I want to tell you something the traditional publishing industry would prefer stayed unsaid.

The system was never designed to let you in.

Not you, drafting chapters after midnight after a full day of work. Not you, writing the dark romance or the vampire fiction or the paranormal thriller that readers devour but that literary gatekeepers decided wasn’t serious enough to matter. Not you, the debut author without an MFA, without an agent who takes your calls, without the financial runway to spend years in a submission queue that moves at geological speed.

I write dark fantasy and paranormal romance. I publish on Wattpad, Inkitt, KDP, Gumroad. I know exactly what it costs — in time, in money, in rejection — to try to reach readers through the traditional path. And I know what it felt like when platforms started appearing that let me go around that path entirely.

The gatekeepers did not celebrate that. They condescended to it.

And now they’re doing the same thing with AI.

What the Panic Is Really About

When you strip away the language about authenticity and ethics and the sanctity of human creativity, what the traditional publishing industry is actually defending is its supply chain.

A developmental editor costs thousands of dollars. A literary agent requires a query letter written in a very specific format that nobody teaches you unless you already know someone inside the industry. A book deal requires an agent, and an agent requires a platform, and a platform requires — well, it requires that you’ve already made it somehow. The maze was designed by the people who charge admission to navigate it.

AI threatens that. Not literature. Not authentic human storytelling. The monopoly on access.

When a major publisher pulls a debut novel because an AI detection tool flagged it — and these tools are notoriously unreliable, flagging human writing as machine-generated with alarming frequency — they are not protecting creative integrity. They are protecting the infrastructure whose entire value depends on authors believing there is no other way.

There is another way. There always was. They just made sure most of us couldn’t find it.

We’ve Seen This Before

When indie authors started reaching readers directly through self-publishing platforms, the industry called it vanity publishing. Self-published authors were not real authors. Genre fiction — the romance, the paranormal, the romantasy that women write and read in enormous numbers — was dismissed as not serious enough to deserve serious treatment.

And then indie authors started outselling traditionally published ones. And then romantasy sat at the top of the bestseller lists while literary prize committees celebrated books that sold a fraction of the copies. And then the industry quietly started paying attention to what readers actually wanted instead of what the gatekeepers had decided they should want.

The internet gave indie authors a direct line to readers. No agent. No acquisitions meeting. No marketing budget contingent on whether your book fit neatly into last quarter’s sales projections. Just a writer and an audience finding each other.

AI is the next chapter of that same story.

What We’re Actually Doing With It

I want to be clear about something, because the conversation about AI in publishing tends to collapse everything into one accusation — that authors are generating books wholesale and passing them off as human work.

That happens. It’s worth criticizing. Flooding platforms with low-quality AI-generated content is a real problem and it damages discoverability for every author publishing genuinely original work.

But that is not what most indie authors are doing.

What most of us are doing is using AI to close the gap the traditional system deliberately left open.

We’re using it to proofread manuscripts we cannot afford to send to a professional copy editor. We’re using it to stress-test plot structure when we don’t have a developmental editor or a writing group with the genre expertise to give real feedback. We’re using it to draft back cover copy and think through marketing — all things that traditionally published authors get support for from their publishing teams, and that we have to either pay for out of pocket or figure out entirely alone.

The playing field has never been level. AI doesn’t tilt it. It levels one corner of it.

Calling that cheating is not an ethical position. It is gatekeeping with better PR.

The Authenticity Argument Has Always Been Selective

Traditional publishing has never been a system of pure, unmediated human creative expression.

It has always involved editors who reshape manuscripts substantially. Ghostwriters whose names never appear on covers. Co-authors who contribute unequally but share bylines. Developmental editors who restructure narratives so thoroughly that the published book barely resembles what the author originally submitted.

None of that disqualified the work. None of it prompted publishers to pull books or literary magazines to rescind prizes.

The line between assisted and authentic has always been blurry. The industry has always been comfortable with that blurriness — as long as the assistance was purchased through approved channels and the gatekeepers received their percentage.

AI is not a new form of assistance. It is a new form of access to assistance that was previously available only to people who could afford it. The outrage is not about the assistance. It is about who gets to decide where it comes from.

So Here Is What I Want to Say to You

Stop apologizing.

Be intentional about your work. Be honest with yourself about where your creativity lives and where the AI ends. Hold yourself to the standards you believe in as a writer — because those standards are yours, not theirs. Write the book that only you could write. Use every tool available to help you write it well.

And refuse — firmly, completely — to accept a framework designed by people whose financial interests depend on you believing you cannot succeed without them.

The writers who came before us used typewriters when scribes said it would ruin penmanship. They used word processors when traditionalists said it would make writing disposable. They used the internet when the industry said it would destroy publishing.

Publishing didn’t die. Gatekeeping adapted.

It will adapt again. The question is whether you’re going to wait for permission that was never going to come, or whether you’re going to write your book, reach your readers, and let the gatekeepers catch up on their own time.

The door is open. Not because the industry opened it.

Because the internet, and now AI, knocked it off its hinges.

Walk through.

KL Adams is a literary blogger and fiction writer specializing in dark fantasy, vampire fiction, and paranormal romance. Follow on WordPress, Inkitt, and Wattpad for reviews, reading lists, and stories that haunt you long after the last page.

Check out my Gumroad course.

Anthropic spent months telling the world that its most powerful model — Claude Mythos — was too dangerous to release.

They weren’t being coy. They meant it. Mythos, unveiled in April 2026, could identify security vulnerabilities in software with a level of precision that made cybersecurity experts nervous and government officials attentive. Anthropic has limited access to a small circle of vetted organizations through something called Project Glasswing. The public will not have access. No API. No wide deployment.

And then, on June 9th, they changed course.

They launched Claude Fable 5.

Six days later, the U.S. government ordered them to pull it back.

What unfolded in that window is one of the most revealing moments in the short, turbulent history of advanced AI — a collision between corporate ambition, national security, the limits of safety engineering, and a question that nobody has fully answered yet: What do you do when the thing you’ve built is genuinely too powerful to be everywhere, but too valuable to be nowhere?

What Fable 5 Actually Was

Let’s be precise about what launched on June 9th, because the framing matters.

Fable 5 was not a watered-down version of Mythos. Anthropic was explicit: the two models share the same underlying architecture, training, and published specifications. What separated them was a layer of safety classifiers — automated guardrails that would intercept and reroute high-risk queries in areas like cybersecurity and biology over to the older, less capable Opus 4.8 model.

So Fable 5 was Mythos with a filter on top.

The filter, by Anthropic’s own account, triggered in fewer than 5% of sessions. An external bug bounty program logged over 1,000 hours of adversarial testing without producing a universal jailbreak. By most measures, this was a serious, methodical safety effort.

The benchmarks were jaw-dropping. Fable 5 scored 80.3% on SWE-Bench Pro — a grueling software engineering evaluation — compared to 69.2% for Opus 4.8 and 58.6% for GPT-5.5. It became the first model ever to exceed 90% on Hex’s analytical benchmark. For long-horizon agentic work — the kind that runs for hours or days without human intervention — it was in a class of its own.

Anthropic priced it accordingly: $10 per million input tokens, $50 per million output tokens. Double Opus. They believed it was worth it.

And then they launched it on the API, AWS, Microsoft Foundry, and Google Cloud — simultaneously.

The Six-Day Window

The suspension came fast.

Anthropic received a letter from the U.S. government. According to Anthropic’s public statement, the letter did not provide specific details. What it communicated was this: the government believed it had realized a method to jailbreak Fable 5, a way around the classifiers. A path to the Mythos capability underneath — without the filter.

Anthropic’s response was immediate. Access for foreign nationals was suspended. That included foreign national employees of Anthropic itself.

This wasn’t a soft recall or a quiet patch. It was a hard stop, driven by national security concerns, that the company initiated on a model. The company had just announced this model to the world as ready for broad release.

It’s worth sitting with what the UK AI Security Institute had already found in early testing: meaningful progress toward a jailbreak. Not a complete bypass, but progress. Enough that the concern wasn’t hypothetical.

Anthropic acknowledged this. They also acknowledged that, even with the fallback to Opus 4.8, they had made a deliberate trade-off in their biology and chemistry handling — choosing overly broad safeguards over precise ones, accepting some capability loss in exchange for faster deployment.

The question the government was essentially raising was: Was the trade-off you made actually good enough?

The IPO Shadow

It would be naïve to discuss Fable 5 without discussing what else was happening at Anthropic in June 2026.

The company has been building toward a significant IPO, expected as early as this year. Wall Street had been paying close attention since April, when Mythos first surfaced and demonstrated capabilities that made investors salivate and regulators pace. The decision to release Fable 5 publicly, right now, was not purely a safety decision. It was also a market signal.

Anthropic had said publicly that deploying Mythos-class models at scale was its “eventual goal.” Fable 5 was the proof point that they could thread the needle — powerful enough to compete at the frontier, safe enough to ship broadly.

That story had real value. Commercially and reputationally.

The suspension complicates it. Not fatally — Anthropic has been transparent and swift in its response, which counts for something. But the narrative of “we solved the safety problem” took a hit. What replaced it is something more honest and more uncomfortable: We made our best effort, and it may not have been enough.

The Deeper Tension This Exposes

Here’s what I keep coming back to.

By its own mission statement, Anthropic is a safety-focused AI company. It believes it might be building one of the most transformative and potentially dangerous technologies in human history — and it presses forward anyway, on the theory that if powerful AI is coming regardless, it’s better to have safety-focused labs at the frontier than to cede that ground.

That logic has always had a tension in it. The Fable 5 situation makes the tension visible in a new way.

Because the safeguards Anthropic built for Fable 5 were real. They weren’t theatrical. The bug bounty was real. In reality, the fallback routing existed. The deliberate overbroad restrictions on biology and chemistry were real. And yet the government received intelligence — the nature of which hasn’t been disclosed — suggesting a bypass existed.

The classifiers triggered in fewer than 5% of sessions. That means in 95% of interactions, you were effectively talking to Mythos. And if someone found a way around the filter, they would have access to a model that Anthropic itself had previously said was too powerful to release to the public.

That’s not a failure of intention. It may be a fundamental limit of the approach.

You cannot build a guardrail powerful enough to contain a model that is smarter than the guardrail.

What This Means Going Forward

The regulatory environment is shifting fast. The White House AI executive order from December 2025 established a federal framework. Colorado’s AI governance law took effect this month. Multiple state laws are now active. The government is not watching from a distance anymore.

For businesses integrating AI at the frontier — and for anyone building on models like Fable 5 — this moment signals something important: capability and safety are not a one-time negotiation. They’re an ongoing one. Every model generation reopens the question. Every new capability requires a new safety answer. And governments are now willing to intervene in real time, not after the fact.

The era of “we’ll ship it and patch it” is over for Frontier AI. Or at least, it should be.

Anthropic will restore access. They’ve said so. When they do, it will almost certainly come with additional restrictions, refined classifiers, or new controls shaped by whatever the government communicated in that letter.

And then the next model will arrive. And the conversation will start again.

This is not a story about a company making a mistake. It’s a story about an industry reaching the edge of what its current safety frameworks can handle — and a government deciding it needed to say so out loud.

The model was powerful enough to launch. It may not have been safe enough to stay.

That’s the line we’re all trying to find. And nobody has found it yet.

Sources: Anthropic.com, CNBC, InfoQ, Capacity Global, WebProNews, API Docs (platform.claude.com)

Klynn is an AI business educator and commentator covering artificial intelligence trends, enterprise AI adoption, and the business implications of generative AI. Published daily on Medium and Substack, Klynn helps professionals and entrepreneurs understand how AI is transforming industries worldwide. Follow Klynn for daily AI business insights.

Two books dropped this month that have critics reaching for superlatives and readers clearing their schedules. They couldn’t be more different in scope, setting, and temperament — one sweeping and elemental, one intimate and quietly devastating. But both are doing what only the best literary fiction does: making you feel like you’ve lived inside another life.

Here’s the full picture on both.

Land by Maggie O’Farrell (Knopf, June 2, 2026)

What It Is

Land is a novel about separation and reunion, tragedy and recovery, colonization and rebellion — a story of buried treasure, overlapping lives, ancient woodland, persistent ghosts, and how, when it comes to both land and history, nothing ever goes away.

Set on a windswept peninsula stretching out into the Atlantic, Tomás and his reluctant son Liam are working for the great Ordnance Survey project to map the whole of Ireland in 1865, in a country not long since ravaged and emptied by the Great Hunger.

Why the Critics Are Losing Their Minds

This is O’Farrell’s follow-up to Hamnet — which means the bar was already set at nearly impossible heights. Publishers Weekly gave it a starred review, calling it “a stunning and gorgeous epic” and “a devastating yet tender portrait of Irish history.” Kirkus called it “evocative and impassioned… steeped in Irish history and folklore, alive with a sense of wonder.” Library Journal starred it. Booklist starred it. Four starred reviews from the major trade publications is not a common occurrence.

At its heart, the novel becomes less about ownership of land than about relationship to it — who remembers, who remains, who returns, and what histories refuse to stay buried.

One of the novel’s most compelling ideas is the tension between mapping and ownership. Tomás works creating maps for the British Ordnance Survey, yet the book repeatedly interrogates the colonial implications of mapmaking itself — maps as tools of conquest, instruments through which language, culture, and belonging are overwritten.

The Gothic Thread

For readers who follow this blog — and who know that we’re drawn to the dark, the haunted, and the elemental — Land has something rare: an ancient story with a hint of the supernatural that reads like stepping inside a dark, forgotten fairytale. O’Farrell’s storytelling is lyrical and evocative, bringing the past to life in vivid detail while dealing with heavy topics in a way that avoids feeling mired in misery — powerful but delicate, whispering its truths rather than shouting them.

There are persistent ghosts here. The land itself remembers. One reviewer described it as “ancient and brand-new all at once.”

If you loved the atmospheric dread threaded through Hamnet, Land takes that quality and expands it to geological scale.

The Verdict

One reviewer called it simply “the best novel I’ve read in 2026. This will win awards.” It’s hard to argue with that after four starred reviews and 14 raves out of the gate. This is a book for readers who want to be changed by what they read.

-----

Whistler by Ann Patchett (Harper, June 2, 2026)

### What It Is

When Daphne Fuller and her husband Jonathan visit the Metropolitan Museum of Art, they notice an older, white-haired gentleman following them through the galleries. The man turns out to be Eddie Triplett — her former stepfather, who had been married to her mother for just over a year when Daphne was nine. Now fifty-three, Daphne hasn’t seen Eddie in decades, not since the fateful event that changed the direction of both their lives.

They reunite by chance, spend the afternoon together, and when they hug goodbye Daphne is overcome by a memory she has spent years suppressing — the night of a harrowing accident on a snowy road that precipitated Eddie’s divorce from her mother, an accident Daphne has blamed herself for ever since.

The Patchett Formula — And Why It Works Every Time

Patchett’s tenth novel may be her most essayistic and, in that sense, her most confident — a reflection on fatherhood largely unruffled by the exigencies of plot. If you come to Whistler expecting the propulsive tension of Bel Canto, you’ll need to adjust. This is a book that earns its power through accumulation — small truths, sharp observations, the precise weight of a remembered smell.

The story mainly takes place in the present as Daphne and Eddie are reunited, with sections dedicated to the past and the fateful car accident. During the accident, the reader learns the meaning behind the title — a defining thread that runs through the entire narrative. Belonging, connection, and forgiveness are the central themes. There are no villains in this story, only people trying to do the best they can.

That our childhoods never leave us, that the very people we should know best are often impenetrable mysteries, that repressed anguish will find its way out — these are Patchett’s recurring themes, and she handles them here with beautiful aplomb.

What Makes It Linger

Patchett writes the reunion of Daphne and Eddie with such grace that it feels both miraculous and inevitable — a novel about the choices we make and the ones made for us, about how small events can redirect entire lives, and how love, unexpected or unconventional, can echo for years.

This is one of the best books of 2026 and will have a lasting legacy. High praise — and based on the sheer volume of readers describing it as a book that made them reflect on their own lives and the people who shaped them, it’s not hyperbole.

The Verdict

Whistler is the quieter of this month’s two literary heavyweights, but quiet doesn’t mean small. This is a novel about the extraordinary weight of ordinary love. Read it if you want a book that stays with you in the hours after you’ve set it down.

Two Books, One Month, No Wrong Choice

Land and Whistler both published June 2nd and both immediately entered the conversation for the best literary fiction of 2026. They represent two of the form’s great traditions — the sweeping historical epic and the intimate character study — executed at the highest level by two writers at the peak of their powers.

If you’re building your summer reading list, start here.

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*KL Adams is a literary blogger and fiction writer specializing in dark fantasy, vampire fiction, and paranormal romance. Follow on WordPress, [Inkitt](https://www.inkitt.com/KLAdams), and [Wattpad](https://www.wattpad.com/KLAdams53) for reviews, reading lists, and stories that haunt you long after the last page.*

[Check out my Gumroad course.](https://kladams53.gumroad.com/l/hisznv)

Let’s talk about what the data is actually saying right now.

Dark romance is projected to move fully into the mainstream in 2026, transitioning from a specialized indie niche to a dominant force in the publishing industry, driven by high reader demand for intense, emotional, and morally gray storytelling. Dark Romance Crates

Not emerging. Not trending. Dominant.

For those of us who have been reading vampire fiction and dark fantasy romance since before it was an Amazon category, this moment feels equal parts vindication and déjà vu. We knew. The readers always knew. The industry is just now catching up with the receipts.

The Maximalist Reader Has Arrived

Here’s what the market data is telling us about why this is happening now.

The genre’s popularity is fueled by a “maximalist” trend where readers seek extreme themes — the darkest, sexiest, and most shocking stories — often as a form of escapism from curated social media lives. Dark Romance Crates

Think about that for a moment. The more performative and filtered public life becomes, the more readers sprint toward fiction that refuses to be either. Dark romance doesn’t curate. It doesn’t soften. It hands you a morally grey anti-hero, a power imbalance that makes your pulse spike, and a love story that costs both characters something real — and it trusts you to handle it.

Dark romance offers a cathartic way to safely address power dynamics, while providing more dramatic character arcs. Villains and anti-heroes have the most room for growth as individuals, as do the relationships so integral to the dark romance storyline. Dark Romance Crates

That’s not escapism in the dismissive sense. That’s literature doing exactly what literature is supposed to do.

The Genre-Bending Wave

The market signal that matters most right now for indie authors writing at the intersection of genres:

The defining trend of 2026 is genre-bending — a massive surge in Gothic-slasher romances, dark reimaginings of classics, and experimental structures. Mid-list authors taking risks with taboo subjects and experimental structures are breaking through alongside the dominants like H.D. Carlton and Rina Kent. Darling Reader

This is the opening. Dark fantasy romance. Vampire fiction with literary ambition. Paranormal with psychological depth. The readers who want those exact books have never been more organized, more vocal, or more reachable — and BookTok has become their megaphone.

The explosion of romantasy was supercharged by BookTok’s devotion to Sarah J. Maas and ACOTAR, and accelerated dramatically with Fourth Wing — bringing readers into fantasy who had never read it before, following the heat and emotional intensity straight into a genre they’d previously ignored. Author Ever After

That pipeline is now wide open. And it runs directly through the kind of dark, character-driven fiction that has always lived on platforms like Wattpad and Inkitt — long before the mainstream noticed.

Vampire Fiction’s Quiet Renaissance

Of all the subgenres moving right now, this one feels most personal to track.

Carissa Broadbent returns with high-stakes vampire politics and heart-wrenching tension in The Lion and the Deathless Dark — the fifth book in the Crowns of Nyaxia series, where blood, power, and love collide. Releasing August 4, 2026. Eviemitchell

H.D. Carlton enters the Hollow Graves Duet with dark stalker-vibes romantasy, morally grey heroes, obsessive love, and a story built to keep you awake. Eviemitchell

A gothic fantasy following an obsession with an immortal serial killer — a dark sapphic romantasy where a vampire hunter’s daughter becomes entangled with a beautiful immortal in a romance that defies every boundary. Eviemitchell

The pattern across all of it: immortality, obsession, moral complexity, and love that extracts a real price. These are not comfort reads. They are not meant to be. And the numbers — pre-orders, BookTok views, KU page reads — reflect exactly how much readers want precisely that.

What the Platforms Are Telling Us

Dark romance is now one of the fastest-growing categories in indie romance publishing, with significant visibility on BookTok and prominent placement in Amazon’s romance categories. Author Ever After

Kobo has integrated with StoryGraph — the Goodreads alternative that skews toward exactly the kind of engaged, genre-committed reader who builds series loyalties and leaves reviews that actually move the needle. Author Help

Ream has emerged as the primary platform for indie romance authors to publish serialized fiction directly to subscribers, following the shutdown of Kindle Vella in early 2025, with a model that combines tiered subscriptions and reader-facing discovery. The appetite for serialized dark fiction didn’t disappear. It just found a better home. Author Ever After

Authors earning $10k+/month average 18,000+ subscribers on email lists, while those without a list earn about 20x less. Email lists power launches, promos, direct sales, and long-term reader relationships — without relying on algorithms. MIBLART

The data on that last point is worth sitting with. The authors winning in this market aren’t just writing the right books. They’re building direct relationships with readers who are waiting for every release.

The Darkness Has Always Been Here

Ink Plots exists because data and darkness aren’t opposites. The numbers tell us what readers want. The stories tell us why they need it.

Right now, both are pointing in the same direction.

Dark romance. Vampire fiction. Gothic fantasy. Morally grey heroes and heroines who refuse to apologize for what they are.

The market has reached the point where independent authors in this space have been standing for years.

If you’ve been writing in the shadows — keep going. The spotlight finally found us.

KL Adams is a literary blogger and fiction writer specializing in dark fantasy, vampire fiction, and paranormal romance. Follow on WordPress, Inkitt, and Wattpad for reviews, reading lists, and stories that haunt you long after the last page.

Check out my Gumroad course.

1. Maggie O'Farrell and Ann Patchett Dominate This Week's Best Reviewed Books

Two of literary fiction's biggest names are leading the conversation this week. Maggie O'Farrell's *Land* (Knopf) earned 14 rave reviews, with critics calling it "a soaring, visionary narrative that connects the known world to the misty realms of Celtic legend." Ann Patchett's *Whistler* (Harper) followed close behind with 11 raves and 5 positives. Both titles have been among the most anticipated releases of 2026 — O'Farrell following *Hamnet* with a historical novel set in 19th-century Ireland, and Patchett delivering a quiet, character-driven story about unexpected connections. If you haven't pre-ordered either, now's the time.

2. HarperCollins Completes Its Biggest Restructuring in Years

The second-largest trade publisher in the U.S. just reshuffled its entire house. CEO Liate Stehlik announced that HarperCollins U.S. trade will now operate through seven distinct publishing groups: Avon, Dey Street, Harper, HarperCollins Children's Books, HarperOne, Mariner Books, and Morrow — with the stated goal of giving each imprint a stronger, more distinct identity. The reorganization is believed to be the final major structural change under Stehlik's tenure. No layoffs were announced. For authors and agents, this matters: knowing which group aligns with your work is now more important than ever when pitching.

3. The Literary Arts Fund Awards $7.7 Million — But It's Not Enough

In the wake of federal arts funding cuts, private philanthropy is stepping in — but the gap is enormous. The Literary Arts Fund, backed by a coalition led by the Mellon Foundation, awarded $7.7 million to 40 nonprofit literary organizations across 19 states in its inaugural grant cycle — one year after the Trump administration gutted the National Endowment for the Arts. Major recipients included the PEN/Faulkner Foundation, the National Book Foundation, Words Without Borders, and Deep Vellum Publishing. But organizations including Red Hen Press, Aunt Lute Books, and Poetry Flash were denied funding, with Red Hen's deputy director warning that "the lack of funding may mean that organizations have to close." The fund aims to distribute at least $50 million over five years — but independent publishers are already sounding alarms.

4. Publishing's AI Debate Heats Up at the U.S. Book Show

The sixth annual U.S. Book Show took place June 2–3 in New York, and AI was unavoidable. The heads of Authors Equity, Hachette Book Group, and Sourcebooks all agreed that using AI for creative production was a no-go — but acknowledged other potential benefits across the industry. Meanwhile, the tension between publishing's veterans and its newer professionals was on full display, with sessions on BookTok and early-career development highlighting the generational divide shaping the industry's future. The AI-in-publishing conversation isn't going away — and the lines are hardening.

5. News Corp Is Coming for Its Share of the Anthropic Settlement

Publishers are watching the AI copyright fight closely, and News Corp just made its position crystal clear. News Corp CEO Robert Thomson confirmed the company expects to receive its share of the $1.5 billion settlement with Anthropic later this calendar year — a deal he framed as asserting the integrity of intellectual property and benefiting authors and publishers. He also issued a pointed warning about firms scraping and reselling copyrighted content without authorization. For indie authors and small presses watching how AI and copyright intersect, this settlement is one of the most important cases to follow in 2026.

Sources

1. Literary Hub / Book Marks — Best Reviewed Books, June 5, 2026 — https://lithub.com/what-should-you-read-next-here-are-the-best-reviewed-books-of-the-week-6-5-2026/

2. Publishers Weekly — HarperCollins U.S. Trade Unveils New Structure — https://www.publishersweekly.com/pw/by-topic/industry-news/publisher-news/article/100573-harpercollins-us-trade-unveils-new-structure.html

3. Publishers Weekly — Inaugural Literary Arts Fund Grants Prompt Reckoning with NEA Losses — https://www.publishersweekly.com/pw/by-topic/industry-news/awards-and-prizes/article/100570-inaugural-literary-arts-fund-grants-prompt-reckoning-with-nea-losses.html

4. US News / AP — The Literary Arts Fund to Distribute $7.7 Million in Grants — https://www.usnews.com/news/entertainment/articles/2026-06-04/the-literary-arts-fund-to-distribute-7-7-million-in-grants-to-40-organizations

5. Publishers Weekly — U.S. Book Show 2026 Coverage— https://www.publishersweekly.com/pw/by-topic/industry-news/index.html

6. Publishers Weekly — HarperCollins Has a 'Heated' Quarter — https://www.publishersweekly.com/pw/by-topic/industry-news/financial-reporting/article/100365-a-heated-quarter-for-harpercollins.html

7. Goodreads — Most Anticipated Books of 2026 — https://www.goodreads.com/book/popular_by_date/2026/6

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KL Adams is a literary blogger and fiction writer specializing in dark fantasy, vampire fiction, and paranormal romance. Follow on WordPress, [Inkitt](https://www.inkitt.com/KLAdams), and [Wattpad](https://www.wattpad.com/KLAdams53) for reviews, reading lists, and stories that haunt you long after the last page.

[Check out my Gumroad course.](https://kladams53.gumroad.com/l/hisznv)

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You may have heard something floating around YouTube lately about Dubai automating its entire government with AI. That’s not quite right — but what’s actually happening is arguably more interesting.

Here’s the real story, and why it matters to anyone thinking about where AI is headed in business.

First, Let’s Talk About the Difference Between AI That Answers and AI That Acts

Most of us have used AI as a question-answering machine. You type something in, it types something back. Maybe it writes a draft email or summarizes a document. That’s useful, but it’s still fundamentally passive. You ask, it responds.

Agentic AI is different. Think of it less like a smart search engine and more like a capable colleague you can hand a task to and walk away from. It doesn’t wait for your next prompt. It plans, makes decisions, takes steps, and completes the job — often checking its own work along the way.

A chatbot tells you what forms you need to renew your business license. An AI agent files the application, cross-checks your documents against current requirements, flags any issues, and confirms when it’s done.

That’s a meaningful leap. And the UAE just announced they’re building an entire government around it.

What Dubai Actually Announced

On April 23, 2026, the UAE Cabinet — led by Sheikh Mohammed bin Rashid Al Maktoum — unveiled a framework to move 50% of all government sectors, services, and operations to agentic AI within two years.

The quote from Sheikh Mohammed himself is worth sitting with for a moment:

“AI is no longer a tool. It analyses, decides, executes, and improves in real time. It will become our executive partner.”

That’s not marketing language. That’s a structural redesign of how a government operates.

The plan positions AI not as a support system for human workers, but as an autonomous participant in governance — one that monitors changes, evaluates options, issues decisions, and carries out multi-step processes without needing a human to approve each move.

It’s Already Happening — Not Just Planned

This isn’t a vision document gathering dust. The implementation is already underway.

In May 2026, the UAE’s Ministry of Human Resources flipped the switch on an agentic AI platform that automatically evaluates every new work permit application filed in the country. The system cross-checks qualifications, professional licenses, and salary data against a live database of skills shortages — and approves straightforward cases in hours instead of the weeks that manual review used to take. Applications with red flags still go to human reviewers, but the AI handles the bulk of the workload.

That’s not a pilot program. That’s a national labor system running on autonomous AI, right now, at scale.

And by 2025, before this latest announcement, more than 96% of government entities in Dubai had already adopted at least one AI solution, with 60% of residents actually preferring AI-supported services over the old way.

Why “Agentic” Is the Word to Know

If you’ve been following AI in business, you may have noticed this word cropping up more and more. There’s a reason.

Gartner predicts that by 2028, 33% of enterprise software will include agentic AI — up from less than 1% in 2024. And 15% of everyday work decisions will be made autonomously through AI agents.

The UAE government isn’t ahead of some distant future. It’s ahead of where most enterprises will be in just a couple of years. They’re essentially running a live, government-scale experiment in what agentic AI looks like when you build around it instead of bolting it on.

What This Means If You’re Not Running a Country

Here’s the practical takeaway for business leaders and entrepreneurs:

The question is no longer “should we use AI?” It’s becoming “what decisions are we comfortable letting AI make on its own — and which ones need a human in the loop?”

That’s the conversation the UAE is forcing its own ministries to have right now. Ministers and department heads are being evaluated over the next two years based specifically on how fast they implement these autonomous systems. The government isn’t asking people to think about AI. It’s building accountability structures around it.

Most businesses haven’t gotten there yet. They’re still in the “AI as assistant” phase — which is fine, but it’s a phase. The organizations that figure out where agentic AI fits into their workflows before it becomes standard will have a significant head start.

The UAE is making that case at a national level. Loudly.

The Part Worth Being Thoughtful About

It would be easy to read all of this as pure enthusiasm, and it’s worth pausing on the harder questions too.

Scaling autonomous systems across government raises real questions about governance, oversight, and accountability. When an AI system makes a consequential decision — about a visa, a business permit, a benefit — who is responsible if something goes wrong? How do you appeal a decision made by an algorithm?

These aren’t hypothetical concerns. They’re the questions every government and every enterprise will need real answers to as agentic AI becomes more common.

The UAE appears to recognize this by ensuring that intricate or flagged cases are still handled by human reviewers. Furthermore, the nation is making a substantial investment in training 80,000 government employees to collaborate with these systems rather than be superseded by them. Whether that balance holds as the rollout scales is something worth watching.

The Bottom Line

Dubai isn’t “fully automated.” But it is doing something no government has attempted at this scale: treating AI not as a tool to be assigned tasks, but as an active participant in how the whole machine runs.

That’s a unique philosophy. And whether you’re running a country or a company, the underlying question is the same: What happens to your organization when AI stops waiting to be asked?

The UAE decided to find out.

Klynn is an AI business educator and commentator covering artificial intelligence trends, enterprise AI adoption, and the business implications of generative AI. Published daily on Medium and Substack, Klynn helps professionals and entrepreneurs understand how AI is transforming industries worldwide. Follow Klynn for daily AI business insights.

For the past week, I jumped headfirst into the trending chaos everyone’s talking about.

Not because I thought I’d wake up a millionaire, but because I wanted to see the reality behind the hype for myself.

You know the posts I’m talking about:

“Make passive income with AI.”
“Sell digital products while you sleep.”
“$100 a day from your laptop — anyone can do this.”

If you’re online for more than ten minutes, you’ll bump into some version of this promise. So instead of just watching from the sidelines, I decided to test it.

I created products.
Built listings.
Designed graphics with AI.
Tested platforms.
Watched the analytics.
Learned the lingo.
And observed the entire ecosystem up close.

One Week Later: The Honest Results

✅ Traffic? Yes.
✅ Curiosity and clicks? Absolutely.
❌ Sales? Zero.

And honestly? I’m not mad about it.

This isn’t a doom-and-gloom post. I’m still wildly excited about AI — I work in this space every day, and I believe it’s genuinely transformative for creativity, business, fraud detection, and so much more.

But I also believe we need to talk honestly about the gap between possibility and probability.

The Part They Don’t Show You

Most of these “easy AI money” stories quietly leave out the real economics:

• Traffic almost always costs money

• Attention is expensive

• Building trust takes time

• Conversion rates are usually brutal

• Survivorship bias is everywhere (you only see the winners)

Someone might brag “I made $100 selling AI products!”
What they don’t tell you is how much they spent on ads, how many products flopped before that one, or how long they’d been building an audience.

As someone who specializes in fraud analysis and risk awareness, I see entire ecosystems built around monetizing people’s desperation. Courses about courses. Templates selling templates. Screenshots of revenue with no expense reports.

Real opportunities in AI exist — absolutely.
But so do slick predators dressed up as mentors.

Why I Don’t Regret Doing This

Experimentation is still one of the best ways to learn.

Even when it doesn’t pay off financially, it teaches you how markets move, how attention works, and how hope gets packaged and sold online.

I walked away with clearer eyes and better questions — and that’s valuable.

I’m not quitting my real work to chase an AI empire promised by an Instagram reel. But I’m glad I tested it myself instead of blindly believing the hype… or cynically dismissing it.

Curiosity without gullibility. That’s the sweet spot.

What about you? Have you tried any “Make Money with AI” experiments? Did they deliver, or did you also get more lessons than income? Drop your real experiences below — I read every comment.

Until next time, may your AI experiments be insightful, your prompts sharp, and your skepticism strong.

— KLynn ✨

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Yesterday at Google I/O, Sundar Pichai stood on stage and said something that should stop every business leader cold: “It’s 24/7, so you don’t need to keep your laptop open.”

He wasn’t talking about a chatbot. He was talking about Gemini Spark— and it may be the most consequential AI product announcement of 2026.

What Is Gemini Spark, Exactly?

Gemini Spark is a persistent AI agent that runs continuously on dedicated virtual machines within Google Cloud infrastructure. It’s designed to complete long-running tasks, integrate with tools, and operate directly inside the Chrome browser. Think less “AI assistant you talk to” and more “AI colleague that works while you sleep.”

Google describes Spark as a shift from an assistant that answers questions to “an active partner that does real work on your behalf and under your direction.” It already integrates with Gmail, Docs, and Google Workspace, with expansion to third-party tools via MCP planned for this summer.

For daily use, Google is also rolling out a Daily Brief feature — a personalized digest that sifts through your Gmail, Calendar, and Tasks to prioritize your day and suggest next steps.

Why This Is a Business Story, Not Just a Tech Story

Every enterprise has tasks that are repetitive, time-sensitive, and don’t require human judgment. Monitoring inboxes. Drafting follow-ups. Scheduling. Summarizing. Cross-referencing documents. These are exactly the workflows Spark is designed to absorb.

The implications are significant: the first companies to operationalize persistent AI agents won’t just move faster — they’ll redefine what a lean team looks like. A five-person operation running Spark across its Workspace environment could execute at the output of a team twice its size.

This isn’t hypothetical anymore. Gemini Spark is currently in beta and will be available first to trusted testers and Google AI Ultra subscribers starting next week. The agentic era isn’t coming. It’s already in your inbox.

The Bigger Picture

Google made clear at I/O that its goal is to help users “navigate their digital life” by taking action on their behalf — with the user remaining in direction of the agent, not the other way around. That framing is deliberate. Trust is the product as much as capability.

For business leaders, the question is no longer whether to integrate agentic AI — it’s how fast and how safely. The companies building governance around AI agent use today will have a significant advantage over those scrambling to catch up in 12 months.

The laptop can stay closed. The work doesn’t have to stop.

Sources

- [Google I/O 2026: All the Major AI Announcements — eWeek](https://www.eweek.com/news/google-io-gemini-agentic-ai-era-2026/)

- [Everything Google announced at I/O 2026 — 9to5Google](https://9to5google.com/2026/05/19/google-io-2026-news/)

- [Google debuts new AI models, personal AI agents — CNBC](https://www.cnbc.com/2026/05/19/google-ai-ultra-gemini-spark-omni.html)

- [Google I/O 2026: New Gemini app, Flash model, and agentic AI push — BusinessToday](https://www.businesstoday.in/technology/artificial-intelligence/story/google-io-2026-new-gemini-app-flash-model-and-agentic-ai-push-heres-everything-google-announced-532395-2026-05-20)

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Klynn is an AI business educator and commentator covering artificial intelligence trends, enterprise AI adoption, and the business implications of generative AI. Published daily on Medium and Substack, Klynn helps professionals and entrepreneurs understand how AI is transforming industries worldwide. Follow Klynn for daily AI business insights.

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I gave myself $100 and one day to build a passive income stream using AI.

No client pitching. No paid ads. No gimmicks.

Just Claude, ChatGPT, Canva, and Etsy.

Here is exactly what I built and what it cost:

— Etsy listing fee: $0.20

— Canva: already had it

— Claude Pro: already had it

— ChatGPT Plus: already had it

— Time invested: about 6 hours total

The product: a 50-prompt AI Business Starter Pack covering marketing, sales, operations, hiring, and strategy. Done-for-you prompts that work instantly with Claude or ChatGPT.

It is live right now for $9.

Why am I telling you this?

Because I talk about AI business tools every single day. My aim was to show, both to myself and to you, that the tools I cover are functional when actively used.

This is the social experiment. I am documenting everything publicly.

Week 1 update coming next week: views, clicks, and whether anyone actually bought it.

If you’d like to grab the pack at the launch price, the link is in the comments.

And if you have ever thought about building a digital product using AI but did not know where to start, watch this space. I am going to show you exactly how it unfolds, the good and the bad.

Follow along. This is going to be interesting.

— Klynn

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Klynn is an AI business educator and commentator covering artificial intelligence trends, enterprise AI adoption, and the business implications of generative AI. Published daily on Medium and Substack, Klynn helps professionals and entrepreneurs understand how AI is transforming industries worldwide. Follow Klynn for daily AI business insights.

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Most businesses didn’t adopt AI because they had a clear strategy. They adopted it because everyone else was, and sitting on the sidelines felt riskier than jumping in.

So they bought the tools. They set up the accounts. They told their teams to “start using AI” and waited for the productivity gains to show up.

And for many organizations, those gains have been... underwhelming.

Not because AI doesn’t work. It does. Remarkably well.

But adopting AI without a strategy is like buying a commercial kitchen and expecting gourmet meals to appear. The usefulness of the equipment depends on the operator’s skill and objective.

The companies struggling with AI right now don’t have an AI problem. They have a strategic problem. And until they recognize that distinction, no amount of new tools will fix it.

The Adoption Trap

Here’s what the adoption trap looks like in practice.

A company rolls out ChatGPT, Copilot, or whatever the tool of the moment is. Usage goes up. People start using it for emails, for summarizing documents, and for generating first drafts. Leadership sees activity and calls it progress.

But nothing fundamental has changed.

The workflows are the same. The decision-making process is the same. There has been no change to the organizational structure. AI has been applied broadly, leaving people confused next quarter, when competition remains unchanged.

The problem isn’t the tool. The problem is that nobody asked the harder question before they deployed it: What are we actually trying to change?

That question is where AI strategy begins. And most organizations skip it entirely.

What a Real AI Strategy Looks Like

A real AI strategy doesn’t start with tools. It starts with problems.

Specifically, it starts with three questions:

Where are our biggest bottlenecks? Focus on the hidden costs, not the obvious ones, which drain your resources and hinder growth. Where does work slow down? Which environments see decisions take longer than they should? Where are your best people spending time on things that shouldn’t require their expertise?

What decisions could be made faster with better information? AI’s most underused capability isn’t content generation. It’s pattern recognition at scale. Top AI-adopting organizations utilize it to expose insights teams lack the bandwidth to find, and to facilitate more intelligent, rapid choices.

Where could removing the friction compound over time? This is the big one. A ten percent improvement in one area is nice. A ten percent improvement across every handoff, every approval cycle, every communication loop — that’s transformational. AI strategy is about finding the places where small efficiency gains stack on top of each other until the cumulative effect is unrecognizable from where you started.

Once you’ve answered those questions, the tools become obvious. You’re not picking AI features, looking for a problem to solve. You’re solving actual problems and choosing the right tools to help you do it.

The Organizational Readiness Nobody Talks About

Here’s something the AI vendors won’t tell you: your organization might not be ready for AI — and that’s okay.

Not “not ready” in a technical sense. Most tools are accessible enough that technical readiness isn’t the issue. Not ready in a cultural sense.

AI adoption fails when organizations haven’t done the harder work of first clarifying their processes. If your workflows are inconsistent, if your data is messy, if your teams don’t have shared definitions of success — AI won’t fix any of that. It will amplify it.

Garbage in, garbage out isn’t just a data science principle. It’s an organizational truth. AI is extraordinarily good at doing things fast. If what it’s doing fast is wrong, confused, or misaligned, it just gets you to the wrong place faster.

The businesses winning with AI right now spent time before deployment asking uncomfortable questions. Are our processes documented? Are they consistent? Do our teams agree on what good looks like? Do we have the data infrastructure to support what we’re trying to build?

Those questions aren’t glamorous. They don’t make for exciting press releases. But they’re the foundation that everything else is built on.

The Speed Trap

There’s another mistake worth calling out, and it’s one that even sophisticated organizations fall into.

In the rush to adopt AI, companies are moving fast and skipping the measurement step. They deploy tools, see anecdotal improvements, declare victory, and move on to the next initiative.

This is dangerous for a reason that isn’t immediately obvious.

If you don't carefully assess the impact of AI adoption or monitor the right metrics pre- and post-implementation, learning becomes impossible. It's impossible to determine what is effective and what is not. You can’t double down on the things generating real value or course-correct on the things that aren’t. You end up with a portfolio of AI initiatives where nobody can honestly tell you which ones are moving the business forward.

New data from OpenAI’s B2B Signals report drives this home. Frontier companies — the ones pulling significantly ahead of the competition — aren’t just using more AI. They’re measuring it differently. They’re tracking operational outcomes, not just usage metrics. They’re asking whether AI is changing cycle times, decision quality, and business results — not just whether employees are logging in.

Measurement isn’t the exciting part of AI strategy. But it’s what separates the organizations building a durable advantage from the ones chasing a trend.

The Window Is Still Open

Here’s the good news.

Despite all the noise about AI moving too fast to keep up with, most industries are still early enough in adoption that the strategic window is open. The companies that take the time now to build a real AI strategy — one grounded in genuine business problems, organizational readiness, and disciplined measurement — still have the opportunity to pull ahead.

But that window isn’t permanent.

The gap between frontier companies and everyone else is already measurable and already compounding. Every quarter that passes without a coherent strategy is a quarter where that gap grows harder to close.

The businesses that will look back on this period as their inflection point aren’t the ones that moved fastest. They’re the ones who moved most deliberately — who asked the right questions before they bought the tools, who built on solid foundations, and who measured what mattered.

You don’t need to be a technology company to win with AI.

You need to be strategic.

And that, more than any tool on the market, is still very much within reach.

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Klynn is an AI business educator and commentator covering artificial intelligence trends, enterprise AI adoption, and the business implications of generative AI. Published daily on Medium and Substack, Klynn helps professionals and entrepreneurs understand how AI is transforming industries worldwide. Follow Klynn for daily AI business insights.

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Everyone’s debating whether AI will replace jobs.

The smartest companies have already moved past that conversation.

They’re not using AI to replace people. They’re using it to eliminate the invisible tax on their best ones — the daily grind of repetitive decisions, manual tracking, and cognitive overload that quietly drains your most capable employees before noon.

That’s the real shift. And it’s already happening.

Not robots. Not sci-fi. Not some dramatic overnight takeover.

Just thousands of small decisions being lifted off overwhelmed humans every single day.

The accounting team isn’t manually flagging duplicate invoices anymore. The compliance department isn’t spending half a workday hunting missing documentation. The operations manager isn’t carrying 47 follow-ups in her head, terrified of the one she’ll forget.

That’s where AI is becoming unstoppable — quietly, practically, without fanfare.

Because burnout isn’t a wellness problem. It’s a business problem.

Companies have been running on human memory and willpower for decades, and calling it culture. The cost of that — in turnover, errors, and lost momentum — is staggering. It just rarely shows up as a line item.

The next generation of winning companies won’t necessarily have better employees. They’ll have better systems — ones that protect those employees from drowning before they ever get to the work that actually matters.

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Something shifted in the fraud landscape in the last 18 months.

It used to be that sophisticated fraud — the kind that slips past detection systems, mimics legitimate behavior, and evades rules-based controls — required real expertise. Criminal networks needed technically skilled people. That barrier kept the volume of advanced attacks manageable.

That barrier is gone.

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Fraud Just Got a Major Upgrade

A new report from identity verification firm Sumsub reveals something that should stop every business owner and risk professional cold: sophisticated fraud has increased 180% compared to 2024.

Not 18%. One hundred and eighty percent.

The driver? AI tools and autonomous fraud agents are now being sold as a service — meaning criminals with zero technical background can access the same sophisticated techniques that used to require expert knowledge. The industry even has a name for it now: Fraud-as-a-Service.

Think about what that means for a moment. The same generative AI revolution that gave your business access to powerful tools? It gave fraudsters the exact same upgrade. And they wasted no time using it.

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What AI-Powered Fraud Actually Looks Like

This isn’t theoretical. Here is what businesses are dealing with right now in 2026:

Deepfake identity fraud. Generative AI can now produce synthetic voices, forged documents, and deepfake video convincing enough to pass identity verification checks. What used to take a criminal organization weeks to produce can now be generated in minutes.

Autonomous fraud agents. These are AI systems that attempt to pass your verification and onboarding processes — and here is the part that makes them particularly dangerous — they learn from every failed attempt. Each time your system blocks them, they adjust and try again. They don’t get tired. They don’t give up.

Hyper-targeted social engineering. AI tools can analyze publicly available data about your business, your employees, and your customers to craft phishing messages and scam communications that are eerily personalized. The generic “Nigerian prince” email is ancient history. Today’s social engineering reads like it came from someone who knows you.

Synthetic identity fraud at scale.

Fraudsters combine real and fabricated personal information to create entirely new identities — then use those identities to open accounts, apply for credit, and conduct transactions that look completely legitimate until the losses mount.

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The Uncomfortable Truth About Your Current Defenses

Here is what makes all of this so urgent.

Most businesses — and even most financial institutions — are still running fraud defenses built for a pre-AI threat environment. Rules-based systems. Static thresholds. Manual review processes.

Those tools were designed to catch yesterday’s fraud. They were never built to handle autonomous agents that adapt in real time, synthetic identities engineered to pass verification, or deepfakes sophisticated enough to fool human reviewers.

A recent study by Forrester Consulting put a number on this gap: 68% of business leaders admit their current security tools are no longer adequate to protect them against today’s threats.

That is not a small gap. That is the majority of businesses operating with defenses they already know are insufficient.

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Fighting AI Fraud Requires AI Defenses

The good news — and there is real good news here — is that the same AI technology powering the fraud is also the most effective weapon against it.

Rules-based systems lose to AI-powered fraud because they are static. AI-powered defenses win because they are adaptive. They learn. They update. They recognize new patterns before those patterns become systemic losses.

Specifically, the businesses best positioned right now are those that have moved to:

Behavioral intelligence over threshold rules. Instead of asking whether a transaction exceeds a dollar limit, AI asks whether this transaction looks like this customer. The difference in detection accuracy is enormous.

Real-time scoring across every touchpoint. Not just transactions — account creation, login behavior, device signals, and communication patterns all feed into a continuous risk picture that static rules can never replicate.

Models that retrain on new data automatically. When fraud tactics evolve — and they will, faster than ever — your defenses evolve with them. No waiting for a compliance team to manually update a rules library.

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What This Means for You Right Now

If you are a business owner or risk professional reading this, here are three questions worth sitting with today:

When were your fraud controls last updated? If the honest answer is “I’m not sure” or “more than a year ago,” your defenses have almost certainly fallen behind the current threat environment.

Do your current tools understand behavior — or just thresholds? If your fraud detection is primarily rules-based, you have a structural gap that AI-powered fraud is designed to exploit.

Are you treating fraud prevention as a one-time implementation or an ongoing system? The businesses losing the most to fraud in 2026 are the ones that set up controls and walked away. Fraud is not static. Your defenses cannot be either.

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The Bottom Line

The fraudsters upgraded. They did it fast, they did it at scale, and they are using the same technology you use to run your business.

The response is not panic. It is not paralysis. It is a clear-eyed decision to match the sophistication of the threat with the sophistication of the defense.

That is exactly what AI-powered compliance and risk platforms are built to do.

At Brevix AI, this is the problem we wake up thinking about every day — and the one we have built our platform to solve. If your fraud defenses were designed for a pre-AI world, it is time for a conversation.

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Follow Brevix AI on Substack for weekly intelligence on fraud, AI, and financial crime prevention. If this post was useful, share it with someone on your risk or compliance team — the more businesses that understand this shift, the better prepared we all are

Just five moves:

1. Require verification for payment changes

2. Separate duties (no one controls everything)

3. Review transactions weekly

4. Limit access to financial systems

5. Document processes (even basic ones)

Why This Matters

Most fraud doesn’t exploit complexity.

It exploits gaps.

The Real Lesson

Control isn’t about effort.

It’s about consistency.

CTA:
Follow Inkplots for practical fraud prevention that actually fits your day-to-day.

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Sources:

• Small Business Administration – Cybersecurity for Small Business
  → https://www.sba.gov/business-guide/manage-your-business/stay-safe-cybersecurity

• Federal Communications Commission – Small Biz Cyber Planner
  → https://www.fcc.gov/smallbizcyberplanner

But that’s not the full story.

Fraud is getting better.

What’s Changing

• More personalized scams

• Better design (websites, apps, emails)

• Faster execution cycles

• Cross-channel attacks (email + text + phone)

What This Means

You can’t rely on:

• “Gut feeling”

• Obvious red flags

• Outdated training

The New Risk

The biggest threat isn’t ignorance.

It’s false confidence.

The Shift You Need

Move from:

• Reactive → Preventative

• Trust-based → Verification-based

• Informal → Structured

CTA:
Inkplots helps you stay ahead of how fraud is evolving—not how it used to work.

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Sources:

• Federal Trade Commission – Consumer Sentinel Network Data Book
  → https://www.ftc.gov/reports/consumer-sentinel-network-data-book-2023

• Federal Bureau of Investigation – IC3 Report
  → https://www.ic3.gov

Most fraud doesn’t require hacking.

It requires one thing:

A single unchecked action.

The Tactic: Two-Step Verification for Money Movement

Anytime money moves:

1. One person initiates

2. A second person verifies

No exceptions.

Why This Works

It breaks the fraud chain.

Most scams rely on:

• Speed

• Isolation

• Lack of oversight

Adding one more human step destroys all three.

Where to Apply It

• Vendor payments

• Wire transfers

• Refund processing

• Payroll changes

Bonus Layer

Add channel separation:

• Request comes via email

• Verification happens via phone

The Real Lesson

You don’t need complex systems.

You need friction in the right places.

CTA:
Follow Inkplots for practical fraud prevention that actually fits your day-to-day.

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Sources:

• Committee of Sponsoring Organizations of the Treadway Commission – Internal Control Framework
  → https://www.coso.org

• Association of Certified Fraud Examiners – Occupational Fraud Report
  → https://www.acfe.com/fraud-resources/report-to-the-nations

It starts with an email that looks… normal.

“Hey—our banking info has changed. Please send payment to the updated account.”

No urgency. No red flags. Sometimes even comes from a real vendor’s email.

And that’s the problem.

The Mistake

Small businesses often:

• Update payment info based on email alone

• Skip verification to “save time”

• Trust existing vendor relationships too quickly

What Actually Happens

This is called Business Email Compromise (BEC).

Fraudsters:

• Gain access to a vendor’s email

• Monitor communication patterns

• Insert themselves at the perfect moment

• Redirect payments to their account

Once the money is sent—it’s rarely recovered.

Why It Works

Because it doesn’t feel like fraud.

It feels like business as usual.

How to Fix It (Immediately)

Create one rule:

No payment detail changes without verbal verification

Call the vendor using a known number—not the one in the email.

Also:

• Require dual approval for payment changes

• Flag any “urgent” payment shifts

• Train your team to question normal-looking requests

The Real Lesson

Fraud doesn’t break your systems.

It blends into them.

CTA:
Follow Inkplots for practical fraud prevention that actually fits your day-to-day.

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Sources:

• Federal Bureau of Investigation – BEC Public Service Announcement
  → https://www.ic3.gov/Media/Y2023/PSA230504

• Department of Justice – BEC Case Prosecutions
  → https://www.justice.gov/criminal-fraud/business-email-compromise

• Cybersecurity and Infrastructure Security Agency – BEC Guidance
  → https://www.cisa.gov/news-events/news/business-email-compromise

It didn’t look like fraud.

It looked like a clean interface. A polished dashboard. Charts are moving upward. A “support rep” who responded instantly.

And for thousands of people—including small business owners—it looked like an opportunity.

Until it wasn’t.

What Happened

In one of the largest recent fraud schemes, organized groups created fake crypto investment platforms that mimicked legitimate apps.

Victims were:

• Recruited through social media or text

• Encouraged to “test” the platform with small deposits

• Shown fake gains to build trust

• Then persuaded to deposit larger amounts

Withdrawals? Blocked.

Accounts? Frozen.

Money? Gone.

Total losses: $700M+

Why This Worked

This wasn’t a technical hack.

It was a trust hack.

Fraudsters exploited:

• Familiar UI/UX patterns (looked like real fintech apps)

• Gradual commitment (“just try $500”)

• Social engineering (friendly “advisor” relationships)

What This Means for Small Business Owners

If you run a business, you’re already managing cash flow, vendors, and growth decisions.

That makes you a prime target.

Because:

• You’re used to evaluating investments

• You’re open to “better returns”

• You move money more frequently than individuals

How to Protect Yourself

Before you move money into anything:

• Verify the platform independently (not through links sent to you)

• Check regulatory registrations

• Never trust “guaranteed” returns

• Test withdrawals early—not just deposits

The Real Lesson

Fraud today doesn’t look suspicious.

It looks professional.

That’s the shift.

CTA:
If you want to protect your business from financial blind spots, follow Inkplots. We break down real-world fraud so you don’t learn the hard way.

Sources:

• Federal Bureau of Investigation – Internet Crime Complaint Center (IC3) Annual Report
  → https://www.ic3.gov/Media/PDF/AnnualReport/2023_IC3Report.pdf

• Federal Trade Commission – Investment Scams Data
  → https://consumer.ftc.gov/articles/what-know-about-cryptocurrency-and-scams

• Chainalysis – Crypto Crime Report
  → https://www.chainalysis.com/reports/crypto-crime-report/

Fraud in business environments is often imagined as disruptive, visible, and external—an intrusion that breaks systems and leaves clear traces of misconduct. However, some of the most financially damaging schemes occur not through disruption, but through quiet integration into existing processes. Internal fraud, particularly within accounting functions, frequently exploits areas of assumed normalcy rather than areas of heightened scrutiny. One such area is Accounts Receivable (AR), specifically aging receivables. The case of Kami Power illustrates how financial manipulation can remain undetected for extended periods by leveraging the structural assumptions embedded in standard accounting practices.

Accounts receivable represent funds owed to a company for goods or services delivered. Within this system, aging reports categorize outstanding balances by how long invoices remain unpaid, typically segmented into 30-, 60-, and 90-day intervals. These aging structures are designed to support collection efforts and provide insight into cash flow expectations. However, they also introduce a critical vulnerability: the expectation of delayed payments. As a result, aging balances are often reviewed at a summary level rather than investigated at the transaction level (Investopedia, 2023). This reliance on aggregated reporting creates an environment in which discrepancies can persist without immediate detection.

In cases of internal fraud involving AR, the misapplication of payments is a common tactic. Rather than recording payments to the appropriate customer accounts, funds may be diverted or improperly allocated, leaving the corresponding receivable balances outstanding. This manipulation causes accounts to appear delinquent even after payment has been received. Because overdue accounts are a routine occurrence in most businesses, such discrepancies are frequently attributed to customer behavior rather than internal error or misconduct. According to the Association of Certified Fraud Examiners (ACFE, 2022), asset misappropriation schemes—particularly those involving billing and receivables—are among the most common forms of occupational fraud and often persist for extended periods before detection.

The persistence of these schemes is closely tied to internal control deficiencies. The Committee of Sponsoring Organizations of the Treadway Commission (COSO, 2013) emphasizes the importance of segregation of duties as a foundational control mechanism. When a single employee can receive payments, record transactions, and reconcile accounts, the opportunity for manipulation increases significantly. Without independent verification, fraudulent adjustments can be concealed within routine processes. In small and mid-sized businesses, where resource constraints often limit the separation of responsibilities, this risk is particularly pronounced.

Another contributing factor is the reliance on assumption-driven accounting practices. Organizations frequently assume that balances reflected in AR represent legitimate outstanding obligations. This assumption reduces the likelihood of reconciling receivables with actual cash inflows consistently. As a result, discrepancies between recorded receivables and bank deposits may go unnoticed. The Federal Bureau of Investigation (FBI, n.d.) notes that internal fraud schemes often exploit trust and routine, allowing perpetrators to operate undetected by aligning their activities with expected patterns rather than deviating from them.

Detection of such fraud typically occurs when inconsistencies become too significant to ignore. This may arise through internal audits, external audits, or customer inquiries indicating that payments have already been made. At this stage, forensic accounting techniques are employed to trace transactions, identify misapplications, and establish patterns of behavior. These investigations often reveal prolonged periods of undetected activity, highlighting systemic weaknesses rather than isolated incidents.

The implications of AR-related fraud extend beyond financial loss. Organizations may experience reputational damage, operational disruption, and erosion of internal trust. Additionally, the costs associated with investigation, legal proceedings, and remediation can further compound the impact. Despite these consequences, many businesses continue to rely on traditional reporting methods that do not adequately address the underlying risks.

Advancements in data analytics and artificial intelligence offer potential solutions to these challenges. By analyzing transaction-level data, organizations can identify anomalies that may not be visible through standard reporting. For example, automated systems can detect discrepancies between cash receipts and receivable postings, flag unusual adjustments, and identify patterns associated with specific users or accounts. These tools enhance, rather than replace, human oversight by providing deeper visibility into financial processes.

Ultimately, the key lesson from cases involving AR manipulation is that fraud does not always present itself as an obvious deviation from normal operations. Instead, it often resides within the boundaries of what is considered acceptable or routine. Aging receivables, by their nature, represent delayed resolution. When those delays are not actively questioned, they can serve as a cover for misconduct.

Organizations must move beyond passive monitoring and adopt a more investigative approach to financial oversight. This includes implementing stronger internal controls, regularly reconciling receivables with cash inflows, and fostering a culture in which anomalies are examined rather than explained away. By doing so, businesses can reduce their exposure to internal fraud and ensure that their financial systems reflect reality rather than assumptions.

References (APA Style)

Association of Certified Fraud Examiners. (2022). Report to the Nations: Global study on occupational fraud and abuse. https://www.acfe.com/report-to-the-nations

Committee of Sponsoring Organizations of the Treadway Commission. (2013). Internal control—Integrated framework.

https://www.coso.org

Federal Bureau of Investigation. (n.d.). White-collar crime. https://www.fbi.gov/investigate/white-collar-crime

Investopedia. (2023). Accounts receivable definition. https://www.investopedia.com/terms/a/accountsreceivable.asp

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Instead, the scheme lived quietly inside one of the most overlooked areas of accounting: Accounts Receivable.

Through the case of Kami Power, we examine how millions of dollars can move through a business without immediate detection—simply by manipulating timing, assumptions, and trust.

This isn’t just a story about one employee.
It’s a deeper look at how financial systems are relied upon, how blind spots form, and why “normal” is often the most dangerous place for fraud to hide.

If your receivables are growing, your reports look slightly off, or your systems rely more on assumption than verification—this episode will change how you see your books.

Because sometimes, the problem isn’t what’s missing.
It’s what’s already there.

📌 Show Notes & Sources

Case & Legal Context

• U.S. Department of Justice – Financial fraud and embezzlement case records
  https://www.justice.gov/criminal-fraud

• Federal Bureau of Investigation (FBI) – Financial Crimes Overview
  https://www.fbi.gov/investigate/white-collar-crime

Fraud & Internal Controls

• Association of Certified Fraud Examiners (ACFE) – Report to the Nations
  https://www.coso.org

Accounting & Receivables Risk

• Investopedia – Accounts Receivable & Aging Reports Explained
  https://www.investopedia.com/terms/a/accountsreceivable.asp

• Journal of Accountancy – Fraud risks in receivables and internal accounting controls
  https://www.journalofaccountancy.com

Key Takeaway Topics

• Misapplication of payments

• Aging AR manipulation

• Internal fraud detection gaps

• Lack of segregation of duties

• Behavioral fraud patterns in accounting roles

If we were sitting across from each other over coffee and you asked me how Bernie Madoff managed to steal billions of dollars for decades, my first response wouldn’t be outrage.

It would be disbelief.

Not because the fraud was clever.
But because the warning signs were sitting in plain sight.

Bernard Madoff ran what would become the largest Ponzi scheme in history, defrauding investors of an estimated $65 billion in reported account balances before the scheme collapsed in 2008. Yet investigators later discovered that the system holding it together wasn’t sophisticated technology or complex derivatives.

It was something much simpler: trust and silence.

And looking back, the red flags were everywhere.

Red Flag #1: Returns That Were “Too Consistent”

One of the most obvious warning signs was Madoff’s investment performance.

For years, investors were told their portfolios were generating steady positive returns of around 10–12 percent annually, regardless of market conditions.

Markets crashed.
Markets boomed.
Markets moved sideways.

Madoff’s numbers barely moved at all.

At first glance, consistency sounds like good portfolio management. But in reality, markets are volatile. Even the most skilled investors experience fluctuations.

When someone claims they can deliver stable profits year after year, it’s usually not brilliance.

It’s mathematics refusing to cooperate with reality.

Financial analyst Harry Markopolos recognized this early. After examining Madoff’s trading strategy in 1999, he concluded something shocking:

The returns were mathematically impossible.

Markopolos spent years warning regulators. His report to the SEC famously carried the blunt title:

“The World’s Largest Hedge Fund Is a Fraud.”

And yet, nothing happened.

Red Flag #2: A Strategy No One Could Verify

Madoff claimed he used a trading approach known as a “split-strike conversion strategy.”

In theory, the strategy involved:

• Buying stocks in the S&P 100
• Purchasing options to hedge downside risk
• Selling options to generate additional income

On paper, the explanation sounded sophisticated.

But when analysts tried to verify the trades, something didn’t add up.

The options volume required to execute Madoff’s strategy didn’t exist in the market.

In other words, the trades he claimed to make would have been visible across the entire options market.

They weren’t.

Red Flag #3: Secrecy Instead of Transparency

Another strange feature of Madoff’s operation was how little transparency investors received.

Most investment funds use independent firms to handle:

• trade execution
• compliance oversight
• custodial services
• auditing

Madoff handled almost all of this internally.

Even more concerning, the auditing firm responsible for reviewing billions of dollars in client funds was a tiny accounting office with only a few employees.

For a financial operation managing tens of billions, that should have raised immediate alarms.

Instead, it was largely ignored.

Red Flag #4: Exclusivity as a Sales Tool

Madoff’s fund wasn’t marketed like a typical investment product.

Instead, access felt like a privilege.

Investors often described the experience as being invited into a private club. Some were even turned away initially, only to be accepted later.

Psychologically, this tactic is powerful.

When something feels exclusive, people assume it must be valuable.

Madoff leveraged that perception to build an aura of prestige around his fund. Wealthy investors, charitable foundations, universities, and even financial professionals wanted in.

Very few stopped to ask the uncomfortable question:

Why was this opportunity so secretive if it was legitimate?

Red Flag #5: Regulators Looked — But Didn’t See

Perhaps the most troubling part of the Madoff story is that regulators actually investigated him multiple times before the collapse.

The U.S. Securities and Exchange Commission received several credible complaints about his operation over the years. Yet none of the investigations uncovered the fraud.

In hindsight, many experts believe the complexity of financial markets, combined with Madoff’s reputation as a respected industry figure, made regulators hesitant to push harder.

Authority can create a powerful illusion of legitimacy.

And Madoff had plenty of authority.

He had served as chairman of NASDAQ.
He was a well-known figure on Wall Street.
He moved comfortably within elite financial circles.

Sometimes credibility is the perfect disguise.

The Collapse

The entire scheme unraveled during the 2008 financial crisis.

As markets crashed, investors began requesting large withdrawals.

Ponzi schemes rely on new money to replace the amounts withdrawn by earlier investors. Once the withdrawals exceed incoming investments, the system collapses.

By December 2008, Madoff confessed to his sons that his investment operation was essentially “one big lie.”

They reported him to federal authorities.

He was arrested the next day.

In 2009, Bernard Madoff was sentenced to 150 years in federal prison.

The Real Lesson

It’s tempting to treat the Madoff scandal as the story of a brilliant criminal mastermind. The reason for this is that such content garners significant media attention. It dramatizes a scheme in which affluent people are conned by a clever architect of fraud who outwits everyone.

Unfortunately, the truth appears far less reassuring.

Cleverness alone is seldom enough to explain the vast scale of the Madoff fraud. It endures by subtly conforming to human psychology and established institutional practices.

The first factor was reputation.

Madoff didn’t start as an outsider. For decades, he worked on earning respect in the financial world. His contributions helped establish electronic trading, and he eventually served as the chairman of NASDAQ.

That reputation mattered.

The second factor was consistency within the volatility.

Everyone knows the market is unpredictable - up and down with wild swings back and forth. It's not realistic to be perfectly consistent in yields all the time. Losing is an unavoidable part of the investing experience, a reality that market participants come to accept.

Madoff's portfolio remained stable amidst the chaos of the world. He reported steady gains year after year, even during major downturns. Ironically, the feature that drew investors was the same feature that mathematically exposed the scheme.

Financial analyst H. Markopolos recognized this pattern. After analyzing the strategy Madoff claimed to use, he concluded the returns were statistically impossible and repeatedly warned the US Securities and Exchange Commission (SEC).

The third factor was institutional failure.

Banks may have questioned Madoff’s strategy.
However, they did nothing to prevent the collapse.
Although analysts pointed out inconsistencies and regulators launched several investigations, the banks failed to detect the fraud.

It wasn’t because they weren’t there.

The reveal was less climactic. It was because no single failure appeared catastrophic in isolation. Each warning sign was small enough to rationalize away. Only in hindsight can we revisit the signals as a clear path to failure.

Another factor is the environment of silence.

Inside many organizations and investment circles, asking the wrong question or being the squeaky wheel can carry social and financial consequences. Rather than focusing on the issue, this might convey skepticism about the reporter. It jeopardizes lucrative opportunities and leads people to choose silence.

Choosing to act costs more than the consequences of speaking up or whistleblowing.

The Madoff scheme wasn’t sustained by one lie. Its endurance was a result of numerous minor choices not to contest the prevailing story.

The Lesson for Today

Bernie Madoff died in prison in 2021, but the conditions that allowed his scheme to flourish still exist.

Markets still reward exclusivity.
Reputation still substitutes for verification.
Institutions still struggle to challenge respected insiders.

Which means the most important protection against fraud isn’t regulation alone.

It’s skepticism.

Rather than being cynically suspicious, it’s about being willing to challenge and inquire, even if others are satisfied with the explanations.

Because the most dangerous red flag in finance is not volatility.

It’s perfect certainty.

Sources

U.S. Securities and Exchange Commission
https://www.sec.gov/news/studies/2009/oig-509.pdf

Federal Bureau of Investigation – Madoff Ponzi Scheme
https://www.fbi.gov/history/famous-cases/bernie-madoff

U.S. Department of Justice Case Files
https://www.justice.gov/archives/opa/pr/justice-department-s-10th-distribution-brings-total-provided-over-43b-nearly-full-recovery

Harry Markopolos Testimony to Congress
https://financialservices.house.gov/calendar/eventsingle.aspx?EventID=231766

SEC Investor Alert on Ponzi Schemes
https://www.sec.gov/enforcement-litigation/litigation-releases/lr-20889

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Stories like the Madoff case aren’t just financial history—they’re case studies in human behavior, institutional failure, and the psychology of deception.

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