Blake Oliver

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

Earmark Team · April 25, 2026 ·

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

Earmark Team · April 25, 2026 ·

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.

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

Earmark Team · April 17, 2026 ·

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:

Client-level (learns each business)
Firm-level (encodes your best practices)
Global (trained on 180 million transactions worth nearly $1 trillion)
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.

AI Agents Now Complete Tax Returns Start to Finish While the Government Can’t Even Audit Its Own Books

Earmark Team · April 13, 2026 ·

The US government just declared itself insolvent. AI agents are completing tax returns without human intervention. And the accounting profession is caught between these two massive disruptions.

In Episode 481 of The Accounting Podcast, hosts Blake Oliver and David Leary opened with a bombshell that somehow flew under the mainstream media radar. The Treasury Department’s own financial statements show the US is $42 trillion in the red, and that’s before counting Social Security and Medicare obligations. They then dove into an equally seismic shift with guest Kenji Kuramoto, founder of Acuity and newly appointed Managing Partner in Residence at AI company Basis, exploring how artificial intelligence is transforming every corner of the accounting world.

Deficit Spending Just Keeps Going

“It’s official. We are insolvent,” David announced at the start of the episode, referencing the Treasury’s 2024 financial statements. They show $6 trillion in total assets against nearly $48 trillion in total liabilities. That $42 trillion hole doesn’t even include the $88 trillion in unfunded Social Security and Medicare obligations sitting off the balance sheet.

“Imagine a family making $52,000 that owes $1.3 million in a line of credit,” Blake said, putting the crisis in household terms.

Making matters worse, the Government Accountability Office issued a disclaimer of opinion for the 29th consecutive year, essentially saying it can’t even verify the accuracy of the numbers because the Department of Defense has never passed an audit.

“This is the reason a huge number of people voted for Trump,” David said. “They wanted to stop deficit spending, and it just keeps going.”

Meanwhile, AI Is Eating the Accounting Profession

While the government’s books are falling apart, AI companies are racing to automate the work of keeping everyone else’s books together. TaxGPT announced an AI agent capable of completing 1040 returns from start to finish without a preparer touching a keyboard or mouse. The tool works with existing web-based portals and tax prep software, pulling in W-2s, 1099s, and other source documents, then having a review agent double-check everything.

“Why go after tax pros?” David asked. “Just get in bed with the portal companies and go after TurboTax.”

Kenji, who recently joined Basis after selling Acuity and taking a year off, described watching AI agents handle complex accounting work that made him come out of retirement. “I saw an agent handle complex payroll entries like booking the GL entry, creating an accrual because the pay period didn’t align with month-end, posting the reversing entry for the following month, and building a complete set of work papers,” he said. “I saw this last year, and I was like, wait, what?”

The flood of AI announcements kept coming throughout the episode:

Ramp launched an accounting agent that auto-codes transactions down to the line-item level on invoices, claiming to save finance teams 40+ hours per month
Xero announced a multi-year partnership with Anthropic to integrate Claude AI directly into its platform
Canopy launched a bookkeeping module with AI that continuously reviews books and flags issues in real time
Double (formerly Keeper) released AI Journal Entries that can handle complex, repetitive entries from source documents
BILL announced agents for invoice coding, W-9 collection, and automated vendor payment responses

“Everyone thought we were boring,” Kenji said. “Look at this. All these Y Combinator companies spinning up and fundraising announcements and agents everywhere. Come on. Exciting.”

The Skills Gap Is Already Here

The shift is showing up in real time in hiring data. In 2023, only 18% of accounting job postings mentioned AI skills. Now it’s 30%, a 67% increase.

“The real-world requirement is probably 50%,” David argued. “People are behind on updating their postings.”

But a better question is what happens to the business model. Kenji described how at Acuity, the bottleneck was always people. Plenty of companies needed help with their books, but you couldn’t hire enough accountants to serve them cost-effectively. AI agents break that constraint. One highly efficient bookkeeper might handle 45 to 60 clients today. “Will one person eventually be able to handle 200 clients?” David asked.

The threat isn’t just from other firms. An article on Payments.com found that everyday taxpayers are already using ChatGPT and Gemini to do their taxes before ever talking to a professional. The reason is “speed and simplicity,” David explained. “AI can explain tax concepts, organize the documents, and suggest deductions. These are things they’re not getting from their tax professional.”

Are Tokens the New Billable Hour?

As AI cuts the time needed to complete work, firms are scrambling to figure out how to price their services. Bloomberg Law reported that PwC, KPMG, and RSM are all exploring alternatives to hourly billing.

“This may be the thing that finally gets us there,” Kenji said about moving away from billable hours. “If I just used AI to help me get my work done and I’m cutting down my billable hours, I’m losing revenue.”

“You can bill for tokens,” David suggested, offering a provocative alternative.

He then vented about Earmark’s own token consumption across multiple platforms, including Claude, GitHub Copilot, Retool, ChatGPT, and more. “Two days ago, an automation stopped working,” he said. “We spent five plus people hours trying to increase our tokens and get the automation working again.”

The problem is, token costs are opaque and growing. David introduced two terms gaining traction: “token anxiety,” or not knowing what you’re being charged for, and “AI FinOps,” managing AI costs across platforms.

“There’s an opportunity here for firms to become a token expert and offer it as a service,” David suggested.

Blake’s take was more pragmatic. “It’s better than timesheets, that’s for sure.”

The Window Is Closing

The government that sets the rules can’t even audit its own books while declaring itself insolvent. Meanwhile, AI agents are automating core accounting work at a pace that makes the shift from paper to computers look gradual.

“These agents are actually now becoming a component of our workforce,” Kenji said. “You’ve got accountants and you’ve got agents. This is the future state we’re moving into.”

For practitioners, it’s clear that the tools to dramatically expand your capacity exist right now. But so does the threat of clients going straight to AI and bypassing your firm entirely. The window to adapt is open, but it won’t stay that way for long.

As Blake noted about current AI pricing, “When Uber was new, everything was really, really cheap.” The subsidies won’t last forever. To thrive, firms need to figure out the new economics now, whether that’s value pricing, token billing, or something else entirely. Those that don’t may find themselves as obsolete as the government’s ability to balance its own books.

Listen to the full episode for the complete discussion, including deeper dives into specific AI capabilities and Kenji’s firsthand perspective from inside an AI-native company.

The IRS Now Knows Who’s Trading Crypto But Can’t Tell What Anyone Owes

Earmark Team · April 7, 2026 ·

The IRS now knows who’s trading crypto, but it still can’t tell if anyone owes tax. That’s the reality of the new 1099-DA reporting system that just went live, and it’s about to affect every tax professional with crypto clients.

On a recent episode of the Earmark Podcast, host Blake Oliver sat down with Lawrence Zlatkin, Vice President of Tax at Coinbase, to explain what the new 1099-DA form reports, where the gaps are, and what changes Coinbase is pushing for in Washington. With a front-row seat to crypto taxation’s biggest challenges, Lawrence offered insight on where the system works (and where it doesn’t).

The problem is that the IRS’s new reporting brings crypto tax enforcement into the mainstream, but the underlying framework creates massive overreporting with little tax benefit. Treating stablecoins as property and requiring reports on tiny gas fees generates millions of forms that tell the government almost nothing about actual tax liability. Tax professionals must bridge the gap between what the IRS receives and what matters for computing taxes.

The 1099-DA: What’s There and What’s Missing

Think of the 1099-DA as crypto’s version of the 1099-B that brokers send for stock trades. The basic concept is familiar: the form goes to your client and the IRS, and the government matches what taxpayers report against what exchanges report. Tax pros have worked with this system for decades.

But this first-year version is bare-bones. As Lawrence explained, “We are implementing the system barely 18 months after Congress issued the regulations. The 1099-B system was developed over a period of five years, and even longer for gross proceeds.”

The result is a “skeletal version” that reports just two things: who the customer is and their gross proceeds from transactions. The critical missing piece is cost basis.

“We’re including basis for our customers for informational purposes, but that information is not actually going to the government,” Lawrence said. Next year, exchanges will start reporting basis, but only when they have it.

Blake walked through a simple example. Say your client sells Bitcoin for $100. The IRS gets a 1099-DA showing $100 in gross proceeds. But if your client bought that Bitcoin for $90, the actual taxable gain is just $10. That $10 is the only number that matters for taxes, but it’s invisible to the government this year.

The problem worsens with transfers between wallets and exchanges. When crypto leaves Coinbase for a self-custody wallet or another exchange, the basis tracking breaks. “The only person who knows what’s in a non-custodial wallet is you because you’re the owner,” Lawrence explained. When that crypto returns to an exchange, there’s no way to reconstruct what happened in between.

So what’s the point of all this reporting? Lawrence was candid. “The government’s concern has been that there’s been underreporting and noncompliance in the ecosystem generally. So what this achieves from their standpoint is they find out who’s really participating.”

Until now, the IRS’s only crypto signal was that checkbox on the 1040, which Lawrence diplomatically called “gobbledygook.” It asks about digital asset transactions. Now the IRS will see actual dollar amounts attached to names. They’ll spot whales with millions in proceeds. They’ll identify non-filers.

“There’s nothing nefarious or awful or evil about that,” Lawrence said. “It’s just that they will have that information they didn’t otherwise have before.”

The practical takeaway is, “you are in control of your tax data,” Lawrence emphasized. Clients who consolidate their activity on a single exchange will have better records. Coinbase provides transaction history and gain/loss data through its “position service.” But clients bouncing between exchanges and wallets need to maintain their own records. Nobody else can do it for them.

The Stablecoin Problem: When Property Isn’t Property

Missing basis data would be manageable if the tax framework made sense. It doesn’t, especially for stablecoins.

Since 2014, the IRS has classified all crypto as property rather than currency or cash equivalents. This includes stablecoins like USDC, which are designed to trade at exactly $1. Every time your client uses USDC, that’s a reportable disposition of property.

“Stablecoins are designed to be stable and consistent and traded at par with the US dollar,” Lawrence said. “99.9% of the time, it’s intended to trade within a fraction of a decimal of the US dollar. So in essence, we’re not reporting a gain or loss. So it’s over-reporting of data. There’s no fundamental purpose for that. I would argue that the only reason for that is surveillance.”

The scale is significant. Coinbase must report stablecoin transactions exceeding $10,000 to the government. Hundreds of thousands of customers received 1099s this year that include these transactions. And taxpayers must report even smaller amounts. Coinbase just won’t tell the IRS about those.

Blake offered his own example. Earmark uses USDC to pay vendors for international transactions where stablecoins are faster than traditional banking. Under current rules, every payment is a reportable property disposition. “It’s as if the IRS got every bank transaction,” Blake said. “Americans would never stand for that. We’d call that surveillance and overreach.”

Lawrence revealed this isn’t hypothetical. Five years ago, the Treasury Department proposed requiring banks to report credit card transactions over $10,000 in aggregate. “That was quashed for the reasons you just described,” he said. Yet here we are with stablecoins.

There’s a small silver lining. “The tax system is based on income. If there’s no gain or loss, there’s no taxable income, and there’s no penalty,” Lawrence explained. You can’t underpay taxes on zero gain. But the reporting requirement still exists.

De Minimis Madness: When Pennies Become Paperwork

Beyond stablecoins, tiny transactions that generate enormous paperwork are another reporting nightmare.

Gas fees, which are the network costs for blockchain transactions, often involve disposing of pennies or fractions of dollars worth of Ethereum. Each one is technically a property disposition that must be tracked and reported. Each might have actual (if microscopic) gain or loss.

The volume is staggering. Coinbase files millions of 1099-DAs containing hundreds of millions of underlying transactions that feed into Form 8949. Lawrence estimates that about half qualify as de minimis, meaning they’re essentially meaningless for tax purposes.

“We’re not going to pave roads and solve the deficit on the backs of de minimis reporting for crypto,” Lawrence argued. He’s pushing for a threshold below which transactions become exempt from reporting or taxation. Should it be $5? $50? $200? Should it be income-based or transaction-based?

“At what point do we stop requiring reporting for transactions?” Lawrence asked. “If the IRS gets bombarded with billions of transactions that are tiny in nature because people are required to report them, the system itself breaks.”

These billions of transactions are being reported today, and the IRS’s ancient computer systems must somehow process them all.

The Washington Agenda: Common Sense Reforms in Political Gridlock

Lawrence came with a clear policy agenda that included ten priorities, although the conversation covered highlighted six in detail.

Beyond stablecoins and de minimis thresholds, Coinbase is pushing for the following reforms:

Crypto lending should work like securities lending. “You’re not disposing of crypto because you’re going to get the same amount back,” Lawrence explained. Under current securities rules, that’s not taxable. Crypto should be the same.
Staking rewards timing. The IRS says rewards are taxable when received. Others argue they shouldn’t be taxed until sold. “That’s a source of friction and debate,” Lawrence noted.
Charitable deductions are perhaps the clearest absurdity. Donate over $5,000 in Bitcoin, and you need a formal appraisal. “You can type it in Google and get a Bitcoin price, just like you get the price of any stock or security,” Lawrence said. Bitcoin has “readily ascertainable fair market value.” The appraisal requirement is “ridiculous.”
Foreign investment rules. The US has safe harbors that allow non-US persons to trade securities through US brokers without triggering US tax. No equivalent exists for crypto. “We’re the best and safest market in the world,” Lawrence said. “We’d like to preserve that for crypto, not just for regular old investment assets.”

So why hasn’t anything passed?

“I’m cautiously optimistic,” Lawrence said. President Trump has been supportive. He met with Coinbase CEO Brian Armstrong last week. The White House issued a report on digital assets, including tax provisions. Treasury has been “by and large very supportive.”

But Congress is the bottleneck. The House is narrowly Republican-controlled, and crypto has become more partisan than it should be. “This should not be a partisan debate,” Lawrence insisted. “This ecosystem benefits Democrats and Republicans.”

The Clarity Act for crypto regulation is under discussion. So is broader tax reform. But as Lawrence diplomatically put it, “Things don’t move as quickly as we might like in Washington.”

What This Means for Tax Professionals

The picture Lawrence painted is clear, even if the rules aren’t. The 1099-DA tells the IRS who’s trading and how much, but it lacks the cost basis needed to determine actual tax liability. Tax professionals must fill that gap by reconciling gross proceeds against basis records scattered across exchanges, wallets, and spreadsheets.

Meanwhile, classifying stablecoins as property without de minimis rules creates millions of reportable transactions with zero tax consequences. It’s all noise, no signal.

The reforms Coinbase wants make sense. But with narrow Congressional majorities and partisan friction, don’t expect relief before next filing season.

The message for practitioners is crypto is no longer niche. With millions of 1099-DAs arriving and IRS matching letters sure to follow, you need to understand what these forms show, how to help clients track basis, and where the traps are. Firms that build this expertise now will serve a growing client base. Those who don’t risk being blindsided along with their clients.

“Everyone wants to talk about tax,” Lawrence joked at the start. By the end, it’s clear why. The intersection of crypto and tax is where innovation meets regulation, and right now, regulation is playing catch-up.

Listen to the complete episode of the Earmark Podcast for Lawrence’s full breakdown of Coinbase’s policy priorities and practical advice on basis tracking. You can earn free NASBA-approved CPE credit by listening and taking a short quiz at earmarkcpe.com.