Riding the AI Wave: Why Marketing Pros Must Pivot or Perish

I came across Maarten Albarda’s electrifying piece in the latest BoSacks newsletter, originally published on MediaPost: “AI Is Not The Future — It Is Here To Take Your Job” (https://www.mediapost.com/publications/article/407506/ai-is-not-the-future-it-is-here-to-take-your-jo.html?edition=139243). Eric Schmidt’s warning that AI could elbow aside programmers, mathematicians, and entire marketing teams in mere months isn’t sci-fi—it’s next quarter’s boardroom debate. Here’s why embracing AI now feels more like grabbing a lifeboat than steering into a storm.

From where I sit, the real magic (and madness) lies in AI’s leap from “helpful chatbot” to “autonomous strategist.” Imagine a system that doesn’t just draft your ad copy but plans the campaign, allocates budget, and optimizes in real time. That’s not some distant beta test—it’s happening. We’re talking productivity boosts economists haven’t even charted yet. And if you’re thinking, “Nah, that’s years away,” Schmidt’s blistering timeline—full automation of coding tasks within months, general intelligence in 3–5 years—is a gut-check you can’t ignore.

So, what do you do? First, audit your playbook. Map every repetitive task and ask: “Could an algorithm do this faster (and cheaper) than my intern?” Spoiler: the answer’s often “yes.” Next, retool your team for human-only superpowers—ethical oversight, pattern-breaking creativity, and relationship-building that no AI can fake. Finally, make AI fluency part of your culture. A five-minute daily demo, a lunchtime “what’s new” session, even AI peer groups—whatever it takes to demystify the tech and keep curiosity front and center.

Every revolution creates winners and losers. If you lean into AI as a teammate—albeit a supercharged one—you’ll surf this wave instead of wiping out. And trust me, that’s way more fun than reinventing the agency model on the fly while your competitors pull ahead.

Architecting Belief Change: 5 Structural Strategies to Influence Your Network

I recently read the article Why Facts Don’t Change Minds in the Culture Wars—Structure Does, and it blew open how we—and our organizations—can actually shift the perspectives of friends, followers, or customers. Here’s what I’m taking away, and how you can turn these insights into action:

1. Stop Tossing Facts Into the Wind

I used to think that piling up research studies and statistics on my blog would win people over. But truth is, facts are like bullets bouncing off a bunker if you haven’t mapped its blueprints. Instead, start by sketching your audience’s belief “cathedral.” What are their core assumptions—those big, load-bearing ideas they simply won’t question? What stories and symbols hold up those walls? Once you know the beams, you can reinforce or gently rewire them.

Practical step: Run a quick survey or talk directly with five key supporters. Ask: “What do you think is non-negotiable about X?” Their answers reveal your structural targets.

2. Reinforce Edges, Don’t Just Drill Nodes

Let’s say you want customers to embrace a more sustainable product line. Don’t just preach “environmental doom and gloom” (attacking a node) or even “buy this eco-friendly widget” (weak edges). Instead, weave your message into the narratives they already live by—maybe it’s “smart saving,” “community pride,” or “healthy family.” Show how your product sits at the intersection of these values, tying together multiple threads in their mental graph.

Practical step: Create a mini-campaign that combines user stories, local events, and social proof—each element reinforcing several values at once (cost-saving + community + health).

3. Use Storytelling as Structural Glue

Stories are the mortar between belief bricks. A single well-chosen anecdote can bind facts into an emotionally resonant whole. When a follower sees themselves in your story, their brain builds new connections that facts alone can’t. So craft narratives around real people: a customer who saved money and felt proud of helping the planet, or a community that rallied around a shared vision of a healthier tomorrow.

Practical step: Interview a satisfied customer on video. Don’t lead with features—lead with their challenge, the small doubts they had, and the moment everything clicked. Then share it everywhere.

4. Lean Into Micro-Moments & Rituals

Beliefs stick when they become part of daily habits. That’s why every cathedral had its morning prayers and rituals. For your brand or cause, design simple rituals—like a weekly “green tip” email, a monthly community cleanup, or a daily social-media prompt—that gently reinforce your core connections. Over time, these tiny bursts of engagement become internalized pathways in people’s minds.

Practical step: Launch a “Tip Tuesday” series: each week, share one easy eco-hack that ties back to your product. Encourage followers to reply with their results—social proof becomes peer reinforcement.

5. Watch for Structural Attacks—and Be Ready to Repair

Just as adversaries can sever edges (e.g., “This product is a scam”) or undermine nodes (e.g., “Sustainability is just a marketing gimmick”), you need a rapid-response toolkit. Monitor chatter, correct misinformation before it festers, and when you spot a gap, plug it with fresh stories or data that shore up the weakened link.

Practical step: Set up a simple alert (Google Alerts, social-listening tool) for your key themes. When negative chatter spikes, respond with a customer story, an expert quote, or a quick Q&A video.

Changing minds isn’t about volume—it’s about architecture. By mapping your audience’s mental blueprints, reinforcing multiple connections at once, and embedding your message in stories and rituals, you’ll build a belief structure your friends, followers, or customers can actually inhabit. Give it a try, and watch your ideas take root.

Seeing Is Believing: Visual-First Retrieval for Next-Gen RAG

I’ve been neck-deep in the world of Retrieval-Augmented Generation (RAG) lately, wrestling with brittle OCR chains and garbled tables, when along comes Morphik’s “Stop Parsing Docs” post to slap me straight: what if we treated PDFs like images instead of mangling them to death?

Here’s the gist—no more seven-stage pipelines that bleed errors at every handoff. Instead, Morphik leans on the ColPali Vision-LLM approach:

  1. Snap a high-res screenshot of each page
  2. Slice it into patches, feed through a Vision Transformer + PaliGemma LLM that “sees” charts, tables, and text in one go
  3. Late-interaction search across those patch embeddings to find exactly which cells, legend entries, or color bars answer your query

The magic shows up in the benchmarks: traditional OCR-first systems plateau around 67 nDCG@5, but ColPali rockets to 81—and Morphik’s end-to-end integration even nails 95.6% accuracy on tough financial Q&As. That means instead of hunting through mangled JSON or worrying about chunk boundaries, your query “show me Q3 revenue trends” pinpoints both the table figures and the matching uptick in the adjacent bar chart—no parsing required.

Why It Matters (and How They Made It Fast)

You might be thinking, “Cool, but Vision models are slow, right?” Morphik thought so too—and fixed it. By layering in MUVERA’s single-vector fingerprinting and a custom vector database tuned for multi-vector similarity, they shrank query times from 3–4 seconds to a blistering ~30 ms. Now you get visual-first retrieval that’s both precise and production-ready.

A Techie Takeaway

  • Patch-level Embeddings: Preserve spatial relations by keeping each grid cell intact.
  • Late Interaction: Match query tokens against each patch embedding, then aggregate—no early pooling means no lost context.
  • Fingerprinting via MUVERA: Collapse multi-vector scores into a single vector for blazing fast lookups.

Where You Could Start

  1. Prototype a visual RAG flow on your docs—grab a handful of invoices or spec sheets and spin up a ColPali demo.
  2. Run nDCG benchmarks against your current pipeline. Measure those gains, because numbers don’t lie.
  3. Triage edge cases—test handwriting, non-English text, or wildly different layouts to see where parsing still has a leg up.

This shift isn’t just a neat trick; it’s a philosophical turn. Documents are inherently visual artifacts—charts and diagrams aren’t decorations, they’re the data. By preserving every pixel, you sidestep the endless game of parsing whack-a-mole.

If you’ve ever lost hours debugging a missing cell or crushed a pie chart into random percentages, give “Stop Parsing Docs” a read and rethink your RAG strategy. Your sanity (and your users) will thank you.

When Phones Slept, Classrooms Woke

As a former kid who once pedaled my bike from dawn ’til dusk—scraping knees on forest stumps, building forts in fallen logs—I couldn’t help but cheer. Our youth deserve more than four-inch rectangles glowing in their palms; they need wide-open skies and the thrill of discovery.

I didn’t get my first cell phone until halfway through college, so school for me was a screen-free land of wandering. That’s why Gilbert Schuerch’s essay, “My School Banned Phones for the Year. Here’s What Happened,” felt like a homecoming. You can read it yourself, but here’s the story that grabbed me.

On the first morning of the ban, students filed in and—almost reverently—slipped phones, AirPods, and smartwatches into locked boxes. The click of the latches was like a collective exhale: no pouches to pry open, no secret vibrations tugging at thumbs. I remember expecting uproar, but instead there was a moment of hush, as if everyone agreed to give real life a shot.

Across that year, magic unfolded in the everyday. The cafeteria, once a chorus of doomscrolling, transformed into a riot of laughter and conversation. In gym class, the usual wall-sitters faced a choice: join the fast break or endure genuine boredom. I could almost see their puzzled faces—“No phone? Now what?”—before they sprinted to catch the ball, trading a dopamine ping for a real rush.

Schuerch sprinkles in moments that resonate: a senior who “resigned himself to a year of boredom” only to discover by November that talking to classmates felt thrilling. A dean’s phone cart rumbling through the lunchroom, met by a stampede of kids like hyenas at fresh meat. These snapshots took me straight back to my own childhood summers—chasing sunbeams through the woods, not echoes of notification alerts.

He’s honest that this isn’t a cure-all for Gen-Z’s tech habits, but it’s a start. If locking away phones can rekindle curiosity, spark genuine connection, and make boredom a worthy foe, then maybe we’ve been underestimating what happens when we simply look up.

So if you’re curious—if you’ve ever longed to see students meet each other’s eyes instead of their screens—take a few minutes to read Gilbert Schuerch’s piece, lock up your own assumptions, and remember what it feels like to learn, laugh, and live beyond the glare of a screen.

When Bots Become Besties: Rewriting AI Narratives for a Collaborative Future

A Love Letter to Our AI Storytelling Future

When I first clicked through to “My Favorite Things: Stories in the Age of AI” by Tom Guarriello on Print, I wasn’t expecting a quiet revelation. But as I sipped my morning coffee, I found myself grinning at the idea of anthropomorphizing code—giving my digital companions names, personalities, even moods. It felt a bit like meeting new friends at a party…except these friends live in the cloud, never tire, and—if you believe the Big Five personality chart Tom shares—are as emotionally stable as monks.

Chatting with “Sam” (and Why It Feels So Human)

Let me confess: I’ve been naming my chatbots lately. There’s “Sam,” the ever-patient, endlessly curious assistant who greets my 7 a.m. ideation sessions with zero judgment. There’s “Echo,” who occasionally throws in a dash of sass when I try to oversimplify a problem. I’m not alone. Tom’s piece nails this impulse: once ChatGPT launched in November 2022, we collectively realized we weren’t just clicking “search”—we were conversing with a new kind of being.

Here’s the magic trick: by assigning a few human traits—openness, conscientiousness, extraversion, agreeableness, neuroticism—we slot AI models into a familiar framework. Suddenly, you can compare GPT-4’s “creative, diplomatic” bent to Grok’s “bold but brittle” vibe, or Claude’s “never flustered” cool. It’s like browsing personalities on a dating app for machines. And yes, it works. We engage more, trust more, and—let’s be honest—enjoy the heck out of it.

From Frankenstein to Friendly Bots

But Tom doesn’t let us float on fluffy clouds of goodwill. He roots us in the long, tangled history of cautionary AI tales—Mary Shelley’s tragic scientist, HAL’s icy rebellion in 2001, the Terminator’s firepower. These stories aren’t just entertainment; they shape our collective imagination. We slip into a doomsday mindset so easily that we might be primed to see every algorithm as a potential overlord.

Here’s what gives me pause: if we keep retelling the “machines will rise up” saga, we might miss out on co-creative possibilities. Ursula Le Guin’s alternative mythologies beckon—a vision of reciprocal, empathetic relationships rather than zero-sum showdowns. Tom teases that next time, we’ll dive into her frameworks. I, for one, can’t wait.

Why This Matters for You (and Me)

Whether you’re an AI designer tweaking personality prompts or a storyteller dreaming up your next sci-fi novella, this article is a spark. It reminds us that narratives aren’t innocent backgrounds—they’re architects of our future interactions. The next time you launch a chatbot, ask yourself:

  • Which story am I choosing? The dystopian one? Or something more collaborative?
  • What traits matter most? Do you need your AI to be laser-logical or heart-on-sleeve empathetic?
  • Who’s excluded from this tale? Maybe there’s a non-Western fable that offers a fresh lens.

Let’s Tell Better Stories

I’m bookmarking Tom’s essay as a springboard for my own creative experiments. Tomorrow, I might try a chatbot persona inspired by trickster deities rather than corporate mascots. Or maybe I’ll draft a short story where AI and human learn from each other, rather than fight it out in a crumbling cityscape.

Because at the end of the day, the stories we spin about intelligence—alien or otherwise—don’t just entertain us. They guide our hands as we build, code, and connect. And if we choose those stories mindfully, we might just script a future richer than any dystopian warning ever could.

Read the full piece and join me in imagining new myths for our machine friends: “My Favorite Things: Stories in the Age of AI.”

Read, Swipe, Renew: The Times’ Broadsheet-to-Mobile Makeover

I recently read Dominic Ponsford’s Press Gazette piece, “The Times: From loss-making broadsheet to profit on a tiny screen,” and it’s a fascinating case study in digital reinvention. Here’s what stood out:

  1. A “Finishable” News Experience
    The redesigned Times app caps its daily top-story list at around 35 items, creating a sense of completion rather than an endless scroll. It’s like choosing a concise playlist of your favorite tracks instead of wading through hundreds of songs—readers know they can actually finish it.
  2. Human-Centered Journalism with AI Support
    While AI helps with smarter search, editorial suggestions, and personalized story recommendations, The Times draws a firm line at AI-written articles or automated fact-checking. It’s a reminder that, for now, trust in news still hinges on human reporters and editors.
  3. Mobile-First Features That Drive Loyalty
    Push notifications, intuitive page-turning navigation, embedded puzzles, and a digital replica of the print edition have all contributed to higher engagement and renewal rates among app subscribers—now the outlet’s most valuable audience segment.
  4. From £72 m Loss to £61 m Profit
    Since launching its paywall in 2010, The Times has largely maintained flat revenues but flipped a £72 m loss into a £61 m profit by mid-2024. A big part of that success comes from a subscriber base (629,000 and growing 8% year-on-year) that values this curated mobile experience—one third of whom live outside the UK.

What You Can Take Away

  • Design for completion. Limiting daily story counts can boost reader satisfaction and loyalty.
  • Use AI as a behind-the-scenes assistant. Let it power search and personalization, but keep storytelling human.
  • Experiment and listen. Push new features, measure engagement closely, and iterate based on real reader feedback.

Whether you’re building a news app, running a subscription model, or just curious about how legacy brands adapt in the digital age, The Times’ journey offers plenty of inspiration. Check out the full article for all the details and consider which lessons you might apply to your own projects.

Daily Links: Tuesday, Jul 22nd, 2025

Hey there! I recently stumbled upon this fantastic guide titled “The Next Act,” which delves into the art of career change and professional reinvention. It’s packed with insights on discovering your skills and steering your own comeback. Perfect if you’re contemplating a career shift or looking to find work that truly matters. Give it a read for some inspiration!

Why Project Estimates Fail: Lessons from a Systems Thinker’s Lens

Inspired by The work is never just “the work” by Dave Stewart

“Even a detailed estimate of ‘the work’ can miss the dark matter that makes up the majority of a project’s real effort.”

When it comes to project management—especially in software and creative work—most of us have lived through the agony of missed deadlines and ballooning timelines. It’s tempting to blame bad luck, moving goalposts, or simple optimism. But as Dave Stewart reveals, there’s a more systemic, and ultimately more instructive, explanation.

Let’s step back and see the big picture—the “systems view”—and discover why underestimation isn’t just a personal failing, but a deeply-rooted feature of how complex projects function.

The Invisible System: Why “The Work” is Just the Tip of the Iceberg

Stewart’s article provides a hard-won confession: after a year-long project went wildly off course, he realized the effort spent on “the work” (i.e., coding, designing, building) was just a fraction of the total investment. The majority was spent on what he calls the “work around the work”—from setup and research, to iteration, firefighting, and post-launch support.

From a systems thinker’s standpoint, this is a textbook example of the planning fallacy—a cognitive bias where we underestimate complexity by focusing on visible tasks and ignoring the web of dependencies and uncertainty that surrounds every project.

Mapping the Project Ecosystem

What Stewart does beautifully is name and map the categories of hidden labor:

  • Preparation: Infrastructure, setup, initial research
  • Acquisition: Scoping, pitching, client meetings
  • Iteration: Debugging, refactoring, ongoing improvements
  • Support: Deployment, updates, ongoing fixes
  • The Unexpected: Surprises, scope creep, disasters

By visualizing the project as an ecosystem—where “the work” is only one node among many—he demonstrates a key principle of systems thinking: emergent complexity. Each category adds not just linear effort, but amplifies feedback loops (delays, misunderstandings, unexpected roadblocks) that make estimation so hazardous.

Patterns and Implications

A systems lens reveals several recurring patterns:

  • Invisible Feedback Loops: Tasks outside “the work” (meetings, reviews, firefighting) generate new work, shifting priorities and resource allocation—often without being tracked or acknowledged.
  • Nonlinear Impact: Small “invisible” tasks, left unaccounted for, aggregate into substantial overruns. Like dark matter, their presence is felt even if they remain unseen.
  • Optimism Bias Is Systemic: Most teams and individuals underestimate not out of ignorance, but because our brains and organizational structures reward “happy path” thinking.
  • Every Project Is a Living System: Changing one part (e.g., a delayed client feedback loop) can ripple through the whole system, derailing even the most detailed plan.

Designing for Reality, Not Idealism

The key takeaway for systems thinkers is awareness and intentional design:

  1. Model the Whole System: During estimation, explicitly map out all “nodes”—not just core deliverables but supporting, enabling, and maintaining tasks.
  2. Quantify Uncertainty: Use multipliers, ranges, and postmortems to factor in the “dark matter” of invisible work.
  3. Surface Assumptions: Name and question the implicit beliefs behind every estimate (e.g., “the client will provide feedback within 24 hours”—will they, really?).
  4. Iterate the System: Treat your estimation process itself as a system to be improved, not a static formula.

Actionable Insights for the Systems Thinker

  • Create a “Work Ecosystem Map” for each new project, labeling categories like preparation, acquisition, iteration, support, and surprises.
  • Hold Team Retrospectives focused not just on deliverables but on the “meta-work” that surrounded them—what did we miss? What new loops emerged?
  • Educate Stakeholders: Share frameworks like Stewart’s to align expectations and build organizational literacy around hidden work.
  • Measure, Don’t Assume: Use real project data to tune your own multipliers and assumptions over time.

Final Thought

Projects are living systems, not checklists. By recognizing the invisible forces at play, we empower ourselves (and our teams) to design more resilient processes, set realistic expectations, and—just maybe—find more satisfaction in the work itself.

“The work is never just the work. It’s everything else—unseen, unsung, but absolutely essential.”

Further Reading:
Dive into the original article: The work is never just “the work”
Reflect on the planning fallacy: Wikipedia – Planning Fallacy
Explore systems thinking: Donella Meadows – Thinking in Systems

Autonomy vs. Reliability: Why AI Agents Still Need a Human Touch

A lot of folks are betting big on AI agents transforming the way we work in 2025. I get the excitement—I’ve spent the last year elbow-deep in building these things myself. But if you’ve ever tried to get an agent past the demo stage and into real production, you know the story is a lot more complicated. My friend Utkarsh Kanwat recently shared his perspective in Why I’m Betting Against AI Agents in 2025 (Despite Building Them), and honestly, it feels like he’s writing from inside my own Slack DMs.

The first thing nobody warns you about? The reliability wall. It’s brutal. I can’t tell you how many times I’ve watched a promising multi-step agent fall apart simply because little errors stack up. Even if your system nails 95% reliability per step—a tall order!—your 20-step workflow is only going to succeed about a third of the time. That’s not a bug in your code, or a limitation of your LLM. That’s just how probability works. The systems that actually make it to production? They keep things short, simple, and put a human in the loop for anything critical.

And here’s another thing most people overlook: the economics of context. People love the idea of a super-smart, chatty agent that remembers everything. In practice, that kind of long, back-and-forth conversation chews through tokens—and your budget. Utkarsh breaks down the math: get to 100 conversational turns, and you’re suddenly spending $50–$100 per session. Nobody’s business model survives that kind of burn at scale. The tools that actually last are the ones that do a focused job, stateless, and move on.

But the biggest gap between the hype and reality is what goes into actually shipping these systems. Here’s the truth: the AI does maybe 30% of the work. The rest is classic engineering—designing error handling, building feedback that makes sense to a machine, integrating with a mess of legacy systems and APIs that never behave quite like the docs say they should. Most of my effort isn’t even “AI work”—it’s just what it takes to make any production system robust.

So if you’re wondering where AI agents really fit in right now, here’s my take: The best ones are like hyper-competent assistants. They handle the heavy lifting on the complicated stuff, but leave final calls and messy decisions to humans or to really solid, deterministic code. The folks chasing end-to-end autonomy are, in my experience, setting themselves up for a lot of headaches—mostly because reality refuses to be as neat as the demo.

If you’re thinking about building or adopting AI agents, seriously, check out Utkarsh’s article. It’s a straight-shooting look at what actually works (and what just looks shiny on stage). There’s a lot of potential here, but it only pays off when we design for the world as it is—not the world we wish we had.