AI Is Fueling a Fake Content Flood — Even People You Know Can Be Caught

In the past week, at least two people close to me unknowingly reshared fake content on Facebook. These aren’t people who fall for chain emails or post conspiracy theories—they’re thoughtful, curious, and fairly tech-savvy. But that’s the reality now: it’s getting harder to tell what’s real online, even for people who usually know better.

The reason? AI is making it fast, cheap, and easy to generate fake stories, headlines, graphics, and even entire videos. And bots are spreading it all before we even realize it.

Take a moment to watch this clip from Rachel Maddow on MSNBC:
https://www.msnbc.com/rachel-maddow/watch/maddow-debunks-weird-fake-news-a-i-slop-stories-about-her-and-msnbc-infect-social-media-243601477992

Whether or not you’re a Maddow fan is beside the point. This segment shows how AI-generated nonsense—fake news stories, bot-written posts, and junk links—are showing up in our feeds, using her name and likeness to push made-up narratives. These aren’t even deepfakes. They’re low-effort, high-impact content designed to manipulate, outrage, and spread like wildfire.

Why People Fall for It

Here’s the tricky part: fake content doesn’t look fake anymore. Logos are copied, images are AI-generated, and the writing sounds just believable enough. AI tools are trained to mimic real news formats, which means many of the visual cues we used to rely on—like headlines, layout, or even tone—can’t be trusted the same way.

Add to that how fast we all scroll, how emotionally charged most social feeds are, and how much trust we put in content shared by people we know… and you’ve got a recipe for misinformation.

What You Can Do

I’m still a believer in AI’s potential, but I’m also realistic about how it’s being used right now. If you’re on social media, you need to assume you’ll be exposed to fake content—because you already have been.

Here are a few habits that help:

  • Pause before you share. If something triggers a strong reaction, that’s a good time to stop and investigate.
  • Check the source. Is it a reputable outlet? Does the link go where it says it does?
  • Reverse image search. Tools like Google Lens can help identify whether a photo has been altered or recycled.
  • Cross-check. If no one else is reporting it, there’s probably a reason.

Fake content is cheap. Your attention—and trust—are not. Stay sharp out there.

If this post helps even one person slow down before clicking “share,” it was worth writing. Let’s keep each other honest.

Rewiring AI: Putting Humans Back in the Loop

I’ll admit it—I used to love the promise of “one-click magic” in my observability dashboard. Who doesn’t want the AI to just fix that pager alert for you at 2 AM? But after reading Stop Building AI Tools Backwards by Hazel Weakly, I’ve come around to a stark realization: those “auto” buttons are exactly what’s hollowing out our edge as practitioners.

Here’s the thing—I’m a firm believer that we learn by doing, not by watching. Cognitive science calls it retrieval practice: you solidify knowledge only when you actively pull it from your own brain. Yet most AI assistants swoop in, do the work, and leave you wondering what just happened. It’s like teaching someone to bake by baking the cake for them. Fun for a minute, but no one actually masters the recipe.

Instead, imagine an AI that behaves like an “absent-minded instructor”—one who nudges you through each step of your incident playbook without ever taking the wheel. Using the author’s EDGE framework, it would:

  1. Explain by surfacing missing steps (“Have you considered rolling back that deploy?”), not just offering “click to fix” tooltips.
  2. Demonstrate with a 15-second animation of how to compare time ranges in your monitoring UI—turning your rough query into the exact syntax you need.
  3. Guide by asking Socratic questions (“What trace IDs have you checked so far?”), ensuring you articulate your plan instead of mindlessly pressing “Continue.”
  4. Enhance by watching your actions and suggesting incremental shortcuts (“I noticed you always filter by five-minutes-pre-alert—shall I pin that view next time?”).

Every interaction becomes a micro-lesson, reinforcing your mental models rather than eroding them.

I’ve started riffing on this idea in my own workflow. When I review pull requests, I ask our AI bot not to rewrite the code for me, but to quiz me: “What edge cases might this new function miss?” If I can’t answer, it highlights relevant docs or tests. Suddenly, I’m more prepared for production bugs—and I actually remember my review process.

What really blew me away in Stop Building AI Tools Backwards was the emphasis on cumulative culture—the fact that real innovation happens when teams iterate together, standing on each other’s shoulders. By capturing each developer’s on-the-job recalls and refinements, AI tools can become living archives of tribal knowledge, not just glorified search bars.

Of course, building these “human-first” experiences takes more thought than slapping an “Auto Investigate” button on your UI. But the payoff is huge: your team retains critical reasoning skills, shares best practices organically, and feeds high-quality data back into the system for ever-smarter suggestions.

So next time you’re tempted to automate away a few clicks, ask yourself: am I strengthening my team’s muscle memory—or erasing it? If you want to see how to do AI tooling the right way, check out Stop Building AI Tools Backwards and let’s start rewiring our interfaces for collaboration and growth.

Read the full article here: Stop Building AI Tools Backwards.

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!