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AI

The Cryptoqueen Who Bought Her Own Forbes Cover and Vanished With Billions

Earmark Team · July 22, 2026 ·

It’s June 11, 2016, at London’s Wembley Arena. 11,500 people are on their feet. The lights go down. Alicia Keys blasts through the speakers. Fireballs shoot up from the stage as Dr. Ruja Ignatova walks out in a floor-length burgundy ball gown covered in black sequins. The crowd goes wild.

To them, she’s not just a founder. She’s the Cryptoqueen who built the next Bitcoin. And tonight she’s announcing OneCoin has become so successful they’re running out of coins. So she’s going to make more. A lot more.

Nobody in the room seems to hear a problem with that.

This scene comes from Episode 114 of Oh My Fraud, hosted by Caleb Newquist. And Ruja’s story shows how a $4 billion fraud needed no complex financial engineering, just a database, manufactured credibility, and victims who were too invested to ask the right questions.

The Woman Who Sold Herself

Ruja Ignatova was born in Ruse, Bulgaria, in 1980. Her father was a mechanical engineer, her mother a nursery school teacher. When Ruja was ten, shortly after the fall of the Iron Curtain, the family moved to a small town in Germany called Schramberg.

Ruja was exceptional from the start. She earned a doctorate in private international law from the University of Konstanz and completed a master’s in European law at Oxford. Her promotional materials also claimed a stint at McKinsey, though journalists haven’t been able to verify that one. But even without McKinsey, the rest checks out. She has a real doctorate and a real Oxford degree.

As Caleb puts it, “She wasn’t bluffing about the homework. She’d done the homework.” Ruja could speak with real authority on monetary policy and financial revolution because she’d actually studied the material. She showed up to meetings like someone who “decided to be the most credentialed person in the room out of pure spite.”

But Ruja wasn’t selling a cryptocurrency. She was selling herself. She was always “Doctor Ruja,” with the title and the gravitas.

The credentials mattered because they made everything else believable. In 2014, she was named Bulgaria’s Businesswoman of the Year. She spoke at an event organized by The Economist. And then there was her face on the cover of Forbes magazine, which she circulated at recruiting meetings and shared in WhatsApp groups. It looked like the establishment had personally signed off on her.

Except Forbes never did. The cover was a paid advertisement tied to the Bulgarian edition in May 2015. As Caleb notes, “She bought the credibility and handed it to herself, gift wrapped with a bow on it.” By the time anyone thought to check, the damage was done.

The Red Flags That Nobody Wanted to See

The warning signs weren’t hidden. In 2012, Ruja was convicted of fraud in Germany. She and her father had bought a struggling steelworks factory in Bavaria, promising to save jobs. The factory collapsed anyway, and a German court found the collapse to be criminal. She got a 14-month suspended sentence and moved on.

The next year, she turned up in something called BigCoin, a multi-level marketing scheme dressed up as a currency that functioned exactly like a Ponzi scheme and collapsed like one, too. Somehow, she walked away clean.

A fraud conviction one year. A failed fake cryptocurrency the next. So naturally, in 2014, she started another OneCoin.

She didn’t build it alone. Her co-founder was Karl Sebastian Greenwood, a Swedish MLM veteran who’d spent years perfecting the art of getting ordinary people to hand over money in exchange for promises. When BigCoin collapsed in 2013, Sebastian was there too. The two didn’t drop the idea; they just gutted it for parts, slapped on a new label, and relaunched.

Federal filings later identified Sebastian as OneCoin’s “Master Distributor 001,” and Ruja herself credited him as the architect of the entire MLM structure. She could fill an arena. He could make sure the arena kept refilling itself.

In a 2014 email, Ruja summed up their partnership bluntly, saying the whole thing would be “MLM meets the Bitch of Wall Street.”

And we know exactly what they thought of the operation because prosecutors later got their emails. Before launch, before a single package sold, Ruja wrote to Sebastian with the exit plan: “Take the money and run and blame somebody else for this.”

They’d already written the ending.

How the Machine Actually Worked

By 2014, Bitcoin had become a cultural phenomenon. Early adopters were sitting on fortunes. Everyone had a story about someone who bought in for a few hundred bucks and was now rich. And everyone who’d heard about it too late was nursing a very specific kind of regret.

Ruja handed that feeling a product. OneCoin was Bitcoin, but better. And it was for everyone, not just the tech bros. She called it “the Bitcoin killer.”

How it works was the whole joke. You didn’t buy OneCoin directly. That would have created securities problems. Instead, you bought “educational packages,” which were courses on cryptocurrency trading sold through One Academy. The packages had names like Starter, Trader, Pro Trader, Executive Trader, and Tycoon Trader. A Starter package costs about €100 and includes a PDF and some tokens. A Tycoon Trader costs €5,000. Eventually, they added tiers up to €118,000, because apparently someone, somewhere, was willing to pay six figures for a PDF.

The PDFs were largely plagiarized from free sources, including Wikipedia. Investors later discovered that thousands of euros’ worth of “proprietary financial education” was just copied and pasted from the internet. Nobody noticed because nobody was buying them for the content. The PDFs were, as Caleb calls them, “a legal costume.”

What you were actually buying was tokens. These got “mined” and converted into coins at a rate OneCoin set, and could change whenever it wanted. The coins showed up in your digital wallet, and you could watch the price tick upward on their internal exchange, xcoinx. The growth was steady and always up.

Think about that feeling for a second. You check your wallet and the number’s up again. Your friends see the same thing. You’re all in a WhatsApp group, sharing screenshots, talking about retiring early. It feels like you’re part of something real.

By the time it was over, OneCoin had taken in more than $4 billion from investors around the world.

Why Nobody Could Get Their Money Out

Being able to cash out is kind of important when you’re investing. But the xcoinx exchange had tight daily withdrawal limits calibrated to ensure only a trickle of cash could ever leave. You could request a wire transfer, but it was slow, frequently delayed, and often just didn’t go through.

Most people didn’t even try to cash out early. They were holding on for the moon, waiting for the public listing Ruja kept promising.

The real money was in recruitment. Bring in new people, and you earn commissions on their purchases, on the purchases of people they recruit, and so on down the chain. The aggressive early recruiters with big networks were making extraordinary sums in real currency.

This created a perfect loop. The people making the most money were the most devout believers, and their success was living proof to everyone below them that this was real. Why would you doubt the guy one rung up when you could see his commission checks clearing?

The community that formed called itself “One Life.” They had private WhatsApp groups, newsletters and motivational events in hotel ballrooms across continents. When regulators or journalists raised concerns, they had a script ready. These were attacks from the banking establishment, terrified of losing power. Anyone inside who asked uncomfortable questions got the same treatment. They were told they’re being negative, letting the team down, and to just trust the process.

By 2016, money was pouring in from China, Uganda, Pakistan, Brazil, Germany, Norway, Yemen, and dozens of other countries. It spread through churches, immigrant communities, professional networks, and families. As Caleb puts it, it went “wherever trust already existed. And then it burned that trust for fuel.”

The Moment It All Should Have Ended

Back to Wembley Arena, June 11, 2016. The entire fraud revealed itself, and the crowd cheered anyway.

To understand why this moment matters, you need to know one thing about cryptocurrency. In Bitcoin, the hard cap of 21 million coins is the whole point. It’s enforced by a decentralized network of thousands of computers that no single person controls. The scarcity is structural, built into the protocol.

OneCoin’s supply cap was different. It was, as Caleb describes it, “a number in a database in an office building in Sofia, Bulgaria, controlled entirely by Ruja. She could change it whenever she wanted.

So when she announced she was expanding the supply from 2.1 billion to 120 billion coins, multiplying it by nearly 60 with a few keystrokes, she was showing everyone exactly what OneCoin was. There was no protocol or blockchain. She could change it on a whim.

She sold it as a gift. For their support in “phase one,” she’d double the coins in everyone’s account.

The crowd cheered.

She had just told 11,500 people that their life savings were sitting in something she could multiply by 60 whenever she felt like it. And they cheered because by June 2016, most of them were too far in to hear what she’d actually said. They’d recruited their families and staked their credibility on this being real. The cost of hearing “the founder just proved the coin supply is completely made up” was too high to pay.

So they didn’t hear that. They heard, “I’m so confident I’m doubling your coins.”

The Collapse and the Getaway

By 2017, the walls were closing in. Multiple countries had enacted restrictions. Journalists kept publishing investigations. Prosecutors in Germany and New York were building cases.

Then came the clearest evidence yet. In early 2017, xcoinx went down “for maintenance” and never came back up. A real exchange doesn’t have a switch one person can flip. But xcoinx did, because it was never a market, just a number OneCoin employees updated on a ledger nobody else ever saw.

There was no blockchain underneath any of this. In an email prosecutors later obtained, Sebastian spelled it out: OneCoin was “not mining actually, but telling people shit.”

On October 12, 2017, a federal arrest warrant went out for Ruja on charges of wire fraud, securities fraud, and money laundering. She was scheduled to appear at an event in Lisbon shortly after. She never showed.

FBI documents revealed what actually happened. On October 25, 2017, she checked in at Sofia airport, boarded a Ryanair flight to Athens, landed, and disappeared. The FBI believes she likely had help.

She’d seen it coming. Prosecutors say she had bugged her American boyfriend’s apartment and discovered he was cooperating with the FBI. She was executing step one of her 2014 exit plan: “Take the money and run and blame somebody else.” 

The Human Cost in Three Stories

While Ruja vanished, real people were left holding the bag.

Jennifer McAdam, the daughter of a Scottish coal miner, got into OneCoin through a family member she trusted completely. She lost £15,000, the entire inheritance her father left her. She’s spent years trying to get it back, helping found a victim support group. As she put it, “The pain and suffering from losing all your finances, your home, your family and your loved ones come alongside with trusting these fraudsters.”

Igor Alberts, an experienced MLM professional from Amsterdam, made €90,000 in his first month. Within a year, he and his partner were clearing €2 million a month. They poured it straight back into more packages, doing the math on how many coins they’d need to become billionaires. They lost everything.

Daniel Lionheart, 22 years old in Uganda, sold three goats to buy a $250 starter package in 2017. By 2019, when BBC journalists visited, neither Daniel nor the woman who recruited him had told the other people they’d brought in that the money was gone. His recruiter told reporters, “I’m somehow hiding myself. I don’t want those people I introduced to OneCoin to see me moving around. They can easily kill me.”

The people running the scam said what they thought of these investors in private emails, calling the coin “trashy” and the investors “idiots” and “crazy.” Constantine, Ruja’s brother, who later ran the company and went to prison for it, texted Sebastian, “The network would not work with intelligent people.” Then he added a winking emoji.

Where Is She Now?

OneCoin somehow kept going after Ruja disappeared. Constantine stepped in as the new face. Events kept happening, and packages kept selling for almost two more years.

Eventually, the co-conspirators fell one by one. Karl Sebastian Greenwood was arrested in Thailand in 2018, pleaded guilty, and got 20 years in prison. He had to forfeit $300 million. Mark Scott, a lawyer who laundered $400 million through fake private equity funds, got 10 years. Constantine was arrested at LAX in 2019, cooperated with authorities, and served 34 months.

In June 2022, the FBI put Ruja on its Ten Most Wanted list. She’s currently the only woman on it and one of only 11 women ever to appear on it since 1950. The reward is up to $5 million. She still hasn’t been found.

The theories about where she is range from grim to exotic. One Bulgarian report claims she was murdered on a yacht and dumped in the Ionian Sea. German investigators think she’s living in Cape Town under a false identity. The strongest active lead points to South Africa. German documentary filmmaker Johann von Mirbach, who’s tracked Ruja for years, says she’s living in an upscale part of Cape Town under a false identity, based on information from South African security sources.

Another theory links her to Russia, where a journalist reported that Ruja was connected to Kremlin-linked interests through her former security adviser.

Meanwhile, the legal machinery keeps grinding on without her. In 2025, German prosecutors in Bielefeld filed charges specifically to stop the statute of limitations from running out on a woman they can’t find. In January 2026, the Royal Court of Guernsey seized more than £8.5 million from accounts tied to two Kensington flats Ruja bought through offshore shell companies, with the money now routed to Bielefeld for victim compensation. All told, over years of seizures in multiple countries, authorities have clawed back tens of millions of euros from the $4 billion invested.

The Lesson Underneath the Fraud

Strip away the arena, the ball gown, the Forbes cover, and the fugitive on the run, and OneCoin comes down to one sentence: every piece of evidence that it was real came from the people selling it. The price, the wallet balance, the market cap that supposedly beat every coin but Bitcoin, all of it was generated by the same company collecting the money. There was no ledger, auditor, or independent party confirming a single number on that screen.

What makes this case interesting is there was no exotic financial engineering or elaborate accounting tricks. Just timing, that Bitcoin FOMO hit right when Ruja needed it to. Just trust, since your recruiter was your aunt, your brother-in-law, or someone from your church. Doubting OneCoin meant doubting them. And by the time most investors had real doubts, they’d already recruited people and vouched for it personally. Admitting they were wrong meant admitting it to everyone they’d brought in.

The one question that would have protected every person in this story is, “Says who?”

There’s a lot more in the full episode that doesn’t fit in a blog post. Listen to Episode 114 of Oh My Fraud, and if you’re a CPA or work in accounting, you can earn free NASBA-approved CPE for listening through Earmark.

The Big Four Keep Publishing Fake AI Citations and It’s Getting Embarrassing

Earmark Team · July 20, 2026 ·

A solo accountant can complete two years of bookkeeping in a few hours using Claude Cowork. But KPMG had to pull an entire AI report after 89% of its citations turned out to be fake. The AI revolution in professional services is already sorting winners from losers.

In episode 493 of The Accounting Podcast, hosts Blake Oliver and David Leary tackled a cluster of stories that paint a clear picture of how AI is restructuring professional services right now in real workflows, real paychecks, and real embarrassments for the Big Four.

There’s a growing divide between professionals who use AI carefully with human oversight (and get massive productivity gains) and those who rush to market themselves as AI experts while failing basic verification. This episode covers the Big Four’s repeated AI failures, the incredible productivity gains available to practitioners who use AI right, and the broader industry signals showing how AI is reshaping everything.

 

The Big Four’s “Vibe Citation” Problem Keeps Getting Worse

KPMG’s 2025 report, “Total Experience: Redefining Excellence in the Age of Agentic AI,” was supposed to showcase its AI expertise. Instead, it became the latest example of Big Four firms publishing AI-generated content they apparently never checked, also known as “vibe citations.”

GPTZero, a platform originally built to help teachers detect AI-generated text, analyzed KPMG’s report and found out of 45 citations, only five were accurate. Twenty-eight pointed to real sources but had made-up details. Twelve were too vague to verify. At least 16 were complete hallucinations. The tool rated the report 89% flawed.

The fake details weren’t subtle. KPMG claimed an Austrian utility called Verbund was using AI for real-time household energy optimization. In reality, the citation was about Verbund investing in a startup that might do this someday. They said Emirates airline had a chatbot named “Sara” that could change flights. Sara was actually a robot assistant from 2023 with no flight-change capability. The biggest gaffe was claiming East Japan Railway was using AI agents in 2019, before this type of AI even existed commercially.

UBS, NHS Greater Manchester, and Transport for London all said KPMG’s claims about their use of AI were “completely false or misleading.”

“We have to create a database and just track these because the Big Four just keeps doing it over and over again,” David said, noting similar recent incidents at EY and Deloitte.

The irony is KPMG’s website features an article titled “Essential Elements of Responsible AI: How Solid Guardrails Can Help You Scale AI Faster.” They’re selling AI expertise while failing at basic fact-checking.

David’s sarcastic take nailed it. “The only way I could think this could work is if the Big Four can go to the Fortune 500 and be like, ‘Look, we know all the mistakes that can be made. Now listen to us because we know what not to do.’”

How to Actually Use AI: A Real-World Success Story

While KPMG was publishing fantasy case studies, Blake was using AI to do real client work and showing what responsible AI use looks like.

He needed to complete two years of write-up work for a service business: 2,200 transactions across nine accounts, with source documents in a messy mix of PDFs and CSVs from different banks. In the old desktop days, this would have taken days of manual entry. Even with cloud accounting, it would take many hours of importing and coding.

Using Claude Cowork, he finished everything in about four hours, including gathering documents.

His approach was smart and deliberate. He pointed Claude at folders of bank statements and had it extract all transactions into Xero-compatible import files. It did OCR on PDFs, merged CSVs, and organized everything by account. Then he gave Claude the prior year’s general ledger and asked it to categorize transactions, but with a key addition: a confidence score for each categorization.

“I could open that up, sort by that score, and look at the transactions that are less than 90%,” Oliver explained. Instead of reviewing 2,200 items, he focused on exceptions.

The results were impressive. Claude missed just six transactions out of 2,200, and two of those were due to credit card statement date issues, not AI error. When a $5,000 clearing account discrepancy appeared, Claude opened Xero in a browser, analyzed the details, and identified the problems itself. One was a returned payroll miscoded to transfers. The other was more complex: undiscovered transfers to a business line of credit. Claude suggested this possibility, Oliver confirmed by pulling statements, and Claude then created the loan account, separated principal from interest, and fixed everything. That kind of discrepancy usually requires hours of investigation.

“I didn’t just say, ‘Here’s the GL detail, here are the transactions, go code them all and enter them into Xero,” Oliver emphasized. “I wanted to review it first, and I caught significant stuff.”

This capability is becoming more accessible. Microsoft’s Copilot Cowork is now available, with over half of Fortune 500 companies trying it during preview. Microsoft says it’s 30-40% cheaper per prompt than Claude, and since most accounting firms use Microsoft 365, it might already be on your computer.

Not everyone’s getting it right, though. David shared his frustration with QuickBooks AI. When he uploaded a PDF containing 12 monthly bills, QuickBooks mashed them into a single bill with line items from each invoice. No questions asked.

“It should say, ‘Hey, I noticed there are 25 bills in here. Do you want one bill or 25 separate bills?’ And I would just answer,” David said, comparing it to AI coding tools that ask before acting.

The Market Is Already Picking Winners and Losers

Meanwhile, CPA firms are seeing interesting pricing patterns. According to CPA Trendlines, overall pricing is up 4.2% year-over-year, reversing last year’s decline. But looking more closely at the breakdown, tax prep and planning jumped by nearly 8%. Advisory work rose over 6%. Audit only increased by 2.3%.

Clearly, clients will pay more for services requiring human expertise and judgment. Tax planning and advisory command the biggest premiums. More routine, standardized work, like audit, lags behind.

“Clients are willing to pay for tax planning advisory, for the human in the loop to make sure that the numbers are right,” Oliver said. “AI isn’t putting pressure on those fees at this point. And I don’t expect it to.”

The Real Divide: Verification vs. Vibes

The stories from this episode are different views of the same shift. KPMG publishes an AI report that’s 89% wrong while a solo practitioner uses AI to finish two years of work in an afternoon with near-perfect accuracy. CPA firms raise tax planning fees by 8% because clients value human judgment.

AI compresses the value of routine, unverified work while amplifying the premium on carefully applied expertise.

The divide in professional services is between those who verify and those who just publish. Between practitioners who build workflows with confidence scores and exception review, and firms that let AI-generated content sail through with fake citations. Between organizations that treat AI as a force multiplier for human expertise and those that use it to substitute for expertise they never had.

The practical takeaway is to learn the tools, whether that’s Claude Cowork, Copilot Cowork, or whatever comes next. But build human checkpoints into every workflow. Use confidence scoring. Review exceptions. Don’t set it and forget it. The productivity gains are potentially five to ten times traditional methods, but they disappear the moment you skip verification.

The market is already pricing this reality. Clients pay more for advisory, planning, and the assurance that a qualified human reviewed the work. Firms and practitioners who master this balance will command premiums. Those who don’t will find themselves on the wrong side of a restructuring that’s happening right now.

To hear Blake Oliver’s complete breakdown of his AI workflow, David’s full critique of accounting software AI, and more details on KPMG’s “vibe citation” disaster, listen to episode 493 of The Accounting Podcast.

AI Models Now Outperform Human Bookkeepers and One Controller Proves a Finance Team of One Actually Works

Earmark Team · July 8, 2026 ·

A controller at a SaaS company that processes $50 million a month through its marketplace went on a two-week vacation. When he returned, his AI agents had already coded, categorized, approved, and synced 2,000 transactions. He reviewed just 67 (about 3%) by hand, and the entire cleanup took 30 minutes.

James Agius, Financial Controller at Skool, described his actual workflow on a recent episode of The Accounting Podcast. And it landed alongside benchmark data proving that, for the first time, off-the-shelf AI models from OpenAI, Anthropic, and Google are outperforming human accountants at basic bookkeeping tasks.

Hosts Blake Oliver and David Leary unpacked a series of developments that signal a genuine turning point for accounting. New studies from Digits and Ramp put hard numbers on AI’s bookkeeping abilities. A venture-backed startup led by a former PCAOB board member is building an AI-first audit firm. And KPMG’s entire US management committee flies to Silicon Valley every five to six weeks to meet with startups it views as potential threats.

But AI isn’t arriving to replace a surplus of accountants. It’s showing up amid a talent crisis that has more than tripled the number of unfilled accounting roles in a single year.

The Numbers Don’t Lie: AI Now Matches Human Bookkeepers

For years, the accounting profession has heard promises about AI. Now there’s data to back them up.

Digits just released the fourth version of its benchmark study, and CEO Jeff Seibert shared the results in an interview with David, which is featured on the episode. The test included categorizing over 2,000 transactions across multiple businesses into the correct chart of accounts. They tested all the major AI models (OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini) against outsourced human accountants.

“All of the major model providers have, for the first time, beaten real, outsourced human accountants at bookkeeping tasks,” Jeff told David. The humans scored about 79% accuracy. The AI models came in between 79.4% and 80.7%. The margin is small (about 1.6%), but the direction is clear.

Before anyone dismisses 79% as a low bar, Jeff offered important context. That’s actually typical for outsourced accountants who understand general accounting principles but don’t know the specific business. “They don’t know anything about that business or its industry, supply chain, geography, or customer base,” he explained. That missing context accounts for the 20% error rate.

What’s striking is how similar all the models performed. They’re all within three percentage points of each other. As David put it, basic transaction categorization “is kind of a commodity now.” It’s something everyone will essentially get for free from these models right out of the box.

But purpose-built systems go much further. Digits’ own AI, which learns from each business’s transaction history and can’t hallucinate by design, hits 97.8% accuracy. “Digits mimics the knowledge of a dedicated accountant who you’ve worked with for a number of years,” Jeff said.

The picture changes when you look at more complex work. Ramp tested its new Stack platform on 237 accounting tasks across eight synthetic businesses for categorization and financial close work. Its system scored 65.8%, beating the raw models but well short of perfect. This matches what most accountants experience. AI is great at pattern recognition but still struggles with judgment-heavy tasks.

AI still falls short in complex accruals, according to Jeff. Journal entries, fixed asset schedules, and prepaid expenses are the remaining frontier. Digits responded by launching automated accrual schedules where the AI identifies potential prepaids or fixed assets, drafts the schedule, and the accountant approves it.

Jeff drew an interesting parallel. At his tech company, engineers went from zero AI use to 100% in a single quarter. Jeff himself hasn’t written code since December, despite coding being his passion since age 12. “We have not fired our software engineers,” he said. “They are still critical, but the day to day has changed completely. Instead of them writing the code, they’re guiding the agents.”

One Controller, Zero Staff, $50 Million in Monthly Transactions

James Agius proves what these benchmarks mean in practice. He’s the financial controller at Skool, a SaaS company running online educational communities. The company handles over $5 million in monthly spend with nearly $50 million flowing through its marketplace each month.

James is also the company’s entire finance department. The company doesn’t have any staff accountants, AP clerks, or analysts. It’s just him and seven specialized AI agents, plus an eighth admin agent that checks the others’ work and enforces controls.

When Agius took two weeks off, those 2,000 transactions piled up. His automations handled almost everything, from coding, categorizing and approving to syncing to the ERP. When he returned, just 67 transactions needed human judgment. The cleanup took 30 minutes.

“His job changed from doing the work to reviewing the work,” Blake explained on the podcast. That shift freed Agius for forecasting, cash management, and strategy. It’s the work finance leaders always say they want to do but rarely have time for.

The timing couldn’t be more ironic. Just as AI enables one person to run an entire finance function, the profession can’t find enough people to fill open roles.

A Personiv study cited in Accounting Today found that the number of unfilled accounting and finance positions per company jumped from 5 to 17 in a single year, more than tripling. Eighty-four percent of finance and accounting leaders say there’s a talent shortage. The hardest role to fill is the senior accountant role, cited by 43% of respondents.

The drivers aren’t mysterious. The profession has talked for years about how 75% of CPAs were approaching retirement. “Well, now they’re doing it,” Blake said. And the pipeline is thin because staff accountants have been leaving after just a few years.

As David pointed out, senior accountants are exactly the people who would manage AI agents, so the talent shortage and the AI transition are colliding at the worst possible moment.

Firms are responding by racing to adopt AI. Sixty-three percent of leaders use AI to ease hiring pressure, up from 23% last year. For example, Bennett Thrasher moved talent acquisition from HR to the growth function, treating recruiting as strategically as business development. “The human labor becomes more valuable because it’s augmented,” Blake noted.

The Race to Reinvent

The competitive landscape is shifting as fast as technology. New entrants and incumbents alike are making moves that suggest they see this transformation as irreversible.

Christina Ho, former PCAOB board member and past podcast guest, joined Oath, a venture-backed firm building an AI-native audit practice from scratch. No legacy systems or technical debt. It’s AI-first from day one. They raised $6.6 million in seed funding and aim to automate 80% of audit work by 2030.

Oath plans to connect directly to clients’ accounting systems for continuous verification rather than year-end evidence gathering. CEO Lucas Ward emphasized audit remains “a human accountability function” even as machines handle verification. They’re recruiting “accounting engineers,” hybrid roles combining accounting expertise with computer science skills.

The Big Four are taking notice. KPMG’s US CEO now takes the entire management committee to Silicon Valley every five to six weeks, meeting with venture firms like Andreessen Horowitz and Bessemer to identify potential disruptors. They’re open to partnerships or investments, anything to avoid being blindsided.

On the platform side, Ramp’s new Stack product shows where AI agents might actually live in the workflow. Stack connects to existing tools like QuickBooks and accepts plain-language instructions, like “This client allocates revenue by location, not department. Split it across six cost centers.”

As Blake observed, “The GL is not the best place for agents to live. You want the agents at the point of the transaction.” Ramp already sits at the point of spend, giving its agents rich context about each business. The market agrees. Ramp just raised $750 million at a $44 billion valuation.

Not every AI adoption strategy works, though. KPMG rolled out a dashboard requiring employees to use AI for roughly 75% of their working time. Predictably, employees immediately gamed it. They had AI summarize emails they’d already read or generate random drawings — anything to hit targets. Blake called it “token maxxing,” comparing it to padding billable hours. Amazon shut down a similar program after seeing the same behavior.

What Humans Still Own

Where does human value go when AI handles the routine work? Jeff identified three things AI can’t replace.

  1. Judgment. “AI goes off in weird directions,” he said. Experienced professionals must guide it through ambiguous calls.
  2. Trust. “The AI will tell you anything you want. You can never trust AI.”
  3. Accountability. “It’s never going to be liable for the numbers it gives you. What are you going to do, sue your AI?”

These are the differentiators for accountants who want to stay relevant as machines take over the rest.

All of the evidence from this episode points to AI crossing the competence threshold for basic bookkeeping and advancing toward complex tasks. One controller already runs a $50 million operation solo. Yet unfilled roles have tripled. Senior accountants are impossible to find. The retirement wave is here, and the pipeline is thin.

To thrive, you need to bring what AI can’t: judgment, trust, and accountability. The transition is here.

Listen to the full episode for the rest of Jeff’s interview, details on KPMG Australia’s whistleblower scandal fallout, and a discussion of the IRS leadership vacuum.

Accountants Rush to Adopt AI While Ignoring the Security Risks That Come With It

Earmark Team · June 19, 2026 ·

Nearly nine out of ten accountants using AI report positive returns. But another statistic is more troubling. Over half of accounting firms have experienced data breaches recently, yet fewer than half have guidelines for how AI handles sensitive financial data. The productivity gains are real, but so are the risks we’re ignoring.

Blake Oliver, host of the Earmark Podcast, recently sat down with David Jani, Senior Content Analyst at Capterra, to unpack Capterra’s 2026 Accounting Software Trends report. The survey of 500 U.S. accounting managers shows the profession has moved beyond testing AI and into territory where the gap between adoption speed and security readiness is becoming dangerous.

 

The Productivity Gains Are Real (With a Catch)

AI in accounting has crossed from experiment to standard practice. More than half of accountants now use AI in their accounting software, and it appears across all company sizes, not just enterprises with big tech budgets. As David noted, “We’ve gone beyond the point of it being companies testing the water with this stuff.”

The most common uses for AI are chatbots and AI assistants, followed by data entry automation and fraud detection. AI is also making headway in predictive analytics, cash flow forecasting, smart invoicing, and bank reconciliation. David described it as “a coalescence around analytics and process-driven tasks.”

The 89% positive ROI figure comes from two main benefits. Half of respondents cite productivity gains, and nearly as many report reduced errors. So firms see real time savings and quality improvements.

But 48% of accountants manually check every single AI output. Not spot-checking, but checking everything. And about a third catch errors in their AI outputs more than half the time.

How do you square 89% positive ROI with error rates that high? David’s practical take is AI is “creating some gains in some areas, creating some extra work in others,” but the net result stays positive. Even when you add review time, firms come out ahead. But he cautioned, “It’s important that businesses still keep a close eye on the ROI of these situations and confirm it is delivering those gains.”

Meanwhile, plenty of work remains manual. More than half of respondents still handle financial reporting through spreadsheets or manual processes. Accounts payable and receivable, billing, invoicing, and payroll are all heavily manual. And yes, 51% of accountants still use Excel or Google Sheets for financial data. As Blake observed, spreadsheets have survived 40 years and aren’t going anywhere soon.

The Security Gap No One’s Taking Seriously

While firms celebrate productivity wins, the security picture is alarming, and almost nobody seems concerned enough to act.

Consider 52% of accounting managers surveyed have experienced a data breach in the last two years. That’s more than half. While David doesn’t have data linking these directly to AI, what he found about AI and sensitive data should worry every firm leader.

“Most companies don’t have clear guidelines on how they use AI tools with sensitive data,” David revealed. Fewer than half (49%) have guidelines for employee and payroll information. Coverage of bank reconciliation and customer billing data is even lower.

The perception gap is striking. Nearly half view AI cybersecurity risk as “minor,” another 12% as “insignificant,” and only 3% as “critical.” This might be “why so many people don’t have guidelines. Unfortunately, they just don’t perceive the risks at play,” David said.

Blake painted a scenario that’s probably happening now. Someone uploads payroll reports into free ChatGPT, where the terms of service may allow the vendor to train on that data. “We really need to step up,” he said.

The risks go deeper. Blake raised the issue of prompt injection, which involves hidden text in documents that manipulates AI agents into leaking data or changing payment information. It’s sophisticated and hard to defend against. As David acknowledged, “It’s a very new and rather sophisticated way of extracting information from a company. We still don’t really know enough about it.”

David didn’t sugarcoat his advice. “Guidelines around this don’t seem like much, and obviously, everyone is rushing to get AI tools. But it’s a huge risk factor we need to address.”

AI Is Raising the Bar

If AI makes accountants more productive, you’d expect fewer jobs. But the data tells a different story, and it came as a surprise to David.

“Despite a lot of reports predicting the end of accountants, it’s not really what we found,” he said. Companies are adopting AI, but “it’s not necessarily affecting hiring decisions in the same way. A lot of companies are actually more focused on upskilling.”

Blake offered a historical perspective. The same panic hit when VisiCalc and Excel arrived 40 years ago, yet accounting jobs grew. When cloud computing transformed the industry, client accounting services didn’t shrink. Instead, it’s grown year over year for a decade.

The talent shortage persists, with 73% of firms reporting trouble with retention and hiring. The hardest roles to fill are mid-career positions. About a third struggle to find financial analysts, with specialized accountants (tax and cost accounting) close behind.

The paradox is AI actually increases the need for experienced professionals. Someone must review those AI outputs that are wrong half the time. Someone must understand the AI well enough to catch mistakes. Someone must manage the security implications. All that requires judgment and experience, and that’s exactly what’s hardest to hire right now.

The data backs this up. Upskilling existing staff is the dominant strategy at 40%, double the 21% using AI to fill staffing gaps. Traditional hiring sits at 31%, with graduate programs at 23%. The profession is betting on people, not automation, to solve its workforce problem.

Looking Ahead: Challenges and Choices

What keeps accountants up at night? Budgeting and forecasting in an uncertain economy tops the list, followed by figuring out how to use AI effectively. As David put it, firms are trying to understand AI “in a way that makes sense.”

David has specific advice for where firms should invest their AI dollars. Map investments to your particular needs rather than chasing trends. For general guidance, he pointed to data entry automation and predictive modeling tools, especially cash flow forecasting and analysis dashboards, as areas delivering the most value.

When asked to predict what might change by the 2027 survey, David hopes to see more firms with updated security guidelines. “I think as these tools become more mature, more people will update their guidelines, especially for handling sensitive data like payroll and cash flow,” he said.

A Gap Between Speed and Safety

The Capterra data shows the profession is getting AI both right and dangerously wrong. The 89% positive ROI is genuine. Firms are saving time and reducing errors, even after factoring in review burdens. But that headline obscures the fact that over half have experienced breaches, fewer than half have AI data guidelines, and most dismiss the cybersecurity risk as minor, even with threats like prompt injection that the profession barely understands.

AI isn’t solving the talent crisis either. It’s raising the bar for what accountants need to know, making experienced reviewers more critical while the mid-career talent shortage intensifies.

Firms must build guardrails, write guidelines, and invest in upskilling their people to successfully work alongside technology that’s powerful but imperfect.

Want to dig deeper into these findings? Listen to Blake’s full conversation with David on the Earmark Podcast, and earn free NASBA CPE while you’re at it. 

Why the Most Profitable Accounting Firms of the Future Might Have No Employees at All

Earmark Team · May 31, 2026 ·

One guy. Zero employees. He spends 70% of his budget on technology.

Sam Leon runs The Millennial CPA in Richmond, Virginia, where AI does most of the tax prep work while he reviews and signs off. He just landed on Accounting Today’s 2026 Best Firms for Technology list, not by building a bigger team, but by proving you don’t need one at all.

Meanwhile, KPMG is shutting down its entire federal government audit practice after losing a $60 million Pentagon contract. They’re reassigning 450 employees and cutting another 400 from advisory. The old work is shrinking. The new AI, cyber, and forensics work is growing fast.

On this week’s episode of The Accounting Podcast, hosts Blake Oliver and David Leary discussed what these stories mean for the profession. They explored how AI is making the “firm of one” model possible, tested the new QuickBooks and Xero connections to Claude, and wrestled with a big question: If AI can replace so much labor, what happens to the people and the economy that depend on them?

 

The Solo Practitioner Who Turned AI Into His Staff

Sam Leon took a simple but radical approach to building his firm. AI handles the grunt work of tax return preparation, including creating workpapers, doing year-over-year comparisons, and mapping QuickBooks data to tax forms. He reviews everything and signs the returns. That’s it.

“I see AI as coming together to be a total tax preparer, and whoever signs the returns is the reviewer,” Sam told Accounting Today. He thinks of the AI as his junior preparer while he’s the senior reviewer.

The time savings are wild. Work that would take a human three to five hours, such as creating detailed tax workpapers from QuickBooks exports, takes AI five minutes. And Sam has no plans to hire. “I won’t hire until I hit a wall with my AI preparers and AI workflow managers,” he said.

Blake validated this approach based on his own daily use of Claude Cowork. “To do it as an individual is totally possible,” Blake said. “And so I expect we’ll see more of these firms of one, and you’ll be able to scale up and make a lot of money, because you don’t have to hire employees.”

David connected this to a broader trend he calls the “minimum viable-sized company.” The old playbook was simple: raise money, hire people, grow. “You don’t need that anymore,” David said. “The future winners are going to be small, highly efficient teams with strong strategic clarity. Not large organizations.”

Of course, there are questions. How much revenue does Sam actually make? How does he handle client communication and invoicing? Is he a software engineer or just really good at prompting AI? Blake and David want to get him on the show to find out.

The Tools Are Getting Easier, But Still Have Limits

Right now, Sam’s model works because he’s willing to configure AI tools himself. But that’s changing fast as AI gets built directly into the software firms already use.

Canopy just launched an AI “Coworker” feature across its practice management platform. David was initially skeptical when he saw the sample prompts, which included things like “list all my clients,” that you could see with one click anyway. But Blake highlighted the real value: scope-creep detection that analyzes your billing and emails to spot when you’re doing more work than you’re charging for, automatic workflow updates when disaster declarations change filing deadlines, and meeting notes that automatically create tasks with assignees and due dates.

“These AI agents in practice management are going to be hugely important,” Blake said. “They’re going to make practice management ten times more valuable.”

The big platforms are also opening up to AI. Intuit just released connectors linking Claude to QuickBooks, TurboTax, Mailchimp, and Credit Karma. Xero has one too. But Blake tested both and found them pretty limited. You can pull basic reports and import transactions, but you can’t actually analyze transaction-level data yet.

“If they don’t make connectors more robust, they’re kind of useless,” Blake said. Still, the direction is clear. As David put it, “Claude becomes like your central gear that’s spinning data out to these other spots.”

KPMG’s Federal Exit Shows Where the Profession Is Heading

While solo practitioners are using AI to do more with less, KPMG is learning what happens when you can’t adapt fast enough.

The firm just lost its contract to audit the U.S. Army. It was a $60 million annual deal they’d had for over a decade. The Army has never passed an audit, and now the Pentagon wants to restructure the whole approach. KPMG responded by shutting down the entire federal audit practice and reassigning 450 people.

But that’s not all. They’re also cutting 4% of U.S. advisory staff, or about 400 people, mostly in regulatory risk and financial services consulting. These cuts continue a pattern that started in 2023.

Instead, KPMG is investing in AI, cyber, forensic services, and managed services. Traditional audit work is shrinking, while tech-enabled services are growing.

The Big Risk 

If companies use AI mainly to eliminate jobs, who’s going to buy their products?

Christine Kuglin and Bright Ikwetie wrote about this in Accounting Today, calling it the “AI efficiency paradox.” Businesses get more efficient by replacing workers with AI, but they’re also eliminating the incomes that drive consumer spending. It’s a potential death spiral. Less spending means less revenue, more layoffs, and more AI. Rinse and repeat.

The economic data is confusing. Weekly jobless claims just hit 189,000, the lowest in more than five decades. Yet manufacturing employment is down 88,000 jobs year over year. How can unemployment be so low when we keep hearing about layoffs?

“Is this just lagging?” Blake wondered. “Are these workers just finding jobs in other parts of the economy or maybe working for themselves?”

For accounting specifically, the demand for talent remains strong. Intuit analyzed LinkedIn data and found that both tax and accounting roles are “very hard to hire” nationally. They’re actively recruiting with flexible, remote-first benefits, which is exactly what the Big Four firms are cutting.

What This Means for Your Firm

The lesson from Sam is that one person can now deliver what used to require a team. The same principle scales up. A small firm can compete with a large one, and a mid-size firm can offer enterprise-level services.

But don’t use AI just to do the same work with fewer people. Use it to do work you couldn’t do before. As Blake put it, “The growth opportunity in accounting is advisory-type services. And AI paired with expert humans is just so incredibly powerful for doing advisory work like fractional CFO services, M&A advisory, and cost segregation studies.”

David sees another opportunity in helping clients “vibe code” custom apps instead of stacking expensive SaaS subscriptions. “I am confident that accountants could vibe code,” he said. “The old stack of app stacking is going to go away. You’re just going to help your client build the app they need.”

The tools are here. The demand is there. The question is whether firms will use AI to shrink or to grow. Firms that use AI to expand what’s possible rather than just cut costs will set the terms for everyone else.

Want to hear Blake test the QuickBooks-Claude connector live? Curious about how much Sam actually makes? Listen to the full episode of The Accounting Podcast for all the details, plus discussions on new IRS whistleblower rules, tariff refund lawsuits, and why procrastinating on AI adoption might actually pay off.

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