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Everyone’s a Builder Now, and That’s Exactly What Should Worry the Accounting Profession

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“One day, the board is going to ask the CEO, ‘I see you spent all that on tokens. What was the result?’ And they’re not gonna be able to answer it.” That’s David Leary on Episode 496 of The Accounting Podcast, and in one line he captures the tension running under nearly every story he and co-host Blake Oliver covered this week.

Here’s the big idea that ties all of this episode’s stories together is that, as AI lowers the barrier to building software, automating audits, and streamlining everything from month-end close to CPE reporting, the accounting profession’s real value shifts away from doing the work and toward overseeing it. That’s because every advance comes bundled with a hidden cost or risk. The real professionals know exactly where the practical controls, real costs, and actual risks live.

Xero Arms the “Builder Class,” but It’s Still Very Early Days

Xero turned 20 this year, and at Xerocon London the company used that milestone to lean into what CEO Sukhinder Cassidy calls a “builder class” movement. The builder class includes accountants, bookkeepers, and business users who can create automations and software-like tools without being traditional developers. As David noted, this connects directly to Xero’s recent developer-channel push, and its Is Everyone a Developer Now? YouTube channel that once seemed puzzling but now reads as strategy.

The centerpiece is XeroForce, an invite-only, alpha-stage, no-code AI agent builder. David compared it to Zapier because you connect your apps, describe a workflow in plain language (“every time an email like this comes in, pull the PDF attachment and post it as a bill”), and it builds the automation for you. It’s part of Xero’s broader play, alongside the new mid-market Xero Ultra product, to keep customers on its full stack as they grow rather than losing them to a Sage Intacct or Oracle NetSuite.

Since Xero started connecting Claude and other agents in January 2026, its API usage jumped 400%, and 2,000 customers connected Xero to Claude in the first 60 days after the announcement. That sounds impressive until you do the math. Against roughly 5 million Xero businesses, 2,000 is about 0.04%. “We’re so early still,” Blake said. 

David’s advice was blunt: “Don’t get FOMO, because the number of people actually doing it is so, so teeny, teeny, teeny.” He also flagged the missing “database layer.” Accountants can vibe-code an app, but there’s often nowhere for it to live and no easy way to host and maintain it. That’s the practical control problem hiding behind the promise.

Vibe Coding: Real Six-Figure Savings, Real Key-Person Risk

If Xero arms accountants to build, some firms aren’t waiting for a vendor at all. As one listener put it, “Small firms can now develop their own software for less than the cost of buying software.” For example, at HoganTaylor, Randy Nail’s team needed a financial reporting tool for a new audit method. A vendor quote came in around $200,000 a year. Instead, they built it themselves in Excel with AI. Blake says that’s the smart way to do it: build in a familiar tool you already own, not a standalone app “living somewhere on a server” you don’t fully understand. Mike DeKock of MJD Advisors went further, replacing $300,000-a-year audit software with a build in Claude and Retool for under $30,000 annually. Even Starbucks is chasing the same impulse at enterprise scale, trying to trim its roughly $400 million software spend by building more in-house.

The catch in DeKock’s case is that he’s the only one who knows how it works. That’s key-person risk. Blake and David have lived it. Earmark’s AI course generator runs on about 75 Zapier steps Blake built years ago, and when it broke recently, only Blake could fix it. It’s the same problem as the notoriously complex financial model that only one person understands, where everyone else is afraid to click the wrong cell.

David pushed back, and fairly. AI coding tools document their own work well. It includes comments in the code and plain-English summaries, so it may be less of a problem than it first appears. And vendors aren’t a guaranteed safety net either. He recounted a Streamyard support headache: “If I have to use an AI bot to get support for your product, I might as well just chat with a different AI bot and have it build me a replacement.” Still, Blake summarizes, “You’re saving money now, but you’re creating risk potentially in the future.” Build where you’re comfortable, and keep a backup.

Checkbox Compliance vs. Real Protection

That same tradeoff between what looks safe on paper and what actually holds up runs straight through this episode’s audit and security stories. MindBridge submitted formal comments urging the PCAOB to clarify how auditors should document, assess, and defend AI-assisted work, especially now that software can test an entire population of transactions instead of a sample. The existing standards were built around sampling. They simply don’t address risk scores, investigation thresholds, or what counts as “sufficient evidence” in full-population testing. Firms run the new AI-assisted procedures alongside the old manual tests because, as David put it, they “need the check box” to pass inspection.

Meanwhile, the PCAOB voted unanimously to seek comment on easing parts of QC 1000, the 2024 quality-control standard. The changes could remove the external quality-control function for firms that audit more than 100 issuers, and relieve registered firms that don’t actually perform PCAOB engagements. The hosts recognize the need to modernize but question whether simply rolling back standards is the answer. Blake framed the core issue as an inputs-based approach to regulation, not an outcomes-based one. “Having a system doesn’t necessarily mean that your audit is going to be quality.”

David’s parallel nailed it. A cybersecurity audit of 275 Australian accounting firms found 76% had no protection against email spoofing, yet nearly all almost certainly have a required written information security plan (WISP). “You don’t have to be secure,” David said. “You just have to have a plan.” Or, more pointedly, “You must spend time building this document about your security plan instead of investing that time and resources into actually being secure.”

The Token Problem: Usage-Based Pricing and a New Kind of Cost Accounting

If compliance is about knowing where the real controls live, the next challenge is knowing where the real costs live. AI is rewriting software economics, from predictable, per-user subscriptions to variable, usage-based token spend. Tools like Claude Cowork can do far more now, but they cost more too. You ask an agent to do one thing, and it chugs away in the background, burning tokens and blowing past your allotment fast.

A KPMG survey of over 2,000 senior leaders across 20 countries put numbers to the pain. Only 29% feel they understand operating costs as they scale enterprise AI. Roughly a third cite limited understanding of AI economics as a barrier to deploying agents. And nearly half of organizations have re-phased AI deployments when costs exceeded expected value. Blake noted this could be “the next great area of cost accounting,” measuring where tokens get burned and proving where they deliver results.

Part of the answer is matching the model to the task. Lower-cost, high-fidelity models were the fastest-growing influence on AI strategy, up seven points from the prior quarter. Think Claude Opus versus Sonnet versus Haiku. Pick the right one not just per workflow, but per step within a workflow. Emerging “routers” now automatically direct each task to the most cost-effective model because, as David said, “you don’t need to blow $100 for a $0.02 answer.”

The Accountability Layer

Pull the threads together, and the pattern is unmistakable. Xero’s builder class and XeroForce, vibe-coded software, AI-assisted audits, token economics. Every advance in this episode came paired with a counterweight. Adoption is still a rounding error. Custom builds create key-person risk. Modernized standards risk hollowing out real oversight. And usage-based pricing makes costs genuinely hard to forecast.

Even the episode’s feel-good story carries a caution. One listener used Claude Cowork to handle Florida’s tedious CPE reporting, consolidating roughly 80 certificates into an Excel list, entering them in the state portal, and even catching and fixing its own duplicate entries and combining PDFs into a single upload. It was a “relatively low-risk” win, done with his own data while he caught up on Severance. But David’s cautioned listeners to check the portal’s terms of service, since older, pre-AI site terms may prohibit bots. 

The need for judgment is the through-line. As AI collapses the barrier to building and automating, doing the work stops being where accountants create their edge. The durable value moves to oversight. You need to know where the practical controls, real costs, and actual risks live. The tension is always between innovation and accountability, and accountants are uniquely positioned as the accountability layer. So start experimenting, but build where you’re comfortable (Excel is fine), keep a backup for anyone building custom tools, and start measuring your AI spend now.

There’s plenty more in episode 496, including Lionel Messi’s roughly $28 million potential U.S. tax bill and FIFA’s tax-exempt status, the activist-investor fight over CBIZ’s acquisition strategy, and the full World Cup betting-tax breakdown. Listen to the whole back-and-forth on The Accounting Podcast, and don’t forget you can earn free NASBA CPE for the episode through Earmark.

Forty Percent of Workers Admit Faking Receipts With Company-Paid AI Tools

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Forty percent of U.S. workers admit to using AI to generate fake receipts for expense reports. Even more troubling is that 40% of those workers use AI tools their own companies paid for.

Blake Oliver and David Leary opened Episode 494 of The Accounting Podcast with these startling statistics from new surveys by AppZen and Emburse. David introduced a new term that’s emerged from this trend: “revenge spending,” in which employees who fear AI will replace their jobs turn the company’s own AI tools against it by submitting fraudulent expense reports.

“It’s similar to spam,” David explained. “AI and technology make it easier than ever for people to send you millions of spam messages. But then on your side, you’re using all these AI tools to detect the spam messages and move them to your trash.”

The numbers tell an interesting story. In just 14 months, AI-generated fake receipts went from virtually nonexistent to representing 70% of fraud flags in expense systems. These fake receipts average about $100 each, with a median of $32. Those deliberately small amounts are designed to slip under auto-approval thresholds.

NASBA Backs Down

The theme of shifting power dynamics became personal for Blake when he shared the resolution of Earmark’s standoff with the National Association of State Boards of Accountancy (NASBA).

Back in April, NASBA sent Blake a demand letter over comments he made at an AICPA conference. While demonstrating how to use AI to create CPE courses, Blake criticized NASBA’s methods as “backward” and called out the problems with current CPE practices, including webinar polling questions that serve as mere check-the-box exercises, attendees doing email during sessions, and people sleeping through in-person presentations.

NASBA’s letter directed Blake to “cease making any unfavorable, unprofessional, or inappropriate comments” about the organization, citing a sponsor agreement requiring programs to “reflect favorably on NASBA.”

Blake pushed back hard. “I felt that it was wrong, even unconstitutional, for an organization like the National Association of State Boards of Accountancy to tell a sponsor of CPE, a CPA, a professional educator, what they may and may not say about NASBA,” he explained to David.

In his response letter, Blake argued his comments were meant to improve CPE, not attack NASBA. He also asked for clarification on what exactly would constitute a violation, since terms like “unfavorable” weren’t defined in the agreement.

The resolution came in June when Amy Tongate, NASBA’s Director of Compliance Services, essentially backed down, writing, “NASBA welcomes constructive professional dialogue regarding continuing professional education. Based on your response and subsequent discussions, NASBA considers this matter resolved. No further action is required.”

Blake sees a deeper issue here. NASBA isn’t actually a regulator; the state boards are. NASBA was created as an administrator to handle licensure efficiently across all states. But it often acts like a regulator, which Blake argues oversteps its bounds.

“If a state board of accountancy tried to do what NASBA tried to do with that demand letter, that would be unconstitutional,” Blake said. “The question is whether or not the state boards can set up a private company, a nonprofit that then acts on their behalf and suppresses the speech of CPAs. And I would be willing to bet that they can’t.”

Big Firms Can’t Command Loyalty Anymore

While regulators discover the limits of their authority, big accounting firms are finding they can’t control their workforce as they once did.

A new academic study published in Contemporary Accounting Research with the dramatic title Losing Control: The Erosion of Disciplinary and Pastoral Power in Accounting Firms, reveals just how much has changed. Based on 31 interviews with Canadian auditors from 2021 to 2023, the research shows firms are struggling to shape employees into the traditional model of the committed, overworking auditor.

The numbers are striking. What the study calls “default auditors,” defined as people who enter under weaker selection standards and treat the job transactionally, are replacing the highly socialized, career-committed auditors of the past.

“The Big Four is becoming less of a cult,” David summarized bluntly.

The breakdown is happening on multiple fronts. Remote work disrupted the in-person observation that once normalized 80-hour weeks. When young auditors don’t see everyone else burning the midnight oil, logging off at a reasonable hour becomes much easier. The “we’re all in this together” busy-season rituals, like late-night pizza parties, matter less and less.

But employees aren’t just passively benefiting from remote work. They’re actively pushing back. According to the study, they’re setting firmer personal boundaries, prioritizing family and mental health, rejecting unpaid symbolic rewards, and openly comparing their compensation to that of partners and managers.

The partners and managers feel trapped. They’re taking on more work themselves, reviewing more because of lower work quality, and offering higher pay and more flexibility, but it’s not working. As Blake noted, “They are feeling more exhausted, underappreciated, unable to enforce the old standards and unable to design convincing new ones.”

This cultural breakdown makes the recent wave of private equity investments in accounting firms particularly puzzling. Eide Bailly just became the latest to take PE money: a majority stake from Reverence Capital valuing the firm at $1.8 billion, about 2.1 times revenue.

Looking at a chart of the top 30 U.S. firms, Blake and David counted that a majority now carry outside capital. Yet the hosts are skeptical these investments will pay off.

“I have not heard of a PE success story where PE came in and the company became this rah-rah great thing,” David said. “It gets worse from PE, right?”

“Are they really going to be able to turn it around and sell it for more?” Blake asked, pointing at the math problem.

David’s verdict was characteristically direct: “Put lipstick on that pig and sell it to somebody else.”

The AI Revolution Gives Power to Individuals

While institutions struggle to maintain control, individual practitioners gain capabilities that once required entire companies or expensive software.

The adoption numbers are explosive. According to Blue J and CPA.com’s latest survey, 60% of tax professionals now use AI for tax research at least weekly, up from just 33% a year ago. They use it for advisory projects (44%), tax planning (40%), and compliance research (39%).

“Where are the other 40% getting answers?” David wondered about those who are not using AI, noting that even Google searches now show AI answers first.

This surge in AI use prompted the IRS Advisory Council to issue its first-ever guidance on AI in tax practice. The guidelines don’t create new rules but clarify how existing standards apply. Most notably, practitioners can’t bill for time not actually spent, can’t charge manual rates for AI-assisted work, or double-bill for work done by both staff and software.

“This is the nail in the coffin of hourly billing,” Blake declared. If you use AI to cut your work time in half, you’re ethically obligated to pass those savings to the client.

The democratization goes even further. David highlighted Xero Developer’s new YouTube series, Is Everyone a Developer Now?, where the development team “vibe codes” working applications in real-time. In one episode, they built a functional month-end close tool in just an hour and fifteen minutes.

“Instead of chasing a small pool of developers to build apps, they basically have now opened up millions of accountants that could actually create apps,” David explained.

Blake shared his own example. He’d been procrastinating about converting Earmark’s books from a cash to an accrual basis because building the revenue recognition workpapers seemed overwhelming. Then he tried Claude.

“I just asked it what I needed,” Blake said. The AI walked him through methodology choices, downloaded sales reports from Apple and Google, and built a complete waterfall table that spread revenue across 12 months, plus reconciliation tabs and journal entries.

“This is the right template. This is the right format for me to have done this manually,” Blake marveled. “I don’t even know how many days it would have taken me to put this together.”

This shift in capabilities has venture-backed companies worried. Pilot, valued at $1.6 billion, just spun off its internal AI close platform as a standalone product. Another startup raised millions for similar technology. But as David pointed out, if you can “vibe code” these solutions in an afternoon, “is the app ecosystem the way it’s traditionally been just going away now?”

The Power Shift Is Just Beginning

These aren’t isolated stories; they’re all symptoms of the same fundamental change. Power is flowing away from institutions and into the hands of individuals.

Regulators like NASBA are discovering they can’t dictate what professionals say. Big firms can’t enforce the overwork culture that once defined public accounting. Private equity investors are betting billions on firms whose fundamental model is breaking down. And the same AI that helps Blake build sophisticated workpapers helps employees create fake receipts.

“It’s rules-driven innovation instead of customer-driven innovation,” David said about the institutional mindset that’s failing across the profession.

This shift brings opportunity and responsibility for accounting professionals. The tools that can build a revenue recognition system before lunch can just as easily fabricate an expense report. The capability is neutral; how the profession uses it isn’t.

Want to hear Blake’s complete walkthrough of building his rev rec workpaper, more details on the NASBA correspondence, and the hosts’ full analysis of these industry shifts? Listen to the complete Episode 494 of The Accounting Podcast. You can even earn free CPE credit through Earmark.

As Blake and David make clear, the redistribution of power in accounting is just getting started, and every practitioner needs to understand what it means for their future.

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

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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

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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

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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.