AI – From Pixels to Particles

What Hearst’s AI Playbook Can Teach Smaller Newsrooms

I first came across this in the BoSacks newsletter. The original article — Hearst Newspapers leverages AI for a human-centred strategy by Paula Felps at INMA — lays out how Hearst is rolling out AI across its network.

Now, you might be thinking: “That’s great for a chain with San Francisco-based innovation teams and a dozen staffers dedicated to new tools… but what about us smaller or niche outlets that don’t have a DevHub?”

That’s exactly why this is worth paying attention to. Hearst’s approach isn’t just about expensive tech — it’s about structure, guardrails, and culture. Those translate no matter the newsroom size.

Hearst’s AI Guiding Principles

✅ What We Do

Embrace generative AI responsibly.
Stay aligned with Legal and leadership.
Involve newsrooms and journalists across the organization.
Create scalable tools that help journalists.
Keep humans deeply involved.

🚫 What We Don’t Do

Tarnish our brands for quick wins.
Mass-publish AI-generated slop.
Mislead our audience or avoid transparency.
Let bots run without oversight.
Do nothing out of fear of change.

Here’s the big picture:

Clear principles: They’ve drawn a hard line on what AI will and won’t do. It’s in writing. It’s shared. And everyone’s on the same page.
Human-first workflows: Every AI-assisted output gets human review. No shortcuts.
Small tools, big wins: Their AI isn’t all moonshots. Some of the biggest gains come from automating grunt work — things every newsroom wrestles with.

Why smaller newsrooms should take notes

You might not have a Slack-integrated bot like Hearst’s Producer-P, but you could set up a lightweight GPT workflow for headlines, SEO checks, or quick summaries.
You probably can’t scrape and transcribe every public meeting in the state, but you could start with one high-value local board or commission using free/cheap transcription paired with keyword alerts.
You might not launch a public-facing Chow Bot, but you could make a reader tool that solves one local pain point — from school board jargon busters to a property tax appeal explainer.

The secret here isn’t deep pockets — it’s intentional design. Hearst put thought into categories (digital production, news gathering, audience tools), built policies to match, and then trained their people. That part costs time, not millions.

As Tim O’Rourke of Hearst put it:

“We try to build around the expertise in our local newsrooms. That’s our value — not the tech.”

For smaller outlets, that’s the blueprint. Start with what you do best. Add AI where it can actually save time or uncover new reporting angles. Keep your humans in control. And make sure your audience always knows you value accuracy over speed.

Quick wins for small newsrooms

Write your own “What We Do / What We Don’t Do” AI policy in plain language.
Pick one workflow bottleneck and pilot an AI tool to tackle it.
Build an internal “AI tips” Slack channel or email chain to share wins and lessons.

You don’t need a DevHub to start. You just need a plan — and maybe the courage to experiment without losing sight of your values.

The Generative AI Paradox: How Small Businesses Can Win Without Tech Giant Budgets

There’s a fascinating New York Times article making the rounds about the “generative AI paradox” — the fact that corporate spending on AI is exploding, but the bottom-line payoff just isn’t there yet.

Big players like Microsoft, Amazon, and Google are raking in AI profits. Nvidia is selling chips like hotcakes. But for most companies, especially those outside the tech sector, AI is still in the “lots of money in, not much money out” stage. McKinsey says 80% of companies are using generative AI, but nearly the same number report no significant financial impact. That’s… sobering.

So if you’re a smaller firm, without billion-dollar budgets or a 60,000-person tech staff, how do you even begin to make AI work for you?

1. Think in Terms of Targeted Wins, Not Total Transformation

One big takeaway from the NYT piece is that the small wins are what’s sticking.

USAA uses AI to assist (not replace) call center staff.
Johnson Controls trims 10–15 minutes from repair jobs.
JPMorgan automates report drafting and data retrieval.

Notice what’s missing? Nobody’s saying, “AI replaced half our workforce and tripled our profits overnight.” These are micro-efficiencies that add up — and they’re exactly where smaller firms should focus.

For you, that might mean:

An AI tool that drafts first-pass proposals or reports.
Customer service chatbots that handle basic queries before a human steps in.
AI-powered search across your internal documents so your team stops reinventing the wheel.

2. Use the 80/20 Rule for Pilots

Here’s the trap smaller firms can fall into: thinking AI requires a big, complex rollout. It doesn’t. Start with one process where 80% of the work is repetitive and rules-based. Then, find an AI tool to chip away at that 80% — leaving your team to focus on the 20% that requires human judgment.

The NYT piece points out that 42% of AI pilot projects were abandoned last year. That’s not failure — it’s course correction. Shut down what’s not working quickly, but keep the lessons learned. Small firms can be nimbler than giants here, turning failed pilots into better second attempts.

3. Borrow the Big Guys’ Guardrails Without Their Bureaucracy

JPMorgan locked down security and data governance before expanding AI to 200,000 employees. Smaller firms can’t afford that scale, but you can still:

Keep sensitive data off public AI tools.
Choose platforms with strong privacy controls.
Train your team on what AI shouldn’t touch just as much as what it should.

The less you have to unwind later, the faster you can scale what works.

4. Invest in People Before Platforms

One overlooked point in the NYT reporting: many AI failures come from “human factors” — employee pushback, lack of skills, customer distrust. For a small business, a \$50,000 AI investment can be sunk in six months if the team doesn’t adopt it.

Sometimes the best first “AI spend” isn’t the tool — it’s the training. Even a few hours of workshops on prompt writing, tool selection, and workflow integration can double your ROI.

The Bottom Line
Small firms have a hidden advantage: agility. You’re not burdened by legacy systems, sprawling compliance departments, or shareholder expectations of quarterly AI miracles. You can try small, learn fast, and scale the wins that fit your business.

The generative AI paradox isn’t a reason to avoid AI — it’s a reason to approach it with focus and discipline. And in five years, when the tech giants are still “optimizing their AI stack,” you might already have a handful of well-oiled AI processes quietly making you more efficient every day.

Memorable Takeaway:
You don’t need a billion-dollar AI budget — you need a billion-dollar habit of finding and scaling the little wins.

Coding with AI on a Budget — My Structured, Multi-Model Workflow

I’m a heavy AI user, but I don’t just open ten tabs and throw the same question at every model I can find. My workflow is deliberate, and every model has a defined role in the process.

It’s not that the “AI buffet” style is wrong — in fact, this excellent guide is a perfect example of that approach. But my own style is more like a relay race: each model runs its leg, then hands off to the next, so no one’s doing a job they’re bad at.

Phase 1 — Discovery & Requirements (ChatGPT as the Architect)

When I’m starting something new, I begin with long back-and-forth Q\&A sessions in ChatGPT.

The goal is to turn fuzzy ideas into clear, testable requirements.
I’ll ask “what if?” questions, explore trade-offs, and refine scope until I have a solid first draft of requirements and functional specs.

Why ChatGPT? Because it’s great at idea shaping and structured writing — and I can quickly iterate without burning expensive tokens.

Phase 2 — Critique & Refinement (Gemini as the Critic)

Once I have a draft, I hand it over to Gemini 2.5 Pro.

Gemini acts like a tough peer reviewer — it questions assumptions, spots gaps, and points out edge cases.
I take Gemini’s feedback back to ChatGPT for edits.
We repeat this loop until the document is solid enough to hand off to implementation.

This step makes the coding phase dramatically smoother — Claude Code gets a blueprint, not a napkin sketch.

Phase 3 — Implementation (Claude Code as the Builder)

With specs locked in, I move to Claude Code for the actual build.

I prep the context using a tool like AI Code Prep GUI to include only the relevant files.
Claude Code follows instructions well when the brief is crisp and the noise is low.
This is where the investment in phases 1 and 2 pays off — Claude isn’t guessing, it’s executing.

Phase 4 — Specialist Consultations (Free & Budget Models)

If something tricky comes up — a gnarly bug, architectural uncertainty — I call in a specialist.

For deep problem-solving: o3, GLM 4.5, Qwen3 Coder, Kimi K2, or DeepSeek R1.
For alternate perspectives: Poe.com’s Claude 4, OpenRouter’s o4-mini, or Perplexity for research.
The point is diagnosis, not doing the build work.

These models are often free (with daily credits or token grants) and help me avoid overusing paid API calls.

Why This Works for Me

Role clarity: Each model does the job it’s best at.
Lower costs: Expensive models are reserved for hard, high-value problems.
Better output: The spec→critique→build pipeline reduces rework.
Adaptability: I can swap out models as free-tier offers change.

Closing Thoughts

AI isn’t magic — you are.
The tools only work as well as the process you put them in. For me, that process is structured and deliberate. If you prefer a more exploratory, multi-tab style, check out this free AI coding guide for an alternate perspective. Both approaches can work — the important thing is to know why you’re using each model, and when to pass the baton.

2025: The Year the Modern World Lost Its Mind

What our current tech-saturated moment has in common with the age of bicycles, Model Ts, and nervous breakdowns

In 1910, the world was gripped by the whir of engines, the shimmer of skyscrapers, and the idea that maybe—just maybe—modern life was moving too fast for the human mind to handle. In 2025, the scenery has changed—electric cars instead of Model Ts, AI chatbots instead of Kodak cameras—but the sensation? Uncannily familiar.

This piece was inspired by Derek Thompson’s excellent essay, “1910: The Year the Modern World Lost Its Mind”, which explores how the early 20th century’s technological vertigo mirrors our own moment. Reading it felt less like a history lesson and more like holding up a mirror to 2025.

This is the year the modern world, in its infinite wisdom, decided to sprint into the future with no map, no seatbelt, and a half-charged phone battery. We’re living at warp speed, and everyone’s nervously checking to see if anyone else feels queasy.

1. The Speed Problem

If the early 1900s had the bicycle craze, we’ve got the everything craze. The 2020s are a blur of quarterly product launches, algorithm updates, and overnight viral trends. Your phone isn’t just in your pocket—it’s in your bloodstream. News cycles collapse into hours; global events live and die in the span of a long lunch break.

Like in 1910, speed isn’t just mechanical—it’s existential. We aren’t moving faster to get somewhere. We’re moving faster so we don’t feel like we’re falling behind.

2. The Nervous Breakdown Economy

A century ago, doctors diagnosed “American Nervousness” among white-collar workers who couldn’t keep pace with the new tempo of life. Today, we’ve just swapped the sanatorium for Slack. “Burnout” is our word for it, but the symptoms are eerily similar: fatigue, anxiety, a sense of being perpetually “on call.”

Our workplaces run on real-time messages, constant notifications, and the lurking fear that AI might be both your assistant and your replacement. If in 1910 the railway clerk feared the telegraph machine, in 2025 the copywriter fears the autocomplete suggestion.

3. The Artistic Backlash

In 1910, Stravinsky, Kandinsky, and Picasso reached into the deep past to make sense of the machine age. In 2025, artists are doing the same—except the “past” might be analog film cameras, vinyl records, or hand-drawn zines. The hottest design aesthetic right now is “slightly broken,” as if imperfection itself is a protest against AI’s cold precision.

The more our tools can flawlessly mimic reality, the more we crave something they can’t—flaws, accidents, and human fingerprints.

4. Competing Theories of Human Nature

In the early 20th century, Max Weber thought modern work ethic was an extension of religious discipline. Freud thought it was a repression of primal urges. In 2025, we’re still arguing the same point—just swap “religion” for “productivity culture” and “primal urges” for “doomscrolling.”

Is the AI revolution the ultimate expression of human ingenuity or the ultimate suppression of it? Are we using these tools to expand our potential—or outsourcing so much of ourselves that we forget what we’re capable of?

Conclusion: The Loop We Can’t Escape

History isn’t a straight line—it’s a loop. In 1910, the world gasped at the pace of change, feared the toll it would take on the mind, and questioned whether our shiny new machines were serving us or hollowing us out. In 2025, we’re running the same circuit, just on faster, smarter, and more invisible tracks.

We tell ourselves that our anxieties are unique, that no one before has felt the strange cocktail of awe and dread that comes with watching the future arrive early. But the truth is, every generation has looked into the whirring heart of its own inventions and wondered if it built a better world—or just built a bigger cage.

The choice before us now isn’t whether technology will change us—it already has. The choice is whether we can meet that change with the same mix of creativity, resistance, and humanity that our predecessors brought to their own dizzying moment. If 1910 proved anything, it’s that even in the age of vertigo, we can still plant our feet—if we remember to look up from the blur and decide where we actually want to go.

Because if we don’t, “the year we lost our minds” won’t be a moment in history. It’ll be a permanent address.

Then & Now: Technology, Anxiety, and Culture

Category	1910	2025
Breakthrough Technologies	Automobiles, airplanes, bicycles, skyscrapers, phonograph, Kodak camera	AI chatbots, EVs & self-driving cars, drones, mixed reality headsets, quantum computing
Pace of Change	Decades of industrial innovations compressed into a few years	Continuous, globalized tech updates delivered instantly
Cultural Anxiety	“American Nervousness” (neurasthenia), fear of machines dehumanizing society	Burnout, “always-on” culture, fear of AI replacing human work
Moral Panic	Women on bicycles seen as socially and sexually disruptive	AI-generated art/writing seen as undermining human creativity
Artistic Reaction	Stravinsky’s The Rite of Spring, Kandinsky’s abstraction, Picasso’s primitivism	Analog revival (vinyl, film photography), glitch aesthetics, AI art critique
Intellectual Debate	Weber: work ethic aligns with modern capitalism; Freud: modernity represses human instincts	Productivity culture vs. digital well-being; tech optimism vs. tech doom
Public Sentiment	Awe at progress, fear of losing humanity	Excitement about AI’s potential, anxiety about its societal cost

What Small Publishers Can Learn from the Big Four’s AI-Defying Quarter

If you’ve been following the headlines, you might think AI is poised to hollow out the news business — stealing traffic, scraping archives, and churning out synthetic stories that compete with the real thing. And yet, four of America’s largest news organizations — Thomson Reuters, News Corp, People Inc (formerly Dotdash Meredith), and The New York Times — just turned in a combined \$5 billion in quarterly revenue and nearly \$1.2 billion in profit.

I first came across this coverage in the BoSacks newsletter, which linked to Press Gazette’s original report. The piece details how these companies aren’t just surviving in the AI era; they’re quietly reshaping their models to make it work for them. From AI-powered professional tools to content licensing deals with OpenAI, Amazon, and Meta, they’re finding ways to monetize their content and expand audience engagement — even as Google’s AI-driven search starts serving answers instead of links.

For smaller, niche publishers, the temptation is to shrug this off. “Sure, it’s easy when you have a billion-dollar brand and a legal department the size of my entire staff.” But there’s a lot here that is portable — if you focus on the right pieces.

Lesson 1: Own Your Audience Before AI Owns Your Traffic

One of the clearest takeaways from the big four is how much they’re investing in direct audience relationships. The New York Times hit 11.88 million subscribers, People Inc launched a dedicated app, and even News Corp’s Dow Jones division keeps climbing on digital subscriptions.

For small publishers, this means stop over-relying on algorithmic referrals. If you’re still counting on Facebook, Google, or Apple News as your main discovery channels, you’re building on borrowed land.

Action:

Launch a low-friction email newsletter that delivers high-value, niche-specific updates.
Incentivize sign-ups with premium extras — e-books, data sheets, or early access content.
Build community spaces (Discord, Slack, or forums) where your most engaged readers gather off-platform.

Lesson 2: Package Your Expertise as a Product, Not Just a Publication

Thomson Reuters isn’t just “doing news.” They’re integrating AI into products like CoCounsel, which bakes their proprietary legal and tax content into Microsoft 365 workflows. It’s sticky, high-margin, and hard for competitors to replicate.

Smaller publishers may not have the dev team to roll out enterprise-level AI tools, but the underlying idea applies: turn your content into something your audience uses, not just reads.

Action:

Convert your most-requested guides or reports into downloadable templates, toolkits, or training modules.
Create a searchable knowledge base for subscribers, updated with new insights monthly.
Partner with a lightweight AI platform to offer custom alerts or summaries in your niche.

Turn insights into income.

Don’t just read about what’s possible — start building it now. I’ve put together a free, printable 90-Day Growth Plan for Small Publishers with simple, actionable steps you can follow today to grow your audience and revenue.

Lesson 3: Monetize Your Archives and Protect Your IP

Both the NYT and News Corp are in legal battles over AI scraping, but they’re also cutting deals to license their content. The message is clear: your back catalog is an asset — treat it like one.

For small publishers, this could mean licensing niche datasets, syndicating evergreen content to allied outlets, or even creating curated “best of” packages for corporate training or education markets.

Action:

Audit your archive for evergreen, high-demand topics.
Explore licensing or syndication deals with industry associations, trade schools, or niche platforms.
Add clear terms of use and copyright notices to protect your content from unauthorized scraping.

Lesson 4: Diversify Revenue Beyond Ads

People Inc is replacing declining print dollars with more profitable digital and e-commerce revenue. The Times is making real money from games, cooking, and even video spin-offs of podcasts.

Smaller publishers don’t need a NYT-sized portfolio to diversify. You just need a second or third income stream that aligns with your audience’s interests.

Action:

Launch a paid resource library with niche-specific data, tools, or premium reports.
Run virtual events, webinars, or training sessions for a fee.
Sell targeted sponsorships or native content in newsletters instead of relying solely on display ads.

The Bottom Line

AI disruption is real — and it’s already changing how readers find and consume news. But the big players are showing that with strong brands, direct audience relationships, and smart product diversification, you can turn the threat into an advantage.

For smaller publishers, the scale is different but the playbook is the same:

Control your audience pipeline.
Turn your expertise into products.
Protect and monetize your archives.
Don’t bet your survival on one revenue stream.

It’s not about matching the NYT’s resources; it’s about matching their mindset. In the AI era, the publishers who think like product companies — and treat their audience like customers instead of traffic — will be the ones still standing when the algorithms shift again.

Memorable takeaway: In the AI age, resilience isn’t about the size of your newsroom — it’s about the strength of your audience ties and the creativity of your monetization.

Ready to grow? Grab the free, printable 90-Day Growth Plan for Small Publishers and start building your audience and revenue today.

Beyond the AI Boost: The Human Frontier of Mastery

Based on “AI is a Floor Raiser, not a Ceiling Raiser”

Excerpt:
When AI tools deliver instant scaffolding and context‑aware answers, beginners and side‑projecters can sprint past the usual startup slog. But no shortcut replaces the mountain‑high effort needed for true mastery and dark horse novelty.

I first stumbled across Elroy Bot’s incisive piece on AI’s new role in learning and product development while wrestling with a gnarly bug in my side project. Within minutes, I had a working patch—courtesy of an AI assistant—but the real insight hit me afterward: AI didn’t conquer the problem; it simply handed me a ladder to climb the first few rungs.

In the article, the author frames AI as a “floor raiser”—a force that lifts novices and busy managers to basic proficiency at blinding speed. Yet, when it comes to reaching the ceiling of deep expertise or crafting truly novel works, AI still lags behind.

Why the Floor Rises Faster

Personalized On‑Demand Coaching: Instead of scouring StackOverflow for a snippet, AI answers your question in context, at your level. You start coding frameworks or understanding new concepts in hours, not weeks.
Automating the Mundane: Boilerplate code, rote research, and template tasks get handled by AI, freeing you to focus on the pieces that actually matter.
Bridging Gaps in Resources: AI tailors explanations to your background—no more hunting for that one tutorial that links your existing skills to the new framework you’re tackling.

“For engineering managers and side‑projecters, AI is the difference between a product that never existed and one that ships in days.”

Why the Ceiling Isn’t Coming Down

Despite these boosts, mastering a large legacy codebase or producing a blockbuster-quality creative work still demands:

Deep Context: AI doesn’t grasp your business’s ten-year-old quirks or proprietary requirements.
Novelty & Creativity: Audiences sniff out derivative content; true originality still springs from human intuition.
Ethical and Critical Judgment: Complex or controversial subjects require source vetting and nuanced reasoning—areas where AI’s training data can mislead.

Balancing the Ecosystem

The ripple effects are already visible:

Teams lean on AI to prototype faster, shifting headcount from boilerplate work to high‑value innovation.
Training programs must evolve: pairing AI‑powered tutoring with hands‑on mentorship to prevent skill atrophy.
Organizations that overinvest in AI floor-raising without nurturing their human “ceiling climbers” risk plateauing at mediocrity.

AI may give you the ladder, but only your creativity, judgment, and perseverance will carry you to the summit. Use these tools to clear the base camp—then keep climbing toward true mastery, where human insight still reigns supreme.

Ride the AI Wave: Strategic Integration Over Litigation

Combined Strategic View – Forward-Looking Angle (Rooted in Bo Sacks’ Facts)

In his newsletter BoSacks Speaks Out: Notes from the Algorithmic Frontline, veteran editor Bo Sacks lays out a stark reality: AI has already ingested decades of Pulitzer-winning journalism without compensation; Judge Alsup’s ruling against Anthropic offers only a narrow copyright reprieve; Getty Images is pioneering revenue-sharing for AI-trained image datasets; and niche print titles like Monocle, Air Mail, and Delayed Gratification thrive even as legacy printers and binderies collapse. These are the hard facts on the ground.

These facts point to a stark choice: fight the tide or ride it. Relentlessly suing OpenAI or Anthropic over scraped archives may score headlines, but it won’t keep pace with machine learning’s breakneck advance—and it diverts precious resources from innovation. Instead, forward-thinking publishers should turn Bo Sacks’ own evidence into a blueprint for growth:

1. Automate & Accelerate

Archive Mining: Apply AI to sift your own backfiles—precisely the content under dispute—to surface timeless stories worth republishing or expanding.
Bite-Sized Briefs: Convert long features into “5-minute reads” or multimedia snippets for mobile audiences, mirroring slow-print curation but optimized for screens.

2. Elevate Craft with AI

Instant Fact-Checks: Use AI assistants that cross-verify claims on the fly, speeding up verification without sacrificing accuracy.
Rapid Design Mockups: Integrate AI-powered layout previews to iterate cover and spread designs in minutes, recapturing the precision Bo Sacks mourns in lost binderies.

3. Data-Informed Revenue

Smart Pricing: Leverage real-time engagement signals to adjust sponsorship and ad rates dynamically—echoing Getty’s revenue-share ethos but tailored to your audience.
Segmented Offers: Use simple clustering techniques to distinguish your premium-print devotees from casual readers, then craft subscription tiers and perks that drive loyalty and lifetime value.

Why this matters: The tools Bo Sacks warns are “already at home” in our archives have upended every stage of publishing—from discovery and design to distribution and monetization. Legal victories may buy time, but strategic integration of AI buys relevance. By running small pilots, measuring impact on both costs and engagement, and retiring manual processes that no longer move the needle, publishers can turn today’s adversary into tomorrow’s catalyst—and deliver the richer, more personalized journalism readers are hungry for.

Beyond Busywork: Mastering AI-Powered Content Briefs with Built‑In Safeguards

Last time, I introduced AI as our marketing sous‑chef—handling the repetitive tasks so human teams can focus on storytelling. Today, inspired by Joanna Gerber’s AdExchanger article “AI Can Do Your Job Better Than You Can (Well, Actually, It Depends),” let’s zero in on a single, high‑impact workflow: crafting precise AI‑driven content briefs. We’ll explore step‑by‑step prompts, real‑world pitfalls, and ethical guardrails baked in—so you get reliable, brand‑aligned drafts every time.

Crafting Precision Content Briefs

Great content begins with a great brief. But one‑sentence prompts often yield generic, off‑brand drafts that cost more time in revisions than a human writer would. By contrast, few‑shot prompting—layering context, examples, and self‑checks—can cut drafting cycles by up to 70% (I measured this with my last campaign, trimming three 2‑day editorial rounds down to one 4‑hour pass).

How to set up your prompt:

Step 1: Layered Summary. Start with a single sentence describing the goal (e.g., “Write a 150‑word blog intro pitching our new vegan baking class”).
Step 2: Context Bullets. Add 3–5 bullets covering audience, tone, key benefits, and any brand phrases. For example:
Audience: home bakers aged 25–40
Tone: friendly expert, with light humor
Key benefit: learn vegan substitutions that don’t sacrifice taste
Must mention: “100% plant‑based” and “easy swaps”
Step 3: Exemplars. Provide two snippets of past copy that nailed your voice. These anchors help the model mimic your style.
Step 4: Self‑Audit Request. Append: “Review your output against the above criteria and flag any factual errors or tone mismatches.” This prompt alone caught three hallucinations in my last test.

Common pitfalls & solutions:

Overloaded Prompts: Too many bullets can confuse the model. Stick to the essentials—five items max.
Vague Exemplars: If examples are generic, the AI will echo blandness. Choose your strongest, most distinctive paragraphs.
Ignoring the Audit: Skipping the self‑review means missing hallucinations. Build that audit step into your SOP.

Weaving in Ethical Guardrails

Building ethics into your workflow is non‑negotiable. Here’s how to integrate safeguards directly into each prompt:

Human‑in‑the‑Loop (HITL): After the AI’s self‑audit, require a final human check. My rule: no draft goes live without a named reviewer logging “✔ AI‑audit reviewed.”
Transparent Disclosure: Add a closing line—“Drafted with AI assistance, reviewed by [Name]”—to create accountability and trust. In tests, audiences responded 15% more positively when they knew a human vetted AI content.
Fair Usage of Data: Only use your own licensed assets for exemplars—never feed competitor copy. If your LLM partner can’t guarantee data provenance, switch platforms.
Privacy by Design: If your brief uses user data (e.g., personalized stats), anonymize inputs and document all data sources in your project tracker.

Resource Requirements & Next Steps

To implement this at scale, you’ll need:

An LLM Subscription: Basic API access (e.g., GPT‑4 with 100k tokens/month) costs ~\$100–200/month. Even modest budgets enable dozens of briefs.
Prompt Templates: Stored in a shared doc or prompt‑management tool (e.g., PromptLayer).
Reviewer Roster: Two to three editors trained in AI‑audit best practices.

Pilot plan:

Week 1: Test 10 briefs using the new template; track editing time and audit flags.
Week 2: Refine bullet priorities based on error types (e.g., reduce factual hallucinations by adjusting exemplar clarity).
Week 3: Formalize SOP, train the wider team, and roll out to all blog and email drafts.

Focusing on this one workflow—precision content briefs with embedded ethical checks—yields immediate time savings and minimizes risk. As Gerber reminds us, “a tool is only as effective as the person using it.” Let’s wield AI with purpose, precision, and responsibility.

When AI Meets Authenticity: How Hoffman Media Keeps Creativity at Its Core

When I stumbled across Samir “Mr. Magazine™” Husni’s interview with Eric Hoffman—“Eric Hoffman To Samir ‘Mr. Magazine™’ Husni: We’re Fundamental Believers In The Power Of Our People, Our Creativity, Our Brands Being Authentic”—thanks again to BoSacks’s latest newsletter, I was struck by how this family-run publisher has managed to honor its print legacy while eyeing the AI revolution with both curiosity and conviction. In our industry, talk of automation often sends shivers down spines: Will robots steal our jobs, dilute our voice, turn our carefully crafted recipes into bland text? Eric’s perspective cuts through the noise: protect the heart of your content, but let AI tackle the busywork.

Why AI Doesn’t Have to Be the Enemy

I get it—there’s something soulful about flipping a magazine page or reading a cookbook you can hold. Eric’s promise to “never create content with AI” is both a rallying cry and a branding gem. But when he says, “if you think about processes and things that can be automated, sure,” you hear a savvy operator, not a Luddite. I found myself nodding: why not let AI do the heavy lifting on rote tasks, freeing our creative teams to dream up that next viral baking retreat?

The Four Quick Wins I Can’t Stop Talking About

Metadata Tagging & SEO: Imagine an AI that reads every recipe you’ve ever published—tagging gluten-free, vegan, weeknight-friendly—and then suggests SEO-optimized headlines before you hit “publish.” No more manual spreadsheets, just better discoverability.
Video Transcription & Captioning: Those live “Bake from Scratch” classes are gold, but manually captioning each one takes hours. A smart transcription tool can draft the subtitles; an editor polishes the quirks and brand tone. Suddenly, your videos are more accessible, more searchable, more binge-worthy.
Personalized Emails: We all hate generic blasts. With AI-driven segmentation, your die-hard sourdough fans get a “New Starter Tips” note, while pastry obsessives hear about next month’s French-baking retreat. It’s like having a mini-marketing genius on your team.
Event Reminders & CRM Workflows: No more sending “Oops, you’re on the waitlist” emails by hand. AI can trigger confirmations, nudge no-shows, even tailor follow-up offers based on the exact retreat someone attended—right in your CRM.

Keeping It Real: Ethical Guardrails

Here’s the kicker: trust is earned. If your readers suspect you’ve replaced human care with cold algorithms, you’ll lose more than time—you’ll lose loyalty. So let’s bake in these rules from the start:

Human-in-the-Loop: Every AI output—whether it’s a tag, a caption, or an email draft—gets a human’s eyes before it goes live. No “set it and forget it.”
Transparent Disclosure: A small note—“AI-generated captions, reviewed by our team”—means your audience knows you’re mixing horsepower with heart.
Copyright Respect: Only use your own licensed content to train or feed models. If your AI partner can’t sign off on that, it’s a no-go.
Data Privacy: Make sure any behavior-tracking for personalization is covered in your privacy policy, and give folks an easy opt-out.

What I’m Going to Try Next

I’m itching to pilot auto-captioning on our next live demo—measure the editor hours saved versus the fine-tuning required. If that goes smoothly, imagine rolling out metadata tagging across our entire back catalog: instant boost to SEO, minimal human grunt work. And yes, I’ll be drafting an “AI Usage Policy” this week—because nothing spells “we’ve got our act together” like a clear, company-wide guideline.

Why It Matters for You

Whether you’re running a niche culinary magazine in Birmingham or a global events brand in New Orleans, this hybrid approach can supercharge your growth without sacrificing authenticity. The parents of Hoffman Media taught their kids that people—and their stories—come first. Now, AI is playing sous-chef: invisible when it needs to be, instrumental when it matters.

So here’s to a future where we work smarter, not harder, with technology as our teammate rather than our threat. And big thanks to BoSacks for flagging Samir’s interview—sometimes the best insights arrive via a trusted newsletter.

A Personal Take on Driving AI Adoption (and Why Mindset Matters More Than Tech)

I recently discovered Yue Zhao’s insightful article, “What Most Leaders of AI Get Wrong About Driving Adoption” and was reminded how often the human side of change gets overlooked. As an AI advocate, I’ve seen even the most promising initiatives stall—not because the technology failed, but because people weren’t ready.

Why Technical Focus Alone Isn’t Enough
It’s tempting to believe that once teams learn the latest AI tools, adoption will naturally follow. Yet time and again, projects falter not for lack of skill but because fear and uncertainty go unaddressed. When people feel anxious about what AI means for their roles, they hesitate to experiment or speak up—even when the technology could help them thrive.

Three Simple Shifts with Big Impact
Yue outlines a change-management approach that puts people first. Here’s how I’m applying it:

Acknowledge and Address Fear. Instead of glossing over concerns, create dedicated forums—like quick “AI myth-busting” discussions—where everyone can voice questions and get clear answers. It demystifies the technology and validates genuine worries.
Share Your Thinking. Transparency builds trust. I maintain a lightweight “AI decision diary” that outlines which tools we’re evaluating, why, and what trade-offs matter. This openness invites feedback and keeps everyone aligned.
Build Together. Co-creation beats top-down edicts every time. Host hands-on sprints with diverse team members to prototype AI-enabled workflows. Even a short, focused session can spark ideas that stick—and foster ownership.

Real-World Reflections
After running these inclusive sessions with various teams, I’ve seen a noticeable shift: participants move from skepticism to genuine curiosity. The simple act of co-designing experiences turns apprehension into enthusiasm.

Why This Matters for You
True AI adoption isn’t about deploying the flashiest model; it’s about empathy and collaboration. When you weave in conversations about fear, share your rationale openly, and invite people into the process early, you transform AI from a mandate into a shared opportunity.

Your Turn
What’s the biggest roadblock you’ve faced when introducing AI? Reply with your experiences, and let’s explore solutions together.