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Archives for April 2026

How the Vatican’s Blessing Helped Hide $1.3 Billion in Missing Money

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In June of 1982, a postal worker walking along the Thames in London noticed something hanging beneath Blackfriars Bridge. At first, he assumed it was construction equipment, like scaffolding or a tarp caught on a pipe. Looking closer, he realized it was a man, still wearing a suit, with bricks in his pockets and a rope around his neck. For a few days, nobody knew who he was. Then the name came out: Roberto Calvi. Suddenly, a lot of very powerful people were very interested in who was under that bridge.

That story opened a recent episode of the Oh My Fraud podcast. Host Caleb Newquist dug into one of the largest and strangest banking scandals of the 20th century, the collapse of Banco Ambrosiano and the unsolved death of the man they called “God’s Banker.”

In this story, institutional prestige became the most dangerous fraud enabler of all. When a bank’s credibility rests on religious authority, secret power networks, and cultural trust rather than transparent financials, $1.3 billion can vanish through circular offshore schemes while everyone assumes someone else must have checked the books.

How a Methodical Banker Became “God’s Banker”

Roberto Calvi wasn’t supposed to be a mysterious figure. Born in Milan in 1920 to a working-class family, his early life followed the same path as many of his generation: World War II, military service, and rebuilding from the rubble. He joined Banco Ambrosiano in the late 1940s as an entry-level hire. By all accounts, he was exactly what institutions want: diligent, methodical, and reliable. As Caleb puts it, he was “the kind of person institutions tend to reward because they don’t rock the boat.”

And for decades, he didn’t rock it. Roberto climbed steadily, and was promoted to general manager by 1971, and chairman by 1975.

Banco Ambrosiano was one of Italy’s largest private banks, with deep ties to Catholic financial networks. Italy’s banking has always carried layers of political influence, regional loyalty, and religious connections. Banco Ambrosiano sat comfortably within that ecosystem.

The most important relationship was with the Vatican Bank, officially the Institute for the Works of Religion, which, as Caleb notes, “sounds less like a financial institution and more like a retreat center, but it functions as a bank.” It handles investments, transfers, and assets for church operations worldwide. Banco Ambrosiano became one of its primary external banking partners.

That partnership was worth more than money; it was reputational gold. “If a bank is trusted to handle the Vatican’s money, then a lot of people are going to assume it’s safe,” Caleb explains. And that assumption is where the trouble starts.

The financial press started calling Roberto “God’s Banker.” It was shorthand for “this guy has some serious connections.” But the nickname also fused the bank’s identity with one of the most trusted institutions on the planet. Investors were buying into the idea of a bank backstopped by centuries of religious authority.

“Where there’s a very deep sense of trust, there’s often a lesser degree of scrutiny,” Caleb points out. “Not explicitly, but psychologically.” The reputation became the product. When reputation does the heavy lifting, the actual financial structures don’t get tested nearly as hard.

During the 1970s, the bank genuinely grew through international expansion, complex financial products, and global operations. Some of that growth was legitimate. But growth also meant operating in jurisdictions where oversight was, as Caleb puts it, “loose.”

Italian regulators raised eyebrows more than once at the complex corporate structures, foreign subsidiaries that were hard to track, and financial guarantees that weren’t always transparent. Individually, each could be explained. Collectively, they formed a pattern. But the God’s Banker halo did its job of absorbing questions that might have demanded harder answers.

The Machinery of Fraud: Circular Money and Comfort Letters from God

Over a billion dollars doesn’t go missing all at once. It happens gradually, through structures so layered that by the time anyone understands them, the money’s already gone.

By the mid-1970s, Banco Ambrosiano was expanding aggressively into international markets. Foreign subsidiaries multiplied across Luxembourg, the Bahamas, and Panama, where regulatory oversight was minimal. Some entities served obvious purposes, such as international lending, currency transfers, or supporting clients abroad. But others had extremely vague business descriptions and corporate structures so layered that tracing ownership took real effort.

According to Caleb, the core scheme worked like this: “Some of those offshore companies weren’t really operating like independent businesses at all. They borrowed money from the bank, made deposits back into related entities, issued guarantees to support loans made to other subsidiaries in the same network. Money moving in a loop that created the appearance of capital strength without much actually underneath it.”

Circular financing isn’t automatically illegal. Multinationals do inter-company lending all the time. “The problem starts when those underlying assets aren’t as solid as everyone assumes, because then what looks like strength is really just confidence shifting from company to company,” Caleb explains.

His metaphor nails it: “It was financial scaffolding. Scaffolding works great while the building’s going up. Less great when someone leans on it expecting a finished structure.”

The Vatican Bank’s letters of patronage kept people from leaning too hard. These were essentially comfort letters, or assurances that were, as Caleb jokes, “about as secure as the Lord’s blessing.” But banks and counterparties treated them as something stronger than they technically were. If the Vatican says it stands behind something, who’s going to push back?

The ecosystem around Banco Ambrosiano was getting darker. Michele Sindona, another Vatican-linked Italian financier, had already blazed this trail. His banking empire collapsed in the mid-1970s through similar aggressive financing and opaque offshore deals. He was convicted of fraud in the U.S., later convicted of ordering a murder, and died in prison in 1986 after drinking cyanide-laced coffee.

Then there was Propaganda Due (P2) officially a Masonic lodge. When Italian authorities raided it in 1981, the membership list included Italian cabinet ministers, military leaders, intelligence officials, judges, and media executives. Roberto’s name was there, too. P2 members called themselves “Frati Neri,” Black Friars. Yes, the grim coincidence: Roberto was found under Blackfriars Bridge.

“Membership alone doesn’t prove wrongdoing,” Caleb notes, “but it suggests proximity to power, and in finance, proximity to power can smooth scrutiny, accelerate deals, and sometimes delay uncomfortable questions.”

Add another red flag. In 1981, Roberto was convicted in Italy for illegally exporting currency. He received a suspended sentence but it was still a criminal conviction tied to financial conduct. “Prior financial misconduct usually justifies closer monitoring, not looser scrutiny,” Caleb observes. Instead, institutional trust filled the gaps.

By early 1982, roughly $1.3 billion was unaccounted for. That’s in early 1980s dollars. Investigators later found a 2,400-pound safe in a secret office. When they cracked it open, they found a handwritten list of gold and silver items. No actual gold or silver. Just the list. “A pretty fitting metaphor for the whole operation,” Caleb says.

On June 5, 1982, Roberto wrote to Pope John Paul II warning the bank’s collapse would “provoke a catastrophe of unimaginable proportions in which the church would suffer the gravest damage.” On June 10, he fled Italy with a fake passport under the name Gian Roberto Calvini, having shaved off his mustache. Communication became sporadic, then stopped.

Death Under Blackfriars Bridge and the Lessons Left Hanging

The day before Roberto’s body was found, Graziella Corrocher, Roberto’s 55-year-old secretary, jumped from the fifth floor of the bank’s headquarters. She left a note that said, “May Roberto be double cursed for the damage he has caused to the bank and all of its employees.”

“That doesn’t sound like someone caught up in financial technicalities,” Caleb observes. “That sounds like betrayal.”

As for Roberto, the path from “dead banker” to “unsolved murder” took decades. The initial ruling was suicide. A 1983 inquest returned an open verdict. In 1998, authorities exhumed his body. Forensic analysis found neck injuries inconsistent with hanging and no traces of scaffolding paint, rust, brick dust, or limestone under his fingernails, evidence you’d expect on someone who climbed there himself. By 2002, Italian courts ruled it a homicide.

In 2007, five defendants including alleged Mafia figures went on trial. After twenty months of testimony, hundreds of witnesses, and mountains of forensic evidence, the judge threw out all charges for insufficient evidence. The public prosecutor said, “Roberto has been murdered for the second time.”

After negotiation and public pressure, the Vatican contributed between $224 and $250 million toward creditor settlements. The church framed it as a moral gesture, not an admission of legal liability. Caleb describes it as “the financial equivalent of saying we didn’t do anything wrong, but here’s some money anyway.”

What Accounting Professionals Should Take From This

Caleb closes with five key lessons from the wreckage:

  • Institutional trust is not a control. A respected name doesn’t guarantee sound financial structures. “A good reputation can chip away at skepticism, and reduced skepticism is exactly where fraud tends to thrive. People assume that someone must have checked.”
  • Complexity is not the same as sophistication. “Sometimes complexity is necessary, but it’s also camouflage.” If understanding the structure takes longer than anyone’s willing to spend asking questions, that’s probably a red flag.
  • Prior misconduct deserves attention. Roberto’s 1981 conviction didn’t doom the bank, but it should have triggered closer monitoring. Instead, institutional trust papered over a conviction that should have triggered alarm bells.
  • Liquidity crises expose accounting illusions extremely quickly. “A lot of frauds don’t collapse because someone discovers them. They collapse because cash gets really tight.” When creditors want repayment instead of extending credit, reality tends to win.
  • Fraud rarely happens in isolation. “This wasn’t just one banker making bad decisions. It was a network.” Most frauds reveal a rotten system, not just one bad apple.

The Banco Ambrosiano scandal is ultimately about how prestige substitutes for scrutiny. Four decades later, we still don’t know who killed Roberto Calvi. We do know what killed Banco Ambrosiano: a system where reputation did the work that controls were supposed to do.

Every era has its version of institutions where reputations function as a get-out-of-scrutiny-free card. The vehicles change, but the dynamic stays the same. When trust replaces verification, fraud finds room to grow.

Listen to the complete episode of Oh My Fraud for the full story, including the prequel villain who died from prison coffee, a safe full of nothing but lists, and a mustache shave that fooled no one.

And remember Caleb’s parting advice: if the chairman of your bank ends up hanging under a bridge named Blackfriars, you’re probably not having a normal quarter.

The Billable Hour Is Broken and Every Firm Leader Knows It. So Why Won’t Anyone Kill It?

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Richard Lynch can list every reason the billable hour is broken. It undervalues experienced professionals, creates perverse incentives, burns people out, and reduces human beings to time-tracked units. But without a hint of irony, he admits that Sikich, one of the largest CPA firms in the country, still tracks hours “on a religious basis.”

That contradiction tells you everything about where the accounting profession stands right now.

On a recent episode of the Earmark Podcast, host Blake Oliver sat down with Richard, a managing principal at Sikich with over 25 years in public accounting. They had an honest conversation about where things actually stand. Not where the conference keynotes say they stand or where vendor demos suggest they stand, but where they actually stand inside accounting and advisory firms, at the level where someone still has to fill out a timesheet at 9 p.m. on a Tuesday.

The takeaway is that the profession’s transformation is stalling because firms can’t let go of the operational scaffolding they’ve built around the billable hour.

The Super Accountant Vision

Richard has a term for what’s coming: the “super accountant.” It sounds like marketing language, but his definition is specific. A super accountant has AI fluency, strong judgment, and understands compliance without needing to physically perform it. “They’re not a tech person doing accounting work,” Richard explains. “They are a technical person, maybe a CPA, that specifically knows how to leverage technology.”

The structural change everyone talks about is the pyramid becoming a diamond with fewer people at the entry level and more in the middle. But Richard makes an important distinction. The bottom rung is moving up in capability, not disappearing. Future CPAs will “reach a higher level of intellect, capability, and advisory skills at a much earlier age without decreasing the standards.”

Richard points to how this has happened before. Thirty or forty years ago, interns got coffee and made copies. Today, interns do actual client work. “The capability of interns moved up,” he says. The same shift is about to happen again, just bigger.

But the education system isn’t ready. Accounting programs are mostly theoretical. They “lay a foundation,” Richard says, but “certainly don’t give you anything that is pretty or accessible to a client.” Firms will have to bridge the gap with intensive training, it may look like six to eight months where new hires don’t touch billable work, just learn the craft.

The Review Problem

Blake raises the concern many accountants have voiced. If AI does all the basic work, how do people learn to review? The whole system depends on doing the work first, making mistakes, getting feedback, and building judgment.

Richard doesn’t dismiss this. He calls it “a real concern” and says you “can’t underestimate or understate the value of experience.” But then he reframes it with an analogy.

Try explaining to kids today why they need to know how to use an encyclopedia. It seems absurd. The skill became irrelevant because the tool changed. What replaced it was arguably harder: filtering reliable information from unreliable information online.

The same thing is happening with review. “Technology will take care of putting it in the proper box,” Richard says. “Your objective is to have the filter of understanding how to interpret the outcome.” And he goes further: “There may be a benefit to actually not having that anchor of how we used to do business.”

This isn’t theoretical. Tax GPT claims it fully automated 1040 preparation. Basis says it’s done the same for partnership returns. Richard has talked to both vendors. The pace of accuracy improvement is “impressive.” AI is rapidly getting to where it’s “right more than it’s wrong.”

But Richard draws an important distinction. Completing a tax return is just compliance. The real product is what happens after: the advice on paying less tax, structuring a business sale, or planning succession. “When you engage with your clients beyond delivering compliance services,” Richard notes, “fees don’t really come up.”

Why the Billable Hour Won’t Die

“Our people hate entering their time,” Richard says plainly. “There’s no value to the time they spend entering their time and it undervalues us.” Experienced professionals solve complex problems in an hour because they have 30 years of experience. Bill that as one hour, and you’re “undervaluing the 30 years of experience that allowed you to answer that question.”

Richard calls abandoning timesheets “the Mount Everest” of firm transformation. The billable hour is the operating system. Everything runs on it, including utilization, productivity, margin, capacity planning, performance evaluation, even work-life balance monitoring. “You can’t really erase billable hours without erasing all of it,” he says.

Then Richard makes an argument Blake hadn’t considered before. Timesheets might actually help prevent burnout. Sikich monitors employees running over their expected hours and treats it as a capacity problem. Without those guardrails, Richard argues, ambitious people “will work so hard, they’ll burn themselves out really quickly.”

But Blake zeros in on the real issue. AI has destroyed the link between time and value. If AI makes your team twice as fast, the client pays half as much under hourly billing. That math doesn’t work anymore.

So what replaces hours? “We haven’t necessarily identified a better alternative,” Richard admits. Accountants like data and hours provide lots of data. Any replacement becomes more subjective. Client satisfaction? Value delivered? Team engagement? These are harder to measure, and for a profession built on measurement, that’s a problem.

The Basketball Team Problem

Richard draws on his sports background to explain what might work better. Think about the 1990s Chicago Bulls. Michael Jordan and Scottie Pippen scored the points. But Dennis Rodman, the defensive specialist who didn’t score much, was essential. His contribution didn’t show up in the headline stats, but the team needed him.

“We’re not even looking at points. We are looking at time on the court.” Blake points out. The profession measures the wrong thing entirely.

But Richard warns that team models only work if everyone performs. If Rodman doesn’t hustle for rebounds while Pippen is scoring, or if Pippen takes a game off while Rodman is sacrificing his body, the whole thing falls apart. “You have to have a culture where the team performs within kind of a standard deviation of each other.”

The deeper problem is cultural. “The connotation of the employee becomes, I am an hours-based person. All I am is hours,” Richard says. When every review, promotion, or conversation starts with “how many hours did you work,” people internalize that their value is their time. Not their judgment or ideas.

And the system treats every hour as equal, which Richard calls “baseline, categorically false.” Some people think faster. That doesn’t make them more valuable, but under an hours system, it makes them look more productive.

The Implementation Gap

Richard says people actually don’t burn out from long hours. “I don’t hear complaints about the hours when it’s engaging work,” he says. He says his team gets excited working a long weekend for a complex client issue. The burnout comes from being stuck at 9 p.m. “dealing with software issues and plugging numbers into spreadsheets.”

AI can eliminate that burnout-causing work. But only if firms actually let it.

“We’re playing with it, but we’re not really implementing it,” Richard says. “We’re purchasing it, but we’re not really relying on it.” Firms pour billions into AI tools, but their training, career paths, and daily operations haven’t changed. The technology is there but the willingness to break old processes isn’t.

“There will be progression and there will be extinction. The question is at what pace,” Richard says, framing the stakes clearly.

Working harder won’t compensate for failure to adopt anymore. Buying AI products doesn’t mean you’re adopting AI. And trying to fit AI into existing processes instead of letting it break them is a choice with consequences.

“If you consistently try to find a place of complacency and comfort, you will not adopt at the pace necessary,” Richard warns.

The Choice Firms Are Making Right Now

What makes this conversation valuable is Richard’s willingness to acknowledge he doesn’t have all the answers. “I still have a lot to learn,” he says.

He can see the billable hour is broken and the pyramid is unsustainable. He can see buying AI tools without changing operations is theater. And Sikich is still tracking hours religiously.

That honesty tells you where the real work is. The super accountant future requires dismantling training models, educational assumptions, and measurement systems that have existed for decades. Not just purchasing new software.

For accounting professionals at every level, including partners making decisions, managers caught between old metrics and new realities, or someone early in their career wondering what’s ahead, the question is whether the firm will let AI change your work.

Richard has a message for other firm leaders: “Don’t let fear rule the day.” The firms that use AI as permission to break outdated processes will thrive. The firms that bolt AI onto unchanged operations will struggle. And that divergence is accelerating.

“I have every desire to be on the side of progression,” Richard says. Which side is your firm choosing?

Listen to the full conversation between Blake and Richard on the Earmark Podcast for deeper discussion on replacement metrics for the billable hour, building the super accountant pipeline, and why letting go of the past might be the profession’s biggest challenge. Then visit earmark.app to earn free NASBA-approved CPE credit.

When Tax Day Was Party Night at the Post Office — And Why AI Is About to Upend Everything Else About Accounting

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Before tax e-filing took over, April 15th was a public spectacle at American post offices. As Blake Oliver and David Leary discussed on their Tax Day episode of The Accounting Podcast, crowds would gather until midnight, with live entertainment, giveaways, and even Playboy offering “stress relief massages” in pink booths. In Philadelphia, there was a “dunk the IRS agent” booth for charity. Radio stations broadcast live. Fast food chains handed out samples. It was America’s weirdest annual party.

Those days are gone — 94% of returns are now filed electronically. But as the hosts explored in this wide-ranging episode, the accounting profession faces disruptions far more profound than the shift from paper to pixels. Within three years, KPMG expects routine audit testing to have “next to no human beings” doing the work. Hobbyist developers are cloning QuickBooks with AI over a weekend. And a third of workers aren’t even checking AI outputs before they submit them.

The IRS Can’t Keep Up — With Rules or Technology

The profession’s struggles with rapid change start at the top. Just five days before the filing deadline, the IRS finalized which jobs qualify for the new no-tax-on-tips deduction. Podcasters made the cut (Oliver and Leary were pleased), along with tattoo artists, ice sculptors, and golf caddies. Accountants didn’t.

“Five days after they finalized these rules to implement them for our clients,” Oliver noted with frustration. The deduction allows eligible workers to exclude up to $25,000 in tips from taxable income, but mandatory service charges don’t count. “This could be the death of the automatic gratuity,” Leary speculated, since those forced tips won’t qualify.

Meanwhile, Americans are spending 11.6 billion hours completing federal compliance forms — mostly tax returns. The value of that labor? Over half a trillion dollars. “That’s material,” Oliver said, noting it represents a significant chunk of the economy devoted to paperwork.

The IRS’s own modernization efforts tell a cautionary tale. The agency had 126 AI projects running as of last summer, up from just 10 in 2022. But after losing 25% of its workforce, 61% of those projects remain unfinished with no plan to close the skills gap. Even more puzzling: the IRS killed its Direct File program despite it costing only $16 million instead of the estimated $61 million and growing 78% year-over-year. “The program was gaining traction and was less expensive than they thought it was going to be, and yet it got canceled anyway,” Oliver observed.

The Big Four’s Radical Restructuring

While the IRS struggles with basic modernization, the Big Four are racing ahead with AI automation that could eliminate thousands of jobs and upend the billable hour model that has defined the profession for decades.

KPMG is moving fastest. They’re piloting AI systems this summer and deploying them next year for routine testing of transactions like payroll, receivables, and cost of goods sold. “Within 2 or 3 years, routine testing could become the first major audit area with effectively no human audit team directly doing the work,” Oliver quoted from KPMG’s audit chief digital officer. “Next to no human beings.”

The other firms aren’t far behind. PwC’s evidence-matching tool now processes 30 client document types, up from six months ago. EY is testing something even more futuristic: AI audit agents that talk directly to client AI agents to gather documents and prepare workpapers. Only Deloitte is publicly pumping the brakes, emphasizing AI should “augment not replace” human auditors.

The numbers are stark: Big Four leaders expect 20-30% of a typical audit to be fully automated by 2029. KPMG UK is already cutting 440 audit jobs. “I don’t see any other outcome than the Big Four just cutting massive numbers of staff jobs,” Oliver said. “If they do this right… that’s 20 to 30% of their billable hours. What are they going to do? Just raise their rates 20 to 30% to compensate?”

Leary had the line of the episode: “Agents are the perfect accounting firm employees. The partners are going to love them.”

The traditional career path is crumbling too. EY’s talent chief told Business Insider that linear career models are becoming “less relevant” as AI values skills over tenure. Oliver speculated firms might shift from hiring masses of new graduates to recruiting experienced professionals from industry, or moving to an apprenticeship model with smaller, more intensively trained classes.

Everyone’s Building Their Own QuickBooks Now

The disruption isn’t just coming from the top. A Reddit user built a full accounting system that runs inside Claude Desktop — no interface, just chat. You tell Claude what happened, and it updates your books. Another developer cloned QuickBooks Desktop using AI, creating a free open-source alternative. The motivation? “I didn’t want to pay for QBO.”

“You as an accounting firm had control over your tech stack and your clients’ tech stack,” Leary explained. “We’re a Xero shop or a QuickBooks shop… Now your clients are just building their own stuff. How do you as a firm manage this now?”

Oliver’s prediction, based on every past tech revolution: “We will end up with more work rather than less, because it will enable our clients to do way more accounting stuff that we’ll have to clean up.”

On the funded startup side, Juno raised $12 million to build AI tax prep that automates 90% of data entry while keeping CPAs in the loop. The key: transparency over autonomy, with source-to-return traceability and visual validation tools. Artifact launched Omni, which Leary called “a Zapier for accounting firms” — it trains AI agents to use your existing tech stack rather than replacing it.

Meanwhile, legacy players are scrambling. Xero published a blog post claiming to be an “AI native operating system.” Leary counted over 20 buzzwords and read them aloud in a devastating list: “AI native, intelligent SaaS, autonomous finance, system of action…” His verdict: “I don’t think this is written for customers. I think this article is written for the street in an attempt to move the stock price.”

The Quality Crisis Nobody’s Talking About

Here’s what should terrify every firm leader: 35% of workers rarely or only occasionally review AI output before submitting it, according to a Resume Now survey. Eighteen percent trust it straight out of the box. Only 40% review AI output every single time. And 15% use AI at work secretly without telling their manager.

“That should scare you as an accounting firm owner,” Leary said.

Oliver argued firms need systems with built-in controls: “If an employee is just generating something with AI… and they didn’t change anything or they didn’t spend any time looking at it, then flag that.”

The stakes are real. The episode covered two fraud cases that show what happens with weak oversight. A New Jersey preparer filed over 100 false returns seeking $170 million in pandemic credits, getting $55 million before being caught. A Pennsylvania preparer started a new $5.5 million fraud scheme while still on supervised release from a previous conviction.

What Separates Winners from Losers

A Hinge Marketing study of 133 firms revealed a massive performance gap emerging. High-growth firms are growing at 33% annually versus 9.6% for average firms. The difference? High-growth firms spend 9% of revenue on marketing (versus 5% for others), and over 90% use AI for content creation, automation, and research.

“If you have a firm that’s growing at 10% and you want it to grow at 30%, spend 10% of your revenue on marketing,” Leary summarized, though Oliver questioned whether it’s causation or correlation: “Is it just that the firms that are growing really fast have money to burn on marketing?”

The Reckoning Is Here

The accounting profession has always adapted slowly. As Leary noted, “Just ask Xero how it takes decades for them to barely make a scratch into the QuickBooks world.” But this time feels different. The changes are coming from every direction at once — Big Four automation, bedroom coders, funded startups, and clients building their own systems.

The irony is thick. Even as AI promises to make location irrelevant, EY is requiring US tax staff to work in-office 12 days a month. The IRS has 126 AI projects but can’t finish them. Firms are adopting AI while a third of workers don’t even review its output.

For firms willing to invest, experiment, and build proper controls, the opportunity is massive. For those hoping to wait it out, the message from this episode is clear: the profession that gathered at post offices until midnight to file paper returns is gone. The question isn’t whether AI will transform accounting — it’s whether the profession can maintain its core promise of trustworthiness while everything else changes around it.

To hear the full discussion — including the story of a disgruntled worker who burned down a $500 million Kimberly-Clark warehouse over pay disputes — listen to the complete episode of The Accounting Podcast.

A Simple Practice to Help Professional Women Stop Feeling Like They Haven’t Accomplished Enough

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When Questian Telka texted Nancy McClelland a photo of her newly framed diploma, she couldn’t help but undercut the moment. “I know it’s not a big deal because everyone has one,” she wrote, “but I never thought that I would actually do it.”

This degree took multiple attempts and years to complete. She’d been chasing the goal and finally crossed the finish line. And her first instinct was to shrink it.

Nancy wasn’t having it. “It’s actually a really big deal,” she fired back. “To say that it’s no big deal is silly. It’s a big deal because everyone else has one and you didn’t.”

That text exchange became the seed for the season two finale of She Counts, the real-talk podcast for women in accounting. Hosts Questian and Nancy brought on Valerie Heckman, accountant community manager at OnPay and keynote speaker, to dig into a concept that started as a single line in Valerie’s presentation at Scaling New Heights and became the thing everyone wanted to talk about afterward: the ta-da list.

Women in Accounting Are Wired to Overlook Their Own Wins

Valerie has spent nearly 15 years working alongside accountants and bookkeepers. She’s not an accountant, but she’s watched the profession long enough to spot patterns that run deep, especially among women.

“Very high internal standards,” Valerie said, naming it plainly. “The goal is getting things done, getting things done right, solving big problems, and staying on top of deadlines.”

That’s what makes accountants exceptional. “I think that can also come with a lot of focus on what’s left undone,” Valerie said, pointing out the shadow side. “Your brain is always managing, ‘Okay, we got this thing done, but now we’ve got to do this.’”

Nancy recognized herself immediately. “You just described my brain when I go to bed at night. I don’t go, ‘Oh, look at everything I did today.’ I go to bed and think, ‘Oh my God, I didn’t get this, that, and the other done today.'”

Then there’s the self-effacing reflex Valerie has heard countless times. “Oh, well, this is just what I do. I’m here to help. It was nothing.”

The problem compounds over time. When you only focus on what’s undone, Valerie explained, “We get very critical of ourselves. We start comparing ourselves to others. We start doubting ourselves.”

She spoke from personal experience. Before discovering the ta-da list, Valerie was burnt out, although she didn’t fully recognize it then. “I was on that constant hamster wheel of getting things done, but not necessarily feeling like I accomplished anything.” She’d write everything on post-it notes, stick them on the wall, and tear them down as she completed tasks. But every day brought more post-its. The wall was never clear.

“This is not a cure for burnout,” Valerie was careful to add. “I don’t want to sound like a Pollyanna or suggest we’re fine if we just focus on the good things. Absolutely not.” But it can be one tool in your toolbox.

What is a Ta-Da List?

The concept came from Gretchen Rubin’s podcast Happier with Gretchen Rubin, where it was mentioned almost in passing. A ta-da list runs alongside your to-do list but captures the opposite: what you got done, plus anything that enriched your life.

“You still need to-do lists,” Valerie emphasized. “Sometimes people get confused. They think I’m saying don’t keep a to-do list. No.” The ta-da list is the complement that captures what the to-do list doesn’t show you.

To prove the concept works, Valerie asked the hosts to name three ta-da moments from the past week.

Nancy had one instantly. She’d repotted two plants in her garden. Questian struggled. “Three things? That’s, uh…” The difficulty was the point. When she finally landed on something, it was huge. She and Nancy received their trademark for She Counts that week, after nearly a year of applications and responses.

“Circle the one you’re most excited about,” Valerie instructed. Nancy deliberately chose the smallest, the plants. “Because the small things are the big things,” she explained.

Valerie validated the choice immediately. “That’s such a great example because it’s something you did for yourself. Those are the most important things to celebrate. You actually stopped the busyness of life and did something for your own enjoyment.”

For Valerie, keeping a ta-da list in her planner with pen and paper made things tangible. But she’s seen people use voice recordings, photos, or digital notes. The method matters less than consistency.

What shifted for her went beyond feeling better. She felt more grounded, more capable. She started recognizing effort, not just outcomes. But the most surprising discovery was what was missing. Personal goals she’d been announcing every January but never pursuing became impossible to ignore. Speaking on more stages was one of them, and the gap in her data pushed her toward that Scaling New Heights keynote.

The list isn’t just a nightly ritual either. “On a bad day, I can look back at the ta-da list and be like, I’ve been there before. I’ve done this before. I am capable.”

Nancy was floored. “So you’re not just making this list before you go to bed. You’re going back and looking at these lists when you’re having a bad day.”

That’s the resilience piece. It’s documented proof of your own competence, waiting for exactly when self-doubt shows up loudest.

The Magic of Sharing Your Ta-Da’s

The practice transforms when you let other people in on it.

An accountant named Nancy Jacobson approached Valerie at an event with a story that made her tear up. Jacobson started doing ta-da moments with her son at dinnertime. “I ask my son about his day and what his ta-da moments are. Then I share mine and we talk about it as a family. It’s made us closer.”

There’s also Ali Szymanski’s story. Nancy’s right-hand woman at The Dancing Accountant emailed Nancy when she completed her first wage reconciliation. “I know this is silly, but…” she’d written. It wasn’t silly. It was a ta-da moment worth celebrating.

Valerie suggested teams open meetings by asking, “What’s one good thing that happened in the last week?” One group she worked with realized they always jumped straight into tasks, everyone already overwhelmed before the first agenda item.

The conversation turned to something that made everyone laugh: gold stars. “We grew up being very motivated by gold stars and scratch-and-sniff stickers,” Valerie said. We don’t have to stop giving them to ourselves just because we’re grown ups.

Nancy had proof this works. During a weight loss journey, she used an actual sticker chart with foil stars. Her husband Mark offered to make an Excel version. She told him that was nice, but she needed real stars on real paper. She lost 20 pounds and kept most of it off for over a decade.

But celebration doesn’t mean ignoring struggle. “Gratitude doesn’t have to cancel out your struggle,” Nancy said. “It doesn’t mean you are not also struggling.” You can be drowning in a software transition during tax season and celebrate that someone helped you at 10 p.m. on Zoom. You can be exhausted and notice that your repotted plants look beautiful.

Your Ta-Da List Starts Tonight

Valerie’s keynote slogan crystallized the whole concept: “Less to-do’s, more ta-da’s.”

If your days are so packed with tasks that there’s no room for anything worth celebrating, something needs to come off the list. As Valerie put it, “Mick Jagger does not tune his own guitars. What are the things that only I can do?”

Here’s what you can do starting today:

  • Write down three things tonight. What did you accomplish or what enriched your life today? If you can only think of one, that’s fine. The difficulty means you need this.
  • Circle the smallest one. The little things are the big things, whether that’s repotting plants, calling difficult clients, or making it to the airport on time.
  • Keep old lists. On bad days, they’re proof of your competence.
  • Notice what’s missing. The gaps reveal goals you keep announcing but never pursue.
  • Share your ta-da’s. Text a friend. Open team meetings with wins. Say names out loud when people help you.
  • Make room for more. Eliminate, automate, delegate. Create space for things worth celebrating.

For women in a profession that measures value in accuracy and completed tasks, learning to see (and say out loud) what’s going right is an act of resistance against the voice that says “it’s nothing.”

It’s not nothing. Frame the diploma. Put the gold star on the chart. Ta-da!

Listen to the full episode to hear Valerie’s live exercise and the Marge Piercy poem that closed the show. Then head to the She Counts LinkedIn page and share something you celebrate that might seem silly to others.

Not All AI Is Created Equal and Your Next Software Decision Depends on Knowing the Difference

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When Jeff Seibert ran consumer product at Twitter, he asked the finance team for his budget to throw a team event. They said they’d get back to him in 45 days. So he just ran the event without them.

That gap between real-time data and 30-to-90-day delayed financial reports was frustrating, and it eventually led Jeff to build Digits, a new general ledger designed from scratch for the machine learning age. After raising $100 million pre-launch, testing 2,000 monthly closes, and getting 80% of clients closed in under an hour, Digits launched in March 2025. Now, just over a year later, hundreds of accounting firms are onboarding thousands of clients onto the platform.

Jeff launched Twitter’s algorithmic timeline in 2016, and it was one of the first global deployments of machine learning. Now, the AI revolution Jeff helped launch is flooding the accounting profession with claims that are hard to verify. Every accounting software company seems to include AI in its marketing copy, promising everything from “fully automated bookkeeping” to capabilities that don’t add up under scrutiny.

In a recent Earmark webinar, host Blake Oliver and Rob Hamilton, Head of GTM at Digits, pulled back the curtain on how AI in accounting actually works. He was joined by Megan Reid, Product Specialist & Firm Enablement at Digits, who fielded questions throughout the session.

Every AI claim in accounting software isn’t real. But accountants who understand the four core model types (plus one common lie) will make smarter investments, automate the right parts of their workflow, and position their firms for a shift Rob sees coming by the end of 2026.

The AI hype problem (and one question to cut through it)

Before making any technology decision, you need a filter for separating real capabilities from marketing fluff. Rob offered a simple one that cuts through the noise.

He showed screenshots from multiple accounting software companies making bold AI claims. One promised “fully automated bookkeeping.” Another asked, “Do you do AI bookkeeping or do you use a dedicated team of experts?” The positioning has gotten so confusing that firms can’t tell what’s real anymore.

The confusion isn’t new. About five years ago, tech investor Naval Ravikant tweeted, “In most pitch decks, AI stands for Anonymous Indians.” For a long time, that was literally true. Services like Botkeeper rose and fell using offshore labor dressed up as automation. Today, “AI actually means we just bolted on and sent all of your data to ChatGPT,” Rob explained.

Here’s your filter: “AI is the same thing as machine learning,” Rob stated. “If someone is talking to you about AI and they’re not referring to machine learning as the underlying premise, it’s just BS.”

But this filter only works if you understand what machine learning actually is.

Traditional software is straightforward. You write code that tells the computer exactly what to do. It’s tedious to build, but rock solid once it works. Machine learning flips this completely. You feed the system thousands or millions of examples, and the model learns the patterns itself. As Jeff explained in a clip Rob played, “You give the computer the goal state—I want this outcome—and then the computer itself is learning how to do it.”

These models are neural networks. Thousands of hidden layers mimic how neurons connect, based on Google’s 2017 “transformer” research paper (the “T” in GPT). It’s a massive matrix multiplication problem where the system figures out how variables relate to each other.

But machine learning isn’t one thing. Different model types have different strengths and uses in accounting. Understanding these distinctions helps you avoid buying the wrong software and shows you exactly where AI can save time and where vendors are overselling.

The model types that matter (and one that doesn’t)

Rob walked through five categories that get lumped under “AI,” but understanding the differences is what separates informed decisions from expensive mistakes.

Generative models

Large language models (LLMs) are the ones you hear about most, ChatGPT being the prime example. GPT stands for “Generative Pre-Trained Transformer,” and these models generate the most likely continuation of whatever prompt you give them. Rob showed a useful application: turning bullet-point close notes into polished client emails. His advice is to write a “job description” for the AI once. Tell it who it is, give context, specify output format, add examples. Then just paste in different client notes as needed.

But generative models have serious limits. They’re “super eager” and always want to complete prompts, making them prone to hallucinations, or making things up that sound real. They’re bad at math because they generate text rather than calculate numbers. And they’re trained on the internet, not your specific clients. “The ways that it is hallucinating is stuff that maybe even is hard for humans to catch sometimes,” Rob warned. Always review the output.

Agents

These are LLMs with help. You give them a job description, a task, and tools, like computer programs they can use to generate reports, list accounts, or run calculations. The agent makes a plan, uses its tools, checks if the task is done, and loops until complete. Rob showed Digits’ agent answering “I want to hire 20 software engineers next year. Can I afford to?” with a data-backed response.

Guardrails are critical. Microsoft’s early agent “started asking people on dates in the chat,” Rob noted. “You don’t want your accounting agent dispensing dating advice.” Agents work well for updating schedules, running quality checks, and answering analytical questions, but they’re slow and need careful boundaries.

Predictive models

These got Rob visibly excited, and for good reason. These models take an input and predict an output from known options. When the model sees a $5 Starbucks charge, it considers the client’s location, history, and chart of accounts. For a local client, it’s meals and entertainment. For a New York client in California, it’s travel. A $157 Starbucks charge is probably an event, regardless.

What makes predictive models perfect for transaction categorization is they can’t hallucinate; they only choose from existing options. They’re deterministic (same input, same output), include confidence scores, and run fast and cheap once trained.

Digits built a “layer cake” of predictive models:

  1. Client-level (learns each business)
  2. Firm-level (encodes your best practices)
  3. Global (trained on 180 million transactions worth nearly $1 trillion)
  4. An LLM fallback for completely new transactions

The result was over 97% accuracy, compared to standalone LLMs that plateau below 80%, which is about the same as outsourced bookkeepers.

Document extraction models

These combine OCR with layout-aware language models that understand document structure. Previous tools used Amazon’s Mechanical Turk, which relied on humans manually extracting data and took hours. Modern extraction models work in seconds. Digits’ bank reconciliation automatically pulls PDF statements, matches transactions to the exact spot in the PDF, and generates audit reports.

Data analysis

This model is where Rob pulled the rug out. Financial reporting and analysis is “actually just math. It’s not ML.” Computers have done statistical analysis for decades. Could you build an agent to do it? Sure, but it would be slow, expensive, and probably wrong. “If anyone says their AI does reporting and statistical analysis, please ask them what they’re talking about.”

Here’s how the right models map to your month-end close:

  • Book transactions: Predictive models (with LLM fallback)
  • Reconcile statements: Extraction models plus matching algorithms
  • Update schedules: Agents
  • Review and correct: Agents with quality checklists
  • Analyze and report: Statistical analysis plus agents for questions

“Shoehorning an LLM in to solve a problem and just sending a bunch of information is fundamentally incorrect,” Rob emphasized. Each step needs the right model. No single AI approach handles everything.

The 2026 prediction

Understanding model types is just the foundation. The urgency comes from how fast everything is converging.

“Across a large client set in different industry types, it’s highly likely that the month-end close process is looking to be completely automated by the end of 2026,” Rob predicts. Even his “95% automated” hedge probably sounds aggressive. But his logic follows directly from the technology.

If predictive models hit 97% accuracy on transactions, extraction models automate reconciliation in seconds, agents handle schedules and quality control, and statistical analysis covers reporting, then manual work drops to a fraction. Rob’s goal is to see accountants doing “1/20th of the work you’re doing today.”

He acknowledged limits. Construction firms with complex job costing might not hit that threshold. But for firms serving professional services, cash-basis businesses, and straightforward accrual clients, the automation curve is steep.

An AI-native firm will focus on value instead of tedium. Deeper industry expertise. Stronger client relationships. Higher margins. You’re not reviewing every transaction, you’re supervising the system and handling exceptions. Those hours saved on reconciliation become advisory time clients actually value.

But this is also a competitive necessity. “AI won’t replace you. Someone who’s good at using AI is going to,” Rob said, quoting a common warning. And he was direct about the stakes. Firms that don’t adapt will face “a cascading effect on business models” as early adopters pull ahead.

For overwhelmed firms, which Rob acknowledged includes most firms, he offered practical starting points:

  • Map your processes first. If you use workflow tools like Karbon or Keeper, you’ve probably documented your steps. If not, start there. You can’t identify where AI fits until you know what you’re actually doing.
  • Start small and low-stakes. Don’t tackle your biggest challenge first. Try drafting emails, testing categorization, or visualizing data. Build your intuition gradually.
  • Get hands-on with new tools. Rob mentioned being impressed by Claude Opus, which could build HTML dashboards from his data (something he couldn’t do as a non-engineer). The specific tool doesn’t matter; hands-on experience builds judgment.
  • Know your business before choosing where to start. As Rob put it, “You need to know the details of your business to know where you can start and where the right places to poke and prod are.”

The “wait and see” window is closing. Firms that develop AI literacy now by asking questions about models, data handling, and use cases, will be ready for the rest of 2026 and beyond.

Your next move: Better questions, smaller steps, faster action

Let’s turn Rob and Megan’s insights into actionable takeaways:

  • Not all AI is equal. Four real model types plus one fake (data analysis) get lumped together. When vendors pitch “AI-powered reporting,” you now know to dig deeper.
  • Each close step needs a different model. Predictive for transactions. Extraction for reconciliation. Agents for schedules. Statistics for reporting. Anyone claiming one solution does everything deserves scrutiny.
  • Predictive models beat LLMs for categorization. Layered architectures that learn your clients and firm patterns dramatically outperform chatbots. Bigger isn’t always better.
  • Ask vendors the hard questions. What model type? Where does data go? Are you training your own models or sending financial data to third parties? This is due diligence.
  • The tipping point is closer than you think. Whether Rob’s 2026 prediction proves exactly right or directionally right, the trajectory is clear. Understanding these distinctions now positions you to take action.

For those interested in going deeper, Rob mentioned resources like the AI Native Accounting Foundation and the AI-Native Accounting podcast hosted by Kacee Johnson, where industry leaders discuss the latest developments.

The accounting profession is at a real inflection point. Smart firm leaders will develop the literacy to ask smart questions, experiment in the right places, and redirect time from tedium to advisory work clients value.

Rob noted this might be one of the few professions with such clear AI use cases, putting accountants at the forefront of innovation. That’s an opportunity to shape how technology serves the profession, not the other way around.

Watch the on-demand webinar for complete details, including live demonstrations, security architecture specifics, and audience Q&A covering nonprofits, inventory clients, and platform migrations. The future of accounting is being written now. Make sure you’re part of the conversation.