gets weaker.

Why Good Marketing Makes This Worse

The better the marketing, the harder the backlash when the experience fails.

Because great marketing raises expectations.

If your brand promises white-glove service but delivers bureaucratic indifference, the gap doesn’t just disappoint: It feels like a lie.

And disappointed customers talk.

A quiet bad experience becomes a one-star review, a Reddit thread, or a group chat warning. The marketing team may never see it in their dashboard, but it shapes the brand far more than any campaign.

The Companies That Get It Right

The companies that avoid this trap treat marketing and operations as two halves of the same promise.

If marketing says *we care about our customers,* the operational systems have to make that real.

Support tickets stay open until the issue is resolved — not until a timer runs out.

Customers get proactive updates.

Frontline employees have the authority to fix problems without escalating through layers of bureaucracy.

And success is measured by whether the customer’s situation improved — not by how quickly the interaction ended.

This isn’t a problem unique to FirstKey Homes, it’s a system design failure that repeats across industries — telecom, insurance, property management, SaaS — anywhere the dashboard becomes more real to the company than the customer’s experience.

The experience has to match the story.

That’s not just a slogan, it’s the whole job.

The Real Brand

Companies often believe their brand is built through advertising. It isn’t.

Advertising creates the opening line of the relationship, but the real brand is built in the quiet moments that follow:

When something breaks.

When a customer asks for help.

When the system is tested.

Marketing creates the promise.

Operations decides whether it was a lie.

The Shift in Reality

For years, the marketing funnel has been our north star. But if you look at the current trajectory of user behavior, that star is flickering. We are seeing a significant trend toward zero-click environments. Between AI-generated overviews and the rise of algorithmic feeds, users are increasingly finding what they need without ever leaving their starting point.

As a founder or CMO, this doesn't mean the funnel is dead today, but it does mean the utility of the click is fragmenting. If our strategy is solely dependent on "traffic," we are optimizing for a metric that is becoming harder and more expensive to capture.

The Emerging Framework: The Visibility System

To stay ahead of this trend, we are moving toward a Visibility System—a layered approach that ensures your brand surfaces where decisions are actually being made. It’s about building a stack that functions even when the user never visits your site.

• The Brand Foundation (The Base): This is where authority, clarity, and trust reside. In a landscape where AI aggregates information, trust is the primary signal that determines whether you surface at all. Without a foundation of verifiable expertise and ethical transparency, you give machine-retrieval systems nothing to pull from.

• Platform Discovery (The Middle): This is about meeting attention where it lives. It requires native content—short-form, feeds, and community signals—that provide value within the platform, rather than just acting as bait for a landing page.

• AI & Search Answers (The Surface): This is the shift toward Answer Engine Optimization (AEO). It’s where questions are resolved and decisions happen without a click, because the layers below have already done the heavy lifting of establishing your brand as the definitive answer.

The Strategic Payoff

The goal of this architectural shift isn't just to keep up with tech; it’s about economic efficiency. We are seeing that building compounding authority at the foundation—focusing on that core of trust—significantly lowers Customer Acquisition Cost (CAC) over time. When you are the "trusted answer," you spend less money chasing the lead.

The trend is clear: the future of marketing isn't about pushing people down a narrow tube. It’s about building a solid base so that you surface as the answer, regardless of the platform.

Creative agencies have always been early adopters. Digital production, social platforms, automation — new tools show up, get absorbed, and eventually become invisible.

Generative AI is different. Not because it’s magic, but because it blurs lines that used to matter. Authorship. Responsibility. Trust. The question isn’t just what can be made anymore. It’s who’s accountable for it once humans and machines are working together.

The Higgins-Berger Scale exists to deal with that.

It’s not a moral verdict on AI, and it’s not a list of rules meant to slow anyone down. It’s a practical framework for evaluating how generative AI is actually being used in creative, informational, and commercial work — and for making those choices visible, defensible, and intentional.

Ethics here isn’t philosophy. It’s a design constraint.

Practice, not theory

Most AI ethics conversations live at the extremes. Either they’re abstract principles that collapse under real deadlines, or rigid rules that ignore how creative work actually happens.

This scale is meant to be used inside real workflows.

It looks at outcomes and processes, not press releases or stated intentions. The question it asks is simple:

Given how AI is being used in this specific project, what ethical risks are being introduced — and how are they being handled?

To answer that, the scale focuses on five areas where generative AI consistently changes the ethical landscape:

• Transparency

• Potential for harm

• Data usage and privacy

• Displacement impact

• Intent

Each category is scored based on observable behavior. Lower scores reflect stronger alignment. Higher scores signal the need for mitigation, redesign, or restraint.

Perfection isn’t required. Judgment is.

Transparency means accuracy, not disclosure theater

Transparency doesn’t mean listing every tool in the stack. It means not misleading people.

Claiming purely human authorship when AI played a meaningful role undermines trust — especially in contexts where audiences expect craftsmanship, originality, or accountability. Journalism. Education. Political messaging. Explicitly handcrafted work.

As AI becomes standard, many audiences already assume some machine assistance. Transparency matters most when omission would mislead. In those cases, clarity isn’t performative. It’s corrective.

Harm lives in context

AI doesn’t create harm in a vacuum. Harm comes from context, distribution, and interpretation.

The scale looks at whether AI output could reasonably mislead, reinforce bias, damage reputations, or create foreseeable downstream consequences once it’s released.

The goal isn’t zero risk. It’s examined risk. Lower scores reflect work where safeguards exist and human review actually matters. Higher scores reflect unexamined assumptions or indifference to how the work might land.

The presence of a machine doesn’t change the responsibility.

Data responsibility doesn’t disappear

Agencies may not control how large models are trained, but they’re still responsible for what they feed into them and how outputs are used.

Sensitive inputs. Questionable datasets. Ignored licensing. None of that becomes acceptable because it’s automated or convenient.

Unclear data provenance isn’t a loophole. It’s a warning sign.

Augmentation beats erasure

Displacement alone isn’t unethical. Creative work has always changed with new tools.

Risk increases when automation quietly replaces human judgment while preserving the appearance of human authorship.

The scale distinguishes between AI used to augment creative work and AI used to substitute for it. Projects that treat AI as a collaborator score very differently from those that remove people from the process without saying so.

Trust is built not just on what gets delivered, but on who is still responsible for it.

Intent ties it together

Across all five categories, intent is the connective tissue.

Commercial goals aren’t the problem. Risk escalates when speed, novelty, or engagement are prioritized over transparency, consent, or harm mitigation.

Most ethical failures don’t come from malice. They come from disengagement — from quietly removing human responsibility because the system makes it easy.

The point isn’t the score

Projects land in ethical zones ranging from exemplary to unacceptable. These aren’t judgments of creativity or innovation. They’re signals of risk and oversight.

A low score isn’t moral permission. A high score isn’t an accusation. The value of the scale is that it forces earlier conversations — before shortcuts become habits and habits become liabilities.

Ethical use of generative AI doesn’t require abstinence. It requires intention, awareness, and accountability.

The Higgins-Berger Scale isn’t meant to be static. It’s meant to evolve. Its purpose isn’t to produce a number — it’s to keep human responsibility visible wherever machines are invited into the creative process.

Review the latest version of the Higgins‑Berger Scale (Version 2.5)

Or test your process using the HBS Interactive Utility

Cracker Barrel tried to do what every brand is told to do in 2025: simplify, modernize, “clean it up.” You know the move. Flatten the mark. Lose the fussy stuff. Make it work at 32 pixels.

Reblanding.

And then their customers lit the porch on fire.

Cracker Barrel didn’t just catch flak — it reversed the logo change within about a week, publicly stating the new logo was going away and the “Old Timer” would remain.

This is the reblanding trend colliding with a brand whose entire value proposition is: nothing changes.

What “Reblanding” Actually Is

Reblanding isn’t really “rebranding.” It’s brand simplification as an operating system update:

• legible on apps and delivery platforms

• consistent across packaging, menus, uniforms, signage

• easier to animate, easier to reproduce, easier to manage

• fewer bespoke assets, fewer edge cases

It’s not inherently dumb. It’s often rational.

The problem is: rational isn’t the same as wise when your brand equity lives in a specific, beloved symbol.

What Cracker Barrel Changed (and why it triggered backlash)

Cracker Barrel’s short-lived logo redesign removed the illustrated “Old Timer” figure and leaned into a simplified wordmark look.

The company’s stated logic was practical: the simplified mark would be more visible and readable — including on highway signage — and more functional across modern surfaces.

That logic is fine.

But it ignores the central truth of Cracker Barrel’s brand:

The logo isn’t decoration. It’s a promise.

That figure and that “Old Country Store” vibe aren’t a style choice. They’re the psychological receipt that says:

“This is still the same place you remember.”

So the moment you remove it, customers don’t think “modern.”
They think “they’re about to change everything.”

Which is exactly what the interior remodel experiments signaled to people — and Cracker Barrel ended up suspending remodeling plans in the wake of the backlash.

Where They Really Went Wrong: Silence and Surprise

The biggest mistake wasn’t the logo.

It was how it was introduced.

Cracker Barrel didn’t frame the change.
They didn’t socialize it.
They didn’t bring loyal customers along for the ride.

The new logo was rolled out quietly, almost incidentally — mentioned deep in a press release about menu updates. No explanation. No narrative. No reassurance about what wasn’t changing.

So customers didn’t encounter a thoughtful evolution.
They encountered a screenshot.

In a vacuum.

And when you introduce change without context, people fill in the blanks themselves — usually with fear.

Had Cracker Barrel done even a minimal amount of audience engagement, this might have played out differently:

• A heads-up to loyalty members

• A “why now” blog post

• Side-by-side visuals showing continuity

• Explicit reassurance that the Old Country Store vibe wasn’t going anywhere

Instead, the brand went from familiar to unexplained overnight.

For a nostalgia brand, that’s fatal.

People didn’t reject the logo because it was simplified.
They rejected it because it arrived without consent.

Reblanding relies on trust. Cracker Barrel skipped the trust-building step.

And when the new logo appeared as an afterthought in a press release about menu updates, the brand ceded the narrative entirely. No framing. No context. Just a screenshot and a sinking feeling.

The Backlash Wasn’t Just Online Noise

This is what makes Cracker Barrel a real case study instead of a design-food fight:

• Reuters reported customer traffic fell roughly 8% after the rollout window referenced, and shares were down materially from the initial announcement point; they also reported the company abandoned planned remodels and reverted pilot stores back toward the classic look.

• The reversal itself was publicly confirmed, widely covered, and fast.

So yes: this wasn’t “Twitter was mad.” This was “your core audience interpreted the change as betrayal, and the company chose not to test how far that goes.”

Why This One Snapped Back So Fast

Cracker Barrel sells comfort-through-consistency. That’s not a metaphor. It’s the business model.

In a previous post, I talked about how Jaguar can afford to polarize because it’s trying to replace its customer story (EV luxury reinvention) and is willing to endure hate if it buys attention and a new audience.

Cracker Barrel can’t.

When your brand is ritual, you don’t get to play “surprise identity reset.” Reblanding works best when the brand’s meaning lives in product performance or innovation (Kia, VW). It backfires when the meaning lives in a symbol people are emotionally bonded to (Tropicana, Gap, Cracker Barrel).

Cracker Barrel is in the Tropicana club:

• remove the iconic asset

• customers can’t find “the same thing” anymore

• they revolt

• you put it back

The Real Lesson: Don’t Move the Trust Anchor

Reblanding is fine until it touches the one element that acts as the brand’s trust anchor.

For Cracker Barrel, the trust anchor is not just rocking chairs and antiques.
It’s the implication that the whole place is a time capsule you can reliably re-enter.

The simplified logo broke that spell.

So Cracker Barrel did the only rational thing for a nostalgia brand:
they reverted — quickly — and lived to modernize another day, more carefully.

Cracker Barrel didn’t lose a logo fight — it ran an experiment and discovered something useful:

You can modernize operations. You can modernize menus. You can even modernize interiors.

But if you modernize the symbol that tells people the brand hasn’t changed, you’re not rebranding.

You’re breaking the contract.

And nostalgia brands don’t survive contract breaches.

Left-leaning political content creators like David Pakman and Rick Wilson are already being impersonated by AI.

Not parody.

Not satire.

Impersonation.

Fake channels. Cloned voices. Synthetic faces. Real clips lightly altered to bypass detection. Feeds filled with “close enough” versions of people audiences already recognize — and that recommendation systems already trust.

And the key detail: they don’t lie (yet).

How this actually works

The popular mental model for deepfakes is wrong. People expect a single outrageous clip, a scramble to debunk it, and a clean resolution.

That’s not what’s happening.

What’s happening is much more subtle and sinister. Early AI Deepfake content mirrors the real creator closely. Same tone. Same framing. Sometimes it’s just recycled footage. Nothing alarming. Nothing extreme. Enough to blend in.

The goal isn’t persuasion. It’s building channel legitimacy.

Once a fake channel racks up watch time, subscribers, and “safe” engagement signals, the algorithm treats it as real. From there, the platform does the rest. Fake and authentic content start appearing side by side. Search results mix. Viewers hesitate.

The damage doesn’t require escalation

Here’s the part that matters most: the system causes harm even if the message never changes.

Once people know there are multiple convincing versions of the same person circulating, video loses authority. Real clips don’t land the same way. Denials sound self-serving. Corrections arrive late and travel poorly.

“I saw him say it” stops being decisive.

That isn’t classic misinformation. It’s erosion of confidence in the medium itself.

Why these creators get hit first

Presidents are too visible. Major networks have lawyers, verification pipelines, and platform contacts.

Mid-tier political commentators sit in a weaker position:

• familiar faces

• loyal audiences

• strong algorithmic reach

• little institutional protection

They function as trust hubs. Undermining them doesn’t require changing anyone’s mind. It just disrupts the flow.

And the burden falls entirely on the human. Reporting fakes. Posting disclaimers. Explaining what’s real. Losing time, momentum, and control — even after the impersonation is removed.

The impersonator moves on. The residue stays.

Why this scales

Once a voice and face model exist, content can be produced faster than it can be reviewed or challenged. Platforms reward output and engagement. Verification is manual and slow.

That imbalance isn’t a flaw. It’s the operating condition.

At scale, this stops being a content problem and becomes a credibility problem. When video no longer functions as evidence, accountability weakens by default.

What this is really about

This isn’t about convincing people of false claims, it’s about making people unsure what to trust.

That’s cheaper than persuasion.

And harder to reverse.

The collapse we witnessed over the last twelve months wasn’t a standard tech-cycle crash. It was a pruning of the vine. A set of foundational assumptions that fueled the 2023–2024 boom simply dissolved:

• The belief that opacity could scale indefinitely.

• The hope that reliability was an optional “version 2.0” feature.

• The delusion that users would tolerate permanent dependency on black-box systems.

The companies that didn’t make it to 2026 didn’t just run out of runway; they ran out of legitimacy. In the new AI economy, once legitimacy evaporates, no amount of compute or branding can bring it back.

The Ethical Graveyard (2025)

Transparency: When “Magic” Was Just Deception (Builder.ai)

The most clinical implosion of the year belonged to Builder.ai. On paper, it was the dream of the no-code era: automated software construction powered by an omniscient AI. In practice, audits and investigations revealed a routing layer that funneled tasks to a concealed offshore human workforce.

This crossed the line from prototyping into product misrepresentation. Whatever the original intent, what customers purchased as automation resolved into labor — priced, marketed, and valued as software. The market reaction wasn’t driven by moral outrage, but by brutal math. Enterprises realized they weren’t buying scalable automation; they were buying labor arbitrage disguised by a high-margin software multiple.

The Lesson: In AI, transparency isn’t a virtue; it’s a technical specification. If you sell automation and deliver humans-in-a-trench-coat, you aren’t a platform — you’re an accounting liability waiting to happen.

Reliability: The Danger of Beta-Testing Humanity (Humane AI Pin)

If 2024 was the year of the AI wearable, 2025 was the year reality intervened. The high-profile failure of the Humane AI Pin (and its contemporaries) wasn’t due to bad industrial design. It failed because it misunderstood the ethical load of the interface it sought to replace.

A smartphone is a tool you can put in your pocket. A wearable agent mediating your navigation, communication, and social context is life-adjacent infrastructure. Humane didn’t fail because it shipped early — it failed because it treated probabilistic output as deterministic authority. Thermal shutdowns during critical moments, hallucinated directions in unfamiliar cities, and inconsistent voice triggers weren’t just bugs — they were violations of an unwritten rule: do not increase the user’s cognitive risk.

The Lesson: You cannot outsource cognitive load to an unreliable narrator. Ethics enters the chat not as philosophy, but as uptime. If the system isn’t 99.99% reliable, “hands-free convenience” becomes anxiety, not liberation.

Data Sovereignty: The Un-Smartening of the Home (iRobot)

The quietest failure of 2025 wasn’t a bankruptcy — it was a trust withdrawal. iRobot, once the gold standard of the smart home, attempted to offset hardware margin pressure by monetizing spatial data.

Consumers didn’t stage a protest; they simply looked for the exit. Privacy stopped being an abstract concern for digital-rights activists and became a functional product requirement. Local-first stopped being a niche hobbyist term and became a mark of durability. Devices that required constant cloud mediation for basic operation began to feel fragile, risky, and — eventually — obsolete.

The Lesson: Privacy is no longer a policy layer; it’s a feature. When a device must export the geometry of your living room to function, users no longer see intelligence. They see exposure.

The Infrastructure Wake-Up Call

What made these failures stick was the backdrop of a shaking foundation. In 2025, we saw the infrastructure blink.

Outages across Google Ads and Cloudflare didn’t take the internet down, which is exactly why they were so unsettling. When Google Ads paused, thousands of businesses discovered they didn’t have a marketing strategy — they had a revenue pipe they didn’t own. When Cloudflare flickered, it revealed that the “distributed” internet is logically centralized around a few high-leverage choke points, where a single API hiccup can zero out a day’s revenue.

The Lesson: Resilience isn’t about uptime percentages; it’s about blast radius. This is where ethics and infrastructure converge. Hidden dependencies are trust failures waiting to happen.

The Agentic Squeeze (2026)

If 2025 cleared the deadwood, 2026 will liquidate the middlemen. We are entering the era of the Agentic Squeeze, where the distance between intent and execution collapses toward zero.

The Legal Squeeze: Perplexity and Attribution Debt

Perplexity AI sits on a growing pile of attribution debt. Its value proposition — collapsing diverse sources into a single, polished answer — conflicts directly with the economic reality of the publishers it depends on.

In 2026, we won’t see Perplexity disappear. We’ll see compression. Licensing requirements and legal guardrails will push margins toward utility pricing. The product survives, but the venture-scale upside evaporates. It becomes infrastructure — valuable, necessary, and boring.

The Feature Squeeze: Character.AI and the Generalists

Character.AI faces a different pressure. Its primary competitor isn’t another startup — it’s the evolution of general-purpose models. Once GPT-5 and Gemini deliver native long-term memory, persona persistence, and emotional tone control, companionship ceases to be a category. It becomes a setting.

This is the category-to-setting collapse: when a standalone product degrades into a checkbox inside a general system. Expect 2026 to be the year persona apps are quietly absorbed by the giants.

The Wrapper Purge

The most ruthless phase of 2026 will be the Wrapper Purge. AI abstraction is moving directly into the operating system — macOS, iOS, Windows, Android.

Any product that exists solely to do one thing an agent can invoke natively is in terminal danger:

• Standalone PDF assistants: now a native right-click.

• Basic AI copywriting tools: embedded in every text field.

• Single-workflow summarizers: handled by the notification layer.

Unless a company owns proprietary data or deep, specialized workflow context, it will be replaced by the Insert AI button.

The Insert AI button doesn’t compete.
It eliminates.

The Sovereignty Pivot

The winners of 2026 won’t be anti-cloud, but they will be anti-opacity. The market is shifting toward Hybrid Sovereignty:

• Identity and data stay local; compute scales to the cloud only when necessary.

• Verifiable agents with inspectable reasoning — no more “trust me, I’m an AI.”

• Graceful degradation: systems that still work locally when the servers go dark.

Final Take

2025 didn’t kill AI hype; it killed the illusion that abstraction equals safety. Ethical shortcuts surfaced as operational failures. Infrastructure hiccups surfaced as trust failures. Trust failures surfaced as valuation collapses.

As we move into 2026, the rule of the road is simple:

If your product exists only to stand between the user and execution, you are already obsolete.

Nothing exploded.
Everything simply re-priced.

The 2026 Outlook

• Reliability is the new ethics.

• Privacy is the new utility.

But six months later, the story has gotten bigger. This isn’t just about “better notes” anymore. It’s about the survival of the Communication Service Provider (CSP).

The Commodity Trap Right now, thousands of service providers are fighting a losing war on price. They are selling voice minutes and SIP trunks in a race to zero. If you are just moving audio from point A to point B, you are a utility. And utilities don’t get 10x multiples.

The winners in 2026 won’t be the ones with the best uptime (that’s table stakes). They will be the ones who turn that audio traffic into business intelligence.

From “Dial-Tone” to “Data-Flow” This is where the vCon standard changes the business model.

When a Service Provider adopts vCons, they stop being a “pipe” and start being a “platform.”

• Old Way: You sold a phone line to a Dentist’s office.

• New Way: You sell a “Patient Experience Engine” that automatically updates the scheduling software every time a patient calls to cancel.

You didn’t just sell a call; you sold a verticalized outcome.

The “Verticalization” Opportunity I’ve seen too many companies try to sell generic “AI” to everyone. It fails. The real money—the “2X your revenue” money—is in verticalization.

• For Legal: vCons + SCITT ensure attorney-client privilege and chain of custody.

• For Healthcare: vCons automate triage and insurance coding.

• For Retail: vCons drive sentiment analysis that predicts churn before it happens.

Automation is the New Product Your customers are tired of buying software seats. They want to buy results. They want AI Agents that don’t just “transcribe” but actively work.

• “Hey AI, listen to this vCon and book the appointment.”

• “Hey AI, analyze the last 50 vCons for pricing complaints.”

This level of automation requires clean, structured data. It requires vCons.

Final Thought for the C-Suite If you are a leader in the comms space, stop looking at your call volume as “traffic.” Look at it as an untapped oil field.

The insights vanish the second the call hangs up—unless you catch them. vCons are the net. If you aren’t catching that value, you aren’t just losing data. You’re losing revenue.

Meanwhile, your CRM is starving. Your analytics tools are guessing. Your AI summaries are stitched together from scraps.

That’s where vCons come in.

What is a vCon

A vCon, short for verifiable conversation, is a standardized digital record of a conversation. It captures the full context of what happened, who said what, when, and on which platform. It’s structured, portable, machine-readable, and ready for automation.

Not just a transcript. Not just a recording. It’s a shareable format that turns unstructured talk into actionable data.

You can feed a vCon directly into:

• Your CRM, to automatically update records

• Your AI tools, to generate summaries or next steps

• Your analytics platform, for engagement and sentiment tracking

• Your compliance stack, for audit-ready communications

What is SCITT

SCITT stands for Secure Communications Interoperability and Trust Transfer. It is the trust layer that proves the vCon hasn't been tampered with. It verifies the source, confirms authenticity, and ensures you know who actually said what.

Think of it as the cryptographic signature for conversations. It brings the same kind of integrity to spoken interactions that HTTPS brought to websites.

Why CMOs Should Care

Because your martech stack isn't really connected. It's duct-taped together. And in that gap, you're losing visibility, automation, and trust.

Here’s what vCons solve.

1. Sales calls that never make it into the CRM

2. Customer pain points lost in Slack threads

3. AI summaries based on bad or partial input

4. Compliance concerns from unstructured, unverifiable messages

5. A lack of true voice-of-customer insight

vCons fix the data plumbing. SCITT makes it trustworthy. Together, they let your tools work like a system instead of a set of separate apps.

What This Means for Marketing

You don’t just get transcripts. You get data you can use. Data your AI tools can understand. Data that legal and compliance teams don’t have to panic about.

This is the foundation for smarter campaigns, cleaner handoffs between teams, and truly personalized customer experiences driven by actual conversations, not guesswork.

Final Thought

If your strategy depends on AI, automation, and personalization, then you need structured inputs. And that starts with vCons.

Start thinking about your conversations as data. Because if you can't see them, analyze them, or trust them, you're already behind.

Now there’s a twist: a new approach called latent space reasoning is making AI reflect before it blurts out an answer. Instead of spewing the first response that comes to mind, these models pause to process ideas in the background. They refine, adjust, and only then produce a reply.

This might be our first real step toward Artificial General Intelligence (AGI) or even Artificial Superintelligence (ASI)—which is either incredibly exciting or absolutely terrifying.

Why Latent Space Reasoning Matters

Typical AI models prioritize speed. They start talking almost instantly, giving the impression of “thinking out loud.” But all they’re really doing is predicting tokens—no actual introspection. Latent space reasoning takes a different approach:

• It loops through possible solutions internally, refining the answer.

• It can adjust how much “thought” it applies, spending more time on complex problems.

• It genuinely reasons before speaking.

This approach nudges AI closer to real problem-solving. It’s not just repeating training data; it’s adapting and deciding.

The Road to AGI (And Why You Should Care)

We’re not at full-blown human-level intelligence yet, but we’re inching toward it. If AI truly thinks before it speaks—instead of just stalling—it crosses a threshold from synthetic mimicry to genuine reasoning. That’s the cornerstone of AGI: the ability to learn and adapt across domains.

After AGI comes ASI (Artificial Superintelligence), where machines could surpass human intelligence. That’s the scenario science fiction has been warning us about, and it’s no longer just theoretical.

What’s Next?

Here’s the big question: Are we ready for AI that actively thinks instead of just guessing? A system that not only answers our questions but might start questioning us? Share your thoughts below—while you still can.

So you dive in. You test all the new AI tools, sign up for all the platforms, pick the “Pro Plan” (because that’s how you roll).

But what usually happens?

You spend hundreds on AI-powered leads that go nowhere. Your AI-generated email campaigns get no engagement, failing to connect with real customers. Your website traffic grows, but conversions stay flat. You automate your social media posts, but engagement remains low. Your AI-driven content looks polished but doesn’t resonate with real customers.

What went wrong?

The Problem: Tactics Without Strategy

The issue isn’t the tools themselves. Many of them are powerful when used correctly. The problem is jumping straight into execution without a clear strategy.

Marketing isn’t just about doing things—it’s about doing the right things for your business, in the right way, at the right time. Without a strategy, your marketing is just a collection of random actions, hoping something sticks.

The Fix: A Strategy-First Approach

Before you spend another dollar on any marketing tool or another minute on a campaign, ask yourself these fundamental questions:

1. Who is your ideal customer?

Not just broad demographics—get specific. Who are they? What do they care about? Where do they spend time? What problem are they trying to solve?

2. What makes you different?

Why should a customer choose you over a competitor? If you don’t know, neither do they.

3. Where does your audience actually engage?

Are they on LinkedIn, or are they reading industry blogs? Are they searching for solutions on Google, or are they scrolling Instagram? If you don’t know, you’re wasting resources in the wrong places.

4. What is your customer’s journey?

Do they need to see your brand multiple times before deciding? Do they require education before purchasing? Understanding the buying cycle shapes your marketing efforts.

5. What does success look like?

More leads isn’t always the right answer. Do you need better leads? Higher conversion rates? Longer customer retention? Set the right goals before choosing the tactics to achieve them.

Execution with Purpose

Once you have clarity on who you’re targeting, how you stand out, and where and how to reach them, then—and only then—do you decide on the right mix of marketing tactics.

Maybe you don’t need another AI-generated email sequence. Maybe you need a killer LinkedIn outreach strategy. Maybe you don’t need AI-driven lead lists. Maybe you need better content to attract the right customers. Maybe your SEO or Google Ads keywords isn’t the problem.

Maybe your message isn’t compelling enough.

Stop Wasting Money on Random Marketing

If your marketing efforts feel scattered, ineffective, or frustrating, it’s time to stop throwing tactics at the wall and hoping something sticks. Step back, build a real strategy, and then execute with focus. The difference between struggling businesses and those that thrive isn’t just the tools they use—it’s the strategy behind how they use them.

Having the right strategy isn’t just about planning—it’s about execution. And that’s where the right expertise makes all the difference. Whether you build an internal team or bring in outside guidance, making the right decisions at the start ensures you’re not just marketing for the sake of it—but actually driving real results.

So before you get another “Creator extreme pro” subscription to the latest and greatest AI marketing tool, set up another automated campaign, or launch another initiative, ask yourself:

Do I have a strategy that works?

If the answer is no, and you think you might need some help, I’m offering free brand audits to all my Substack followers.

Free Brand Audit

Podcasting has emerged as one of the most effective content marketing tools for brands looking to increase visibility, establish thought leadership, and create lasting connections with their audience. However, while the benefits are clear, the process of launching and maintaining a high-quality podcast can be overwhelming.

Why Podcasting is a Smart Content Marketing Strategy in the Age of AI

AI-driven search engines and LLMs prioritize rich, contextual content that provides in-depth answers to user queries. As voice search and conversational AI become more prevalent, podcasts provide a unique advantage:

• AI Loves Spoken-Word Content – LLMs process and prioritize audio transcripts, making podcast content highly indexable for search engines.

• Long-Form Engagement Wins – Unlike short social media posts, podcasts hold audience attention for 20+ minutes, creating deeper connections with listeners.

• Audio SEO Boosts Discoverability – Transcripts, metadata, and summaries make podcasts an invaluable part of a brand’s SEO strategy.

• Repurposable Content – A single podcast episode can be transformed into blog posts, LinkedIn articles, YouTube shorts, and social media snippets, maximizing reach.

The Hidden Costs and Challenges of DIY Podcasting

Many companies attempt to launch a podcast in-house, only to quickly realize the complexity and cost involved. A well-produced podcast requires:

• Equipment & Setup – Costs range from $20 to $5,000+, depending on the level of quality desired. While a basic USB mic and computer can do the trick, professional sound quality often requires additional investments in XLR microphones, audio interfaces, and acoustic treatment.

• Production Expertise – Audio engineering, editing, and mastering are crucial for maintaining a polished, listenable show. Without experience, achieving professional quality can be difficult.

• Time Investment – Researching topics, booking guests, scripting, recording, editing, and publishing each episode requires significant hours. Even a small production schedule can take up 10-20 hours per episode.

• Content Consistency – Podcasts thrive on regular publishing schedules, which can be difficult to maintain with internal teams juggling multiple priorities.

• Marketing & Distribution – Simply publishing a podcast isn’t enough; building an audience requires targeted promotion, cross-platform distribution, and engagement strategies.

• Measuring ROI – Tracking performance metrics and tying them back to business objectives is more challenging than with traditional digital content.

The Advantages of Professional Podcast Production (Podcasting-as-a-Service)

For brands that want a high-quality podcast without the operational headaches, professional podcast production services offer a streamlined alternative. Podcasting-as-a-Service is a great way to introduce your brand to new audiences with little overhead.

• Basic Production Services ($1,000+/mo) – Covers technical aspects like editing and post-production but excludes elements like guest scheduling, scripting, and promotion.

• Standard Production Services ($2,000+/mo) – Includes post-production editing, social media graphics, and occasional re-recording but may not offer full strategic support.

• Premium Production Services ($3,000-$6,000+/mo) – Comprehensive solutions that include episode production, post-production, guest scheduling, scripting, video podcasting, and social media amplification.

The Future of Brand Storytelling

Podcasting isn’t just another content trend—it’s a fundamental shift in how brands engage with their audience. As AI and search technologies continue to evolve, long-form, spoken-word content will only become more valuable. The smartest brands recognize this and are positioning themselves as industry leaders through podcasting.

For those looking to expand their content strategy, investing in a well-produced podcast can be a powerful tool for brand visibility, authority, and engagement.

Enter DeepSeek. It’s not about outsmarting the competition; it’s about delivering comparable performance at a fraction of the cost. That alone threatens the economic assumptions propping up giants like OpenAI and Nvidia, which have thrived on the idea that cutting-edge AI demands astronomical compute.

The “Magnificent Seven” tech firms have their valuations pinned to AI dominance. If the market senses their lead is precarious, a trillion-dollar correction is on the table. And if hardware needs shrink or get commoditized, Nvidia especially could see a rapid deflation in value.

Meanwhile, Perplexity challenges Google Search. Open-source models like Mistral, DeepSeek, and Llama undercut proprietary LLMs. Plummeting infrastructure costs open doors for smaller players—and dry up big-tech moats.

This won’t necessarily crash overnight, but every sign of an AI bubble is flashing. The dot-com bust saw the Nasdaq drop 78%. If these AI powerhouses collapse under their own operating costs and overblown expectations, we might be headed for a real reckoning.

During my search, I considered the Hyundai Ioniq 5, Volkswagen ID.4, and Ford Mustang Mach-E. Despite these options, I ultimately went with the Model Y because of its features and a great deal on a low-mileage 2021 model. I didn’t opt for the Full Self-Driving (FSD) upgrade initially, which costs $99 a month or a one-time fee of $5,000. When Tesla offered a free trial, I decided to give it a shot.

After trying FSD, I’ve found it to be more of an advanced cruise control than a fully autonomous system. It handles routine tasks but struggles in low light or complex traffic conditions due to its vision-only approach. Compared to Waymo taxis, which allow for a fully disengaged experience, Tesla’s FSD still requires driver engagement, making the term “Full Self-Driving” somewhat misleading. While it’s a cool feature with great potential, the name might set unrealistic expectations for users.

Consider this: we don’t typically inform clients about the specific tools in our creative tech stack. If someone asks directly, "Do you use Adobe products?" the answer is an honest yes. But beyond that, there’s no moment in a presentation where we break down which part of the logo design was touched by Illustrator or how Premiere Pro shaped the latest cut of a campaign video. These tools, like many others, are embedded in the creative process — they’re extensions of our team.

But AI is different. Or is it? The very nature of AI tools often makes them feel like more than just a hammer or a brush; they analyze, suggest, create, and, in many ways, simulate creative decision-making. Adobe's generative AI features, AI-enhanced phones, and tools like ChatGPT might actively shape the product in ways that go beyond simply assisting. Yet, in many cases, these tools are always used under human supervision, with creatives steering the AI like they would any other piece of software.

So, should the use of AI tools warrant an extra layer of transparency? This question strikes at the heart of modern creative ethics. On one hand, clients entrust agencies to bring them expertise — the best possible solutions within budget, time, and creative constraints. Whether that expertise is expressed through a brushstroke or an AI-generated texture could be seen as immaterial, provided that the quality, originality, and intent remain true to the brand.

On the other hand, AI, with its ability to synthesize data, generate insights, and even compose narratives, represents more than just a tool. It blurs the boundaries between human and machine creativity. Some might argue that clients deserve to know if their campaign tagline was brainstormed by a human team or heavily assisted by an AI language model. Others might see disclosure as over-complicating a process that, at its core, remains human-driven.

For us as agencies, it might come down to a question of values. Transparency builds trust, but efficiency and the seamless integration of tools are what give our industry its edge. If clients were to ask if we use AI, the answer, much like the Adobe example, would be yes. But the real crux of the ethical debate might be whether our clients feel they are missing something crucial by not being told up front. Perhaps the ethical path lies in recognizing when AI significantly impacts the creative output and considering whether that impact changes the nature of what we deliver.

As AI tools become indistinguishable from everyday software, these ethical lines will only grow more complex. The key may lie in fostering open dialogue — being ready to discuss our processes openly without assuming a need to lay bare every step, while always ensuring that our use of technology enhances rather than diminishes the client’s experience of true human creativity.

Actionable Steps: Starting the Conversation

If you haven't already, it's time to start the conversation — with your leadership team, your creative team, and your clients. Whether you are an agency or work with one, discussing the ethical implications of AI tools is crucial for transparency and trust. Here are some actionable steps to consider:

1. Internal Discussions with Leadership: Begin by discussing the role of AI tools within your agency's creative process. What are the ethical standards your agency wants to uphold? How transparent do you want to be with clients?

2. Workshops with Creative Teams: Host workshops or meetings to align your creative teams on how AI tools are used and how to communicate their role. Make sure everyone understands when AI assistance should be disclosed to clients.

3. Client Communication Policies: Develop a policy on how to handle client inquiries about AI usage. Decide whether proactive disclosure is needed or if responses should be reactive based on client questions.

4. Training on Ethical AI Use: Provide training to your teams on the ethical considerations of AI. This could include understanding bias in AI tools, the boundaries of AI-generated content, and maintaining human oversight.

5. Open Dialogue with Clients: Be prepared to answer questions about your creative process. Encourage clients to ask about how tools, including AI, are used in their projects, and foster a culture of openness without overwhelming them with unnecessary details.

6. Assess Impact on Creative Output: Regularly evaluate how AI influences the creative output. If an AI tool significantly shapes the final product, consider whether this requires a different level of disclosure to the client.

Starting these conversations can ensure that your agency remains at the forefront of ethical creative practices, balancing innovation with the trust that clients place in your human creativity.