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The End of Data Entry and What It Means for Your Tax Practice

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Elizabeth Beastrom left public accounting 30 years ago because she was sick of rekeying data into tax returns. Now, as President of Tax and Accounting Professionals at Thomson Reuters, she works to make sure no accountant has to do that mind-numbing work ever again.

“I was a lazy CPA,” she admits with a laugh during this episode of the Earmark Podcast. “I didn’t want to spend my time doing work that I didn’t think was necessary.”

In this conversation with host Blake Oliver, Elizabeth and Kirat Sekhon, Thomson Reuters’ Head of Technology, map out their vision for automating the entire tax workflow, from gathering documents to delivering returns. They want listeners to know that AI-enabled firms are going to outcompete everyone else, and the shift from compliance to advisory isn’t optional anymore.

Why Tax Firms Can’t Keep Doing Things the Old Way

The numbers tell the story. Fewer people are taking the CPA exam while more accountants retire every year. Meanwhile, tax complexity keeps growing, which means more demand for services with fewer people to do the work. Throw in private equity firms buying up practices and pushing for efficiency, and you’ve got a perfect storm.

But it’s not just about headcount. The new generation of accountants expects modern tools that actually work together—not the clunky desktop software their predecessors put up with.

“They expect to use intuitive and connected tools,” Kirat explains, “so they have a better experience while they deliver value to their customers.”

So why has tax software stayed stuck in the desktop era while cloud accounting tools have taken off? Kirat points to two reasons. First, tax calculations are hard to get right, and once you build something that works, nobody wants to break it. Second, accountants themselves haven’t pushed for change. When you’re working 80 to 100 hours during busy season, the last thing you want is to learn new software.

“The term SALY—same as last year—still comes through,” Elizabeth notes. “You found a way to do it and you like to replicate that. Change is hard, especially when you have to introduce that to the firm when you’re working 80 to 100 hours a week.”

But resistance to change is becoming dangerous. Elizabeth’s own exit from the profession 30 years ago shows what happens when the work becomes too tedious. Back then, she discovered she loved the advisory side, including talking to clients, understanding their businesses, and making recommendations that actually helped them improve. But she was stuck doing data entry.

“I would spend time talking to my customers,” she recalls. “Some of my best inputs came from the people in accounts payable or accounts receivable. I would get a detailed understanding of their process.” But then she’d have to go back to rekeying tax data, and the contrast was too much.

Building the “Bookends” Around Tax Prep

Thomson Reuters isn’t trying to fix one piece of the tax workflow; they’re automating the whole thing. Their strategy focuses on creating what Elizabeth calls “strong bookends” around their core tax engines (GoSystem Tax, CST, and UltraTax).

The front bookend came through their acquisition of SurePrep three years ago. Practitioners dump all their client documents into the system, and SurePrep automatically classifies them, pulls out the relevant numbers, creates a binder for review, and fills in the tax software. No more manual data entry.

“That’s a huge time savings when you don’t have to spend time doing all of that manual data entry,” Kirat says, “and they can actually focus on the return.”

The back bookend arrived with SafeSend, acquired earlier this year. It handles return delivery, e-signatures, and payment collection, eliminating what Elizabeth remembers as the nightmare of printing, mailing, and faxing documents back and forth 30 years ago.

What’s different about Thomson Reuters’ approach is they’re keeping these tools open to work with competitors’ software too, not just their own tax products.

“It is an open, curated ecosystem,” Elizabeth emphasizes. “If customers find value in part of their workflow, we want to make sure we connect to it.”

Beyond just automating existing steps, they’re trying to eliminate unnecessary work entirely. Take the client questionnaire—that paper organizer Blake’s mom still fills out by hand every year. Thomson Reuters wants to “kill the questionnaire” by using AI to pre-populate information from prior returns and only ask for what’s actually new or missing.

The next frontier is what Kirat calls “agentic AI,” systems that don’t just handle one task but orchestrate entire workflows. These AI agents can use multiple Thomson Reuters products in sequence, making decisions along the way to get a return from start to finish with minimal human intervention.

But everything the AI does needs to be auditable. Kirat stresses that any AI handling tax work must show exactly what decisions it made and why.

“Our customers expect the work product of an accountant to be 100% accurate,” she explains. “Without providing that audit log with the decisions and choices and confidence levels, we’re missing the mark.”

Blake agrees enthusiastically, sharing his frustration with current AI tools that don’t show their reasoning. “I want to know why it matched this transaction,” he says. “There’s an AI conversation for each one of these transactions. Why not give that to us?”

The Shift to Advisory Can’t Wait

If machines can prepare returns faster and more accurately than humans, what exactly are clients paying for? Two-thirds of Thomson Reuters’ customers say they want to shift to advisory services, but most don’t know how to actually do it.

Enter Ready to Advise, launched in June 2024. The tool takes everything from a completed return and analyzes it against potential tax strategies based on that client’s specific situation and goals.

“It will quantify the savings,” Elizabeth explains. “It will ask for more information to get to a range. It will allow you to have that discussion where you can say, ‘Hey Blake, I noticed from your 1120-S filing some potential strategies you should take.'”

Then it walks you through implementing those strategies and produces client-ready documentation. For firms struggling to move beyond compliance, this is huge.

But technology alone won’t fix the business model problem. Clients have been trained to expect strategic advice for free. “I might call my accountant and say, ‘Hey, tell me what this big beautiful bill does for me this year?,’ which is code for don’t charge me for this,” Elizabeth says, capturing the conundrum perfectly.

That’s where Practice Forward comes in. It’s Thomson Reuters’ tool for helping firms understand their worth and develop advisory pricing models. The goal is shifting from hourly billing for returns to year-round advisory subscriptions.

Ready to Advise also solves a talent problem. Traditionally, you needed years of experience before you could offer meaningful tax advice. But with AI assistance grounded in Checkpoint’s content (maintained by over 4,500 subject matter experts), newer staff can contribute to advisory work much sooner.

“That junior associate’s experience, paired with all the knowledge that there is available in generative AI today, is incredibly powerful,” Kirat notes.

Blake shares a personal example that drives home the value of advisory over compliance. His tax preparer advised setting up a C-Corp to potentially qualify for QSBS treatment, which could save millions in taxes someday.

“I can’t even quantify the value of that,” Blake says. “But that’s why I’m willing to pay thousands of dollars for a tax return. It’s that insight, not the return.”

Meanwhile, DIY tax software keeps getting better. Blake describes doing a business return himself using consumer software with ChatGPT open for research. The same process would have taken hours of manual work just a few years ago.

Firms that stick to just preparing returns are going to get squeezed from both ends.

“AI-enabled professionals and firms, they’re going to outcompete and outperform,” Elizabeth warns, “because they’re going to be able to do it faster, better and get to this advisory, which our clients want.”

What to Do Right Now

So where should a traditional tax firm start? Elizabeth recommends figuring out what you hate doing.

“What are your pain points that you hate to do?” she asks. “There’s a pretty high likelihood that I or a talented person on my team is going to be able to say, ‘This is how we can solve that for you.’”

The technology exists today. SurePrep can handle document gathering. SafeSend can automate delivery. Ready to Advise can help you identify tax-saving opportunities. CoCounsel can answer complex questions using curated, expert-verified content. The audit logs are there to verify everything the AI does.

The harder change is mental: accepting that the compliance work that defined the profession for decades is becoming commoditized, and the future belongs to firms that embrace automation as the foundation for higher-value advisory services.

Elizabeth even suggests bringing these concepts into accounting education to attract new talent. Currently, tax courses focus on rules and calculations rather than strategy. After all, accounting is still “the language of business,” as Elizabeth was told as an undergraduate. The difference is that AI can now handle the grammar and spelling, freeing professionals to focus on telling the story.

The transformation won’t be easy, but it’s not optional. As Elizabeth learned when she left the profession out of frustration with mundane tasks, talented people won’t stick around if the work doesn’t engage them. The good news is that automation finally makes it possible to eliminate the drudgery and focus on what really matters: helping clients succeed.

Listen to the full conversation with Blake, Elizabeth, and Kirat for more insights on preparing your firm for the automated future of tax.

From OnlyFans Audits to AI Cheating Scandals: Inside Accounting’s Strangest Week Ever

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In episode 465 of The Accounting Podcast, hosts Blake Oliver and David Leary tackle one of the most bizarre unintended consequences of recent tax legislation: IRS agents may soon need to review OnlyFans content at work to determine if digital creators qualify for tax deductions. This absurd scenario perfectly captures the chaos unfolding as artificial intelligence and new regulations collide with traditional accounting practices.

The IRS’s Awkward New Job Requirement

The new “no tax on tips” deduction allows digital content creators to deduct up to $25,000 from their taxes. But conservative groups successfully lobbied to exclude “pornographic activity” from this benefit, leaving the IRS to determine what qualifies as pornography—a definition the Supreme Court has never clearly established.

“Are IRS agents going to have to sit in their offices at work and look at OnlyFans accounts and determine whether or not this content qualifies?” Blake asks. “Supreme Court Justice Potter Stewart famously said, ‘I know it when I see it.’ So that’s my question.”

The timing couldn’t be worse. The IRS just closed hardship telework requests, forcing employees back to the office while the agency faces a backlog of over 8,000 accommodation requests and has lost 25% of its workforce through voluntary separations this year.

David raises another complication: “If somebody did one video that got determined to be pornographic, do you lose the whole deduction or can you claim all the other days that you got tips?”

Tax professionals face their own dilemma. “Let’s say you get a client who says they want to claim the tips deduction, and they’re an online creator,” Blake explains. “Are you going to check out the content and decide whether it qualifies?”

When AI Meets Ethics—The KPMG Scandal

While the IRS grapples with content moderation, KPMG Australia is dealing with its own technology-related embarrassment. Multiple auditors were caught using AI and group chats to cheat on mandatory compliance training during 2023-2024. This happened after KPMG had already paid a $50 million fine for exam cheating from 2015-2020.

“AI is really good at taking these kinds of tests,” Blake notes. “Just copy paste all the questions into ChatGPT and you’ll pass in a heartbeat.”

The consequences were light: formal warnings for most, one verbal caution, and one person who left months later. The firm didn’t report the incident to regulators.

“They got fined $50 million for it before and then they just continued to do it,” David points out. “So the fines don’t work, obviously.”

The PCAOB is now warning it will closely scrutinize AI use in accounting firms. They’re particularly concerned about private equity-backed firms, fearing pressure for short-term results will compromise audit quality when combined with AI automation.

The Death of the Billable Hour

Beyond scandals, AI is reshaping how accounting firms operate and charge for their services. The billable hour, introduced in the early 1900s as a management tool and dominant since the 1960s, faces extinction.

“When AI can review thousands of contracts in minutes instead of weeks, charging for time spent becomes economically absurd,” writes Rita McGrath of Columbia Business School in the Wall Street Journal.

Blake experienced this transformation firsthand as a freelance bookkeeper. “I billed hourly for keying transactions into accounting software. I then figured out how to automate 90% of it. I had a choice: bill 80-90% fewer hours and lose all my revenue, or switch my clients to fixed fees and take ownership of the process.”

The efficiency gains are already here. Ramp has AI approvals handling 80-90% of transactions automatically. Xero’s new auto-reconcile feature uses AI to match transactions with high confidence. According to OpenAI’s survey of 9,000 workers, employees save an average of one hour daily using AI, with heavy users saving ten hours weekly.

But not every company succeeds at this transition. Pilot raised $118 million at a $1.2 billion valuation, betting it could automate bookkeeping and achieve software margins. Today, they have just 2,500 clients and recently launched a partner program to offload the labor they couldn’t eliminate.

“The fact that they’ve launched a partner program indicates they’re trying to push labor costs out of the company so they can be a software company,” Blake observes.

The irony isn’t lost on David. “They have this headline, ‘Tired of endless QuickBooks updates breaking your workflow.’ But the very first app they list in their integrations is QuickBooks. It’s built on QuickBooks.”

AI Writing Reports Nobody Trusts

Companies are racing to use AI for financial reporting even while harboring deep doubts about its reliability. Twenty-eight percent of financial executives already use generative AI for external reporting. ON Semiconductor’s AI writes entire sections of management discussion and analysis. Hewlett Packard Enterprise plans to use AI for first drafts of financial statements starting in January.

“Take financial statements, drop them into ChatGPT and ask for the narrative. It does a spectacular job,” Blake says. “Taking numbers and turning them into a story that non-accountants can understand, highlighting what’s important, it’s really good at that.”

Yet Harvard Business Review’s survey of 603 business leaders shows only 6% of companies trust AI for core business processes. Most limit AI to low-risk or supervised tasks.

“The work accountants do requires near 100% accuracy,” Blake explains. “Research shows AI achieves 80% accuracy at 30-minute tasks but 100% only for tasks taking a few minutes.”

Meanwhile, Meta’s creative accounting for its Hyperion data center—using complex structures to keep it off-balance sheet—shows human financial engineering still outpaces AI. As the Wall Street Journal called it, “Artificial intelligence, meet artificial accounting.”

What Comes Next

Interesting research is challenging assumptions about what drives audit quality. Studies show offices with less competition deliver better audits with fewer errors. “Competition pushes down fees, which incentivizes auditors to cut corners,” Blake explains.

Another study found audit teams with more women deliver higher quality at lower fees, but only in supportive environments with good work-life balance and female partners.

President Trump, meanwhile, claims tariff revenue will eliminate income tax entirely. “We’ve taken in literally trillions of dollars,” he stated, though actual tariff revenue was only $258 billion last year versus $2.7 trillion from income taxes.

“Doesn’t anybody prep him?” David wonders. “He just makes up numbers.”

The accounting profession is at a crossroads. Will accountants become the quality control layer ensuring AI meets professional standards? Or will they cling to outdated models until technology makes them irrelevant?

To hear Blake and David’s full discussion, including details about the new Trump IRA accounts for kids and Senator Jim Justice’s $5 million tax settlement, listen to episode 465 of The Accounting Podcast.

Your Excel Data Never Leaves Your Computer With This AI Automation Method

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While 58% of professionals have tried AI, only 17% use it regularly. Kyle Ashcraft sees opportunity in that gap.

In episode 108 of the Earmark Podcast, host Blake Oliver sits down with Kyle, a CPA who built Maxwell CPA Review and helped over 1,500 students pass their exams, for a live demonstration that might change how you think about Excel automation. Their conversation shows how any accounting professional can start automating their work in under an hour. No coding experience required.

The AI Gap Nobody’s Talking About

“The more advanced AI becomes, we can take one of two directions,” Kyle explains during the demonstration. “You can continually veer away from it, and the more that comes out, you step farther and farther away from it. Or you can make it a goal to learn, let’s say, one new tool a week.”

The problem isn’t that accountants don’t want to use AI. It’s that they don’t have dependable strategies for implementing it. Kyle describes the typical approach as, “Opening up ChatGPT, throwing in a spreadsheet, and then giving it a prompt and seeing what it comes up with. Sometimes like a Hail Mary, where you just want to see if it gives you an acceptable output.”

There are two major issues with this approach. First, it often takes multiple attempts to get the output you want because ChatGPT can’t read your mind. Second, and this is crucial for accountants, when you upload a spreadsheet to ChatGPT, “your Excel document is going directly to OpenAI. Your prompt is going to them, and the prompt that they output to you is going to them as well.”

This matters because OpenAI’s data retention practices are questionable at best. They’re currently in a lawsuit with The New York Times and required to permanently retain logs. No wonder 70% of accounting professionals cite data security as their primary concern with AI adoption.

Enter “Vibe Coding”: When Everyone Becomes a Developer

Kyle’s journey started with a challenge. Could someone with zero coding experience build something that traditionally required a development team?

Four months later, he had his answer. Using Cursor, ChatGPT, and Claude, he built a complete assessment platform that identifies students’ weakest areas, emails follow-up practice materials, and provides analytics dashboards for professors. All with no programming background whatsoever.

“This really shows it’s possible to not have any idea what the code itself is saying, but with clear communication and patience, you can accomplish things that would have been impossible just two years ago,” Kyle tells Blake.

This phenomenon has a name: vibe coding. It’s coding without being a coder, using everyday language to generate complex scripts. During the demonstration, Kyle shows how Cursor generates hundreds of lines of Python code based on simple English instructions. You don’t need to understand what those lines mean, you just need to know what you want to accomplish.

Kyle offers a metaphor that reframes the entire relationship with AI. “Picture it like an orchestra and a conductor. You’re the conductor. You are in control. You set the tempo. You set the vision of what you want to achieve. And it’s the orchestra that’s doing all of the hard work.”

“There’s this assumption that AI is going to eliminate a lot of work,” Blake observes. “But what we find in reality is that it shifts the work from doing to reviewing. So that job is not going away, but now we review the output and provide feedback.”

The Script Solution: Privacy and Reliability in One Package

During a live Q&A, one attendee asks the question on everyone’s mind: “When you load the project into Cursor and it shows you the Excel files, does this AI platform not retain that client data? How is this different than uploading the Excel into ChatGPT?”

Kyle’s answer reveals why scripts are game-changing for accounting work. “It does not retain this data because with this process, it created this Python script, which is just Python code. It’s offline. There’s no record of this script.”

Your Excel data never leaves your computer. Instead, AI creates a script—basically a recipe—that runs locally on your machine. Think of it this way: instead of handing your sensitive client data directly to an AI company, you’re asking AI to write you instructions. The AI writes the instructions based on your request, but it never sees your actual data.

Blake highlights another advantage: “When Cursor communicates with AI services like Claude, it does so through APIs that have zero data retention policies. That’s in stark contrast to the chat interfaces most people use.” As he explains, these companies want large enterprises to be comfortable, so API interactions have much stricter privacy protections.

But privacy is only half the equation. Scripts also solve the reliability problem. Blake shares a cautionary tale about a Big Four firm in Australia that had to refund a government contract because its AI-invented citations didn’t exist. “They send an entire report to the government, the government clicks on the links for it, and they don’t exist. It’s disastrous if you don’t actually review the output.”

When another attendee asks about the risk of hallucinations, Kyle explains why scripts are different: “You’re not having an AI model interact with the Excel information. You’re having this step-by-step script that says, ‘do an auto sum of column B.” The script uses Excel’s own functions, it just automates the clicking and typing you’d normally do manually.

This deterministic nature means the same script produces the same result every time. As Blake notes, “We can reuse the script we created, apply it to a new Excel file and get the same expected result without having to check everything over again.”

The Three-Part Formula That Makes It All Work

“Goal. Steps. Output.” With these three words, Kyle unlocks the secret to making AI do exactly what you want.

During the demonstration, he tackles three real-world Excel challenges that every accountant faces. First up: a messy data export with empty rows, headers in row three, 14 different date formats, and inconsistent spacing.

His prompt is elegantly simple:

  • Goal: Clean up this Excel file
  • Steps: Identify any inconsistent formatting. Add basic color and style. Analyze each column to better understand its format
  • Output: A new Excel document

Within moments, Cursor generates hundreds of lines of code. The result is a perfectly formatted table with consistent dates, proper headers, and professional styling. “It looks clean, smooth, with some nice shading,” Kyle observes. “It’s just easier to look at overall.”

When Blake asks whether Cursor can do its own checksum, they quickly add both files and ask Cursor to verify nothing was lost. The response: “All 20 transactions are present. All amounts were correctly processed. The sum of $19,000 is maintained.”

The second demonstration scales up the complexity. Kyle shows a General Ledger detail export with 400 rows spanning every account. Manually organizing this would require hours of filtering and copying. His structured prompt creates a summary tab showing account codes, transaction counts, debits, credits, and net amounts, plus individual tabs for each account’s detailed transactions.

“Instead of going to each account in your accounting system and exporting the GL individually, just export all the accounts together and then run this through,” Kyle suggests. What might take an hour completes in under a minute.

The third example addresses bank reconciliation, comparing statements to GL detail to find discrepancies. No more scrolling row by row. The automation identifies matching items, missing transactions, and differences between the files instantly.

Blake connects the dots for viewers. “I picture our listeners who work with some older ERP systems that don’t have very customizable reporting and who are doing a lot of manual formatting. Now you can automate that recurring task every month or every week.”

Getting Started Is Simpler Than You Think

The transformation begins with two downloads that take five minutes each. First, download Python, then download Cursor. Start with the free tier. Kyle uses the $20 monthly plan for daily use, but the free version is powerful enough to begin.

When you first open Cursor, it will ask you to install some packages like “pandas” for Excel interaction. Kyle recommends, “Click the dropdown button and choose ‘run everything’ so you trust the platform. It’s very reliable, and then anytime it needs a new required package, it automatically downloads that.”

Don’t forget to adjust your privacy settings. In Cursor’s settings menu, scroll to privacy options and select “privacy mode” with “no training data used.” This ensures your work isn’t incorporated into AI training datasets.

The key to success is to start small and be patient. “Try it with some information that is not private at all, maybe one of your own documents,” Kyle suggests. “The more patience I have, the more I follow up on that review step by giving it tiny pieces of feedback, the more it improves over time.”

Blake adds perspective on managing expectations: “When I try new tech, 80% of what I do doesn’t have a payoff, but then the 20% has a huge payoff. So don’t get discouraged if your first few attempts fail.”

For recurring tasks, the payoff compounds quickly. “Private roles always have month-end closing. Public end clients always need amortization and depreciation schedules for their notes,” Kyle notes. Even creating client checklists based on prior year information becomes a candidate for automation.

The Bottom Line: Your Move

The tools are accessible. The knowledge is available. As Kyle demonstrated with live examples, you can go from messy data to polished reports in minutes using nothing more than clear English instructions.

So, will you step away from AI as it advances, or learn one new tool at a time and stay connected to this movement? Because as Kyle reminds us, “It’s not going to go away. It’s just going to become more integrated into everyday work culture.”

To hear these demonstrations in action, listen to the full episode at podcast.earmarkcpe.com/108. Kyle has also offered to help early adopters, so reach out to him at kyle@maxwellcpa.com with questions or to brainstorm how this could apply to your specific work situation.

As Kyle challenges at the session’s close, “Try your first task with it this week and see how it works for you.” The revolution in accounting work is here, waiting for those bold enough to embrace it.

The Week Accounting Lost Its Professional Status and Dating Apps Became Job Sites

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After 128 years as licensed professionals, accountants just got told they’re not in the same league as doctors and lawyers—at least according to the Department of Education. In this episode of The Accounting Podcast, hosts Blake Oliver and David Leary dig into what this means for the profession, along with news about AI taking over audits, big firms making embarrassing mistakes, and job seekers using dating apps to find work.

The Professional Status Problem

The Department of Education wants to strip accounting of its professional degree status, which would slash federal loan limits from $50,000 to $20,500 per year starting in 2026. This hits graduate accounting programs hard, especially when states are already rethinking the 150-hour CPA requirement.

The proposal came from the One Big Beautiful Bill Act, but as David points out, Congress didn’t actually specify which professions should qualify. “Isn’t Trump supposed to get rid of the deep state where these government agencies just make up the rules?” David asks. Instead, bureaucrats decided that medicine, dentistry, and law are professional programs, but accounting, nursing, architecture, and education aren’t.

AICPA President and CEO Mark Koziel calls this lack of recognition “common sense” to oppose, while NASBA President Daniel Dustin reminds everyone that CPAs have been licensed professionals since 1896—longer than many professions that made the cut.

During the livestream, one viewer made an interesting point: “If we are no longer professionals, that means we are entitled to overtime.” Blake expanded on this, noting that the Fair Labor Standards Act exempts professionals from overtime. Without that professional designation, Big Four firms might suddenly face huge labor costs for all those 50-60 hour weeks their CPAs work.

Students already questioning whether becoming a CPA is worth it will think twice when federal loan support drops by more than half.

AI Is Coming Fast, But Not Always Successfully

While regulators debate whether accountants are professionals, tech companies are betting billions on replacing them with AI. PwC announced it will have “full end-to-end AI automation for audits by 2026.” That’s not some far-off dream; they’re already using tools that auto-populate audit planning documents and analyze walkthroughs.

But the AI revolution has had some embarrassing failures. Deloitte produced a $1.6 million healthcare report for the Canadian government that included completely made-up academic citations. One fake paper was titled “The Cost Effectiveness of Local Recruitment and Retention Strategies for Health Workers in Canada,” which doesn’t exist. This came after a similar mess in Australia with over 20 fake citations.

“Deloitte’s website markets its AI and data teams,” David notes. “Deloitte should hire that team before they do any more AI work with clients.” The irony is that Deloitte sells itself as the company that helps others avoid exactly these AI mistakes.

Meanwhile, EY’s new leader Dante D’Egidio got promoted after cutting their audit deficiency rate from 46% to 9%. How? They fired clients, built support teams, and invested in technology. As Blake explains, “EY had too many clients and their staff and managers and partners were overworked. Quality went down.”

The OpenAI connection to accounting firms gets even stranger. OpenAI is investing in Thrive Capital, which owns Crete Professionals Alliance, a company that buys accounting firms and forces them to use AI technology. OpenAI will even send teams to work inside these firms. “This would be like Intuit buying accounting firms and making them buy QuickBooks,” David says. “People would lose their minds if that happened.”

The Job Market Reality Check

The economic news isn’t great. Small businesses lost 120,000 jobs in November while large companies only added 39,000. Three in ten companies plan to lay people off during the holidays. Americans are planning to spend $73 less on holiday shopping this year.

But there’s useful advice for job seekers. According to data Blake shared, 54% of workers got their current job through personal connections, while only 13% succeeded through job boards. Yet 60% of job seekers don’t use their network at all, mainly due to lack of confidence.

Here’s where it gets interesting: one-third of dating app users are now swiping for jobs, not dates. And it works: 88% made professional connections and 37% got job offers. “LinkedIn is the red water,” David observes. “You can’t stand out there. But if you say on a dating site, ‘Hey, I’m looking for a job,’ there’s nobody competing for jobs there.”

What’s Actually Changing

Beyond the headlines, several big shifts are happening. Xero is raising prices on developers specifically to stop AI models from accessing data. They’re banning developers from using their API to train machine learning models, the same thing Intuit did with QuickBooks.

Speaking of Intuit, the company now shares small business data with The Trade Desk, one of the world’s largest advertising networks. This lets advertisers target small businesses using QuickBooks data. “Your small business client data is now being sold to third party advertising networks,” David warns.

The Department of Government Efficiency (DOGE) quietly disbanded after cutting 300,000 government positions. They haven’t posted anything new since early October, and David suspects “Republicans are cutting away some of this bad press stuff.”

Looking Ahead

The hosts make some predictions for the coming year. David expects a partnership between OpenAI and the AICPA or CPA Academy by 2026 because “there’s just too much money” in CPE and they’re going to go after some of it. He also shared advice for young people: make a podcast interviewing professionals in your desired field. “If you’re in high school and want to become a dentist, make a podcast where you interview dentists. Even if nobody listens to your podcast, when you’re all said and done, you’ll know 40 dentists. And when you finish school, you probably have a good chance of getting a job.”

The accounting profession faces real challenges, from regulatory dismissal to AI automation to economic headwinds. But as Blake and David demonstrate each week, staying informed and adapting creatively matters more than protecting old definitions of professionalism.

Want to hear the full discussion, including details about PCAOB changes, tariff impacts, and why accounting firms might have to start paying overtime? Listen to the complete episode of The Accounting Podcast. You can even earn free CPE through the Earmark app while you listen.

Intuit Pays OpenAI $100 Million While Tech Giants Manipulate Profits Through Creative Depreciation

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Blake Oliver and David Leary are back with The Accounting Podcast after Blake’s bout with what he calls “the worst cold ever” from a Las Vegas conference. Their return episode tackles some major developments in accounting tech, including a massive deal between Intuit and OpenAI that could change how millions interact with their financial data.

AI Adoption Hits a Tipping Point

Blake kicked off the discussion by sharing insights from his recent presentation at the American Society of Cost Segregation Professionals conference. One striking statistic: 55% of US adults have now used generative AI like ChatGPT, up from 45% just a year ago.

“We passed the midpoint,” Blake noted. “The majority of American adults have now used ChatGPT.”

But here’s what should worry accounting professionals: A new survey reported by CPA Practice Advisor found that 10% of adults acted on AI tax guidance within the last 30 days, while 21% followed AI crypto advice. The problem, as Blake warns, is “AI gets complex tax questions wrong up to 50% of the time.”

Intuit Bets Big on OpenAI

The headline news centers on Intuit’s announcement that it will pay OpenAI $100 million per year in a multi-year deal. Soon, ChatGPT users will be able to connect their TurboTax, QuickBooks, Credit Karma, and Mailchimp accounts directly within ChatGPT.

David found this move puzzling given Intuit’s recent behavior: “Intuit just raised prices on developers to pull data from QBO to stop some of these AI plays from sucking all the QBO data. Now it’s the complete opposite. They’re going to pay somebody else to suck that data out.”

According to Intuit’s CFO, the real motivation is customer acquisition. They want to convert OpenAI’s 800 million weekly active users into Intuit customers. Blake sees this as part of a larger trend where ChatGPT becomes “the primary place where you work” by connecting to all your apps and data.

This raises questions for accounting firms. As David wondered aloud, “Are accountants going to panic when clients connect to ChatGPT?” The hosts noted that details about data privacy and whether users need to explicitly authorize these connections remain unclear.

The Two-Minute Rule for AI Accuracy

Blake’s presentation revealed a crucial insight about AI’s current limitations. Through his research, he found that AI can handle tasks with near 100% accuracy, but only if those tasks would take a human about two to five minutes to complete.

“The longer the task, the less accurate it gets,” Blake explained. For example, for tasks taking 30 minutes, accuracy drops to 80%. For two-hour tasks, it’s only 50% accurate. That’s essentially a coin flip.

This has real consequences. According to the survey data Blake cited, 19% of Americans have already lost over $100 following bad AI advice. Yet 27% still believe AI could provide all the financial guidance they need.

One bright spot came from a viewer comment during the live show. An intern at a mid-size firm shared that they spent 10% of their work time using Gen AI and were the only intern offered a permanent position due to superior productivity. “People will be judged against coworkers who are using AI and who are more productive,” David observed.

The good news is that AI’s capabilities are doubling every seven months. “If AI can do tasks with near 100% accuracy that are two minutes long right now, then in seven months it’ll be four, and in 14 months it’ll be eight,” he calculated.

Michael Burry Spots Another “Big Short”

Perhaps the most alarming story involves Michael Burry, the investor and hedge fund manager who famously predicted the 2008 mortgage crisis. His fund is now shorting AI companies like Palantir and Nvidia based on what he sees as widespread accounting manipulation.

The issue is tech giants extending depreciation schedules for their AI infrastructure to make their profits look better. Blake broke down the numbers:

  • Alphabet extended depreciation from three to five years, adding $3 billion to profits
  • Microsoft went from four to six years, gaining $2.7 billion
  • Meta moved from four to five years for a $1.5 billion boost
  • Amazon made similar changes

“Let’s say you have a $10 billion investment in AI chips,” Blake explained. “If your useful life is two years, you’re recognizing $5 billion of expense each year. But if you make that five years, now it’s only $2 billion of expense. That’s a $3 billion difference.”

David drew parallels to the 2008 crisis. “It’s like the Big Short when you roll up these bad loans into a different one. Now it looks good, but it’s really 300 bad loans. It’s totally the Big Short all over again.”

The hosts pointed out that the entire tech industry made these changes starting in 2022, suggesting coordinated “earnings management.” Yet auditors and regulators haven’t pushed back. “If everybody’s doing it, that makes it seem more reasonable,” Blake noted with frustration.

Other Notable Updates

The episode covered several other important developments:

Rippling vs. Deel Drama

New court documents reveal that Deel allegedly paid a corporate spy through the COO’s wife’s bank account. Rippling published bank statements showing the money trail. Deel’s corporate account sent funds to the COO’s wife, who forwarded the exact amount to the alleged spy just 56 seconds later.

Alternative Pathways Progress

New Jersey unexpectedly passed alternative pathways legislation for CPAs. If the Senate approves, it will become the 24th state to offer alternatives to the traditional 150-hour requirement. That’s nearly half the states in just 11 months.

PCAOB Admission

In a surprising interview, the PCAOB’s acting chair revealed that after 20 years, they’ve never formally defined what “audit quality” actually means. Blake couldn’t believe it: “Isn’t that their job?”

Ancient Accounting

In a lighter moment, David shared news about researchers discovering what might be an ancient general ledger in Peru: 5,200 holes arranged in patterns that may represent an accounting system used by the Inca.

Looking Ahead

The accounting profession is in the eye of a perfect storm. Clients trust AI tools despite high error rates, software companies scramble to partner with AI platforms rather than compete, and tech giants manipulate their books to show AI-driven profits that may not materialize.

Blake offers some practical advice for accounting professionals: identify those two-to-five minute tasks where AI excels and use it there. But also prepare to clean up messes from clients who trusted AI with complex questions it can’t reliably answer.

“Good news for tax pros,” Blake concluded with dark humor. “You’re going to be untangling tax messes for years thanks to bad AI tax advice.”

The full episode of The Accounting Podcast includes additional details about these stories and the hosts’ unfiltered analysis of what these trends mean for the profession. As David noted about their nearly broken seven-year weekly recording streak, consistency matters—especially when the industry is changing this fast.