Why AI Won't Replace Accountants — It'll Make Them Richer, Says Basis CEO Matt Harpe
On The Upstarts Podcast, the $1.15B startup talks an accountant shortage, outcome-based pricing, hiring AI talent in NYC, and staying ahead of the big labs.
When Basis co-founder Matt Harpe tells me he wants to build the best home for applied AI talent in New York City at his startup, I’m immediately skeptical.
Harpe’s startup, Basis, serves a specialized customer base that I don’t associate with the bleeding edge of technology: accountants.
Unless there’s some untapped talent pool of accountants turned machine learning experts that Upstarts is unaware of, why would someone choose working at Basis, despite its recent $1.15 billion valuation, versus another local option?
The vast majority of Basis employees don’t have accounting backgrounds, Harpe confirms. “There are probably 5% to 10% of the company whose parents are accountants, which I think is actually an interesting observation, but that’s a relatively small portion of people,” he admits.
Instead, your more classic tech-minded employees are joining Basis because of how it builds its tools, not so much who for.
Most applied AI companies started with a chatbot, or a text box for inputting a prompt, Harpe argues. When he and co-founder Mitchell Troyanovsky launched Basis in 2023, they focused on longer-running agentic systems by necessity. “It was required to move the needle in accounting, so we were forced to,” he says.
Now, he believes Basis has a head start as agents become the topic du jour among peers: “For a lot of people who are really interested in pushing the bounds of what’s possible within the broader applied AI and ML [machine learning] space, Basis is a pretty exciting place to work.”
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In February, Basis announced a $100 million Series B led by Accel, with GV, Khosla Ventures and a host of individual investors from former Goldman Sachs boss Lloyd Blankfein to the CEOs of Box, Quora and Hugging Face, as well as AI luminaries like Google’s Jeff Dean and OpenAI’s Noam Brown.
Basis works with at least one-third of the Top 25 accounting firms in the U.S., helping them manage workflows across processes like taxes and auditing, and returning work for the human accountant to review.
Harpe – who got the idea while studying healthcare at Boston Consulting Group, where he learned accounting was a major bottleneck – isn’t an accountant himself, nor is his co-founder. But similar to Lassie, the startup we covered building software for dentist offices, they shadowed them, starting with Troyanovsky’s pharmacy-owner mom’s own accountant, to learn their problems.
And far from replacing human accountants, Harpe insists that his startup is helping solve a crisis in this field: there aren’t enough people. There’s enough accounting work in the U.S. for 30 million specialists, he says; we make do today with just 3 million, and the number is declining.
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On this episode of The Upstarts Podcast, Harpe talks about how Basis got a head start by shunning a chatbot approach; the ins and outs of outcome-based pricing, and why it won’t work for all; and how he believes he can increase accountant salaries, not depress them.
Plus, he shares his Upstart Moment: turning down short-term revenue by not charging for what he considered an unfinished product early-on.
Our key takeaways from the show for busy builders, and ‘Alex’s take,’ are below.
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A 3-part pitch to win customers
When Basis got started, few accounting firms seemed excited about adding AI tools for the sake of it, says Harpe.
That part’s gotten easier since, but Basis recruited early customers by leading with a pitch to help solve their staffing challenges. Coming out of the pandemic, high attrition rates were a concern. Large firms had invested in offshoring some work, which had its own tradeoffs.
Basis’s strategy: identify the top one or two issues for a firm leader, then promise to help across three areas:
Efficiency: automating some manual processes
Retention: less busywork for employees would make them more likely to stay
Growth: people trained on such tech would be more likely to advance, creating a hiring story for future candidates
“That got people really excited. We were able to show that we could actually make an aggregate impact on the entire practice, change its efficiency profile, not just pick one niche workflow that wouldn’t have any total impact.”
On a practical level, Basis being able to pull documents, flag exceptions and clearly present the results for review was the demo magic. “These are processes that might otherwise take 10 hours, even 100 hours, depending on the volume, that can be done in a matter of minutes, with extremely high levels of accuracy.”
The trick there: showing their work enough that the accountant could easily verify it, and feel confident that the process had run correctly, so they didn’t have to retrace the agent’s steps.
Trade-offs of outcome-based pricing
While Basis resisted calls from investors and some customers to build a short-term chatbot for basic questions, the startup also chose not to prioritize generating revenue from its tools until it felt ready to align pricing against real outcomes.
“We never had a seat-based model for our core product,” Harpe says. No token or consumption-based model, either.
Instead, Basis “held out” until it could deliver outcomes that it would charge for, then “put gas on the fire.”
“ One of the big problems with outcome pricing is for some startups, it’s not easy to prove exactly what the value is that you’re generating, right? Like, you could say, ‘We’re helping with a few things indirectly,’ but maybe you can’t map to dollars saved or new money coming in for the customer.”
Because accountants typically get a predictable monthly or yearly amount for their client services, Basis has an easier time mapping where its time saved on an account can contribute to that revenue.
“Until the AI is actually good enough to do a meaningful portion of that [work], you can’t really put that model in place.
Whereas if you just want to put a seat-based model in place, you can probably do that sooner, because people think, ‘Okay, this is one person. You know, they’re going to save this much time,’ and the ROI story is relatively clear.”
When I ask Harpe if he expects more companies to emulate the outcome model, he’s skeptical. “Honestly, a lot of the direction things have gone is maybe not in the direction we’ve gone,” he says.
Instead, many AI startups are using a token-based, consumption model, adding a surcharge to their own model costs that they pass to a customer.
“You’re punting the question of figuring out ROI and making a case about how it can be attributed to the customer,” Harpe says.
Inside Basis, Harpe notes that the startup tracks outcomes of coding tools to decide how much to spend on them, but volume of activity alone is not enough.
“You can maybe look at per-person productivity, but I think it’s a lot less clean. So I think that puts you in a slightly more difficult position, where you’re essentially pegged to this underlying resource that can be swapped in and out. Maybe, as the cost of the models goes down dramatically, that is a more difficult business to defend, and you are truly wrapping an atomic token, instead of packaging an outcome that you want to continue.”
Bottom line: Basis is happy where it is focused on outcomes. And it seems nice work, if you can get it.
Alex’s take: Find your ‘Goldilocks’ vertical
A major theme of this season of the show has been how startups can ride the wave of the big AI labs, without getting swept away.
Among the 10 startups we featured on the show this season:
Orchid in fertility might not have to worry about ChatGPT or Claude’s health units yet, nor TBC in its work with neuron-trained models, nor Radical AI in materials science.
But at Vanta in compliance, Mutiny in sales software, Writer in enterprise AI, Blitzy in corporate-scale code projects, Pigment in ERP software, and Abridge in healthcare systems, that dance is now part of the story these companies tell to justify continued hiring, investor interest, and long-term viability in the AI era.
We’ve covered this trend extensively in our articles, too, from Handshake pivoting to data creation and labeling to an agentic pivot at Vivun, and a push into AI native services at Gainsight and Remote. It’s in the backdrop of many of other conversations, too, from Wispr launching a new interface lab to Zoom’s CEO plotting an updated product future.
Most dramatically, there was our story about Figma and its board kerfuffle following the Claude Design launch.
Basis also fits neatly into this conversation. It’s easy to imagine Anthropic and OpenAI launching Claude Audit (Claudit?) or ChatGPT for Accountants. And Harpe agrees that the question of defensibility against the labs was one of two big existential ones he addressed to his company as they got started; the other was how they’d continue in a post-AGI world.
His answer now: he fully expects the labs to launch accounting and finance features. “At least for basic productivity things, there are a lot of merits to having a ChatGPT subscription, or something like it,” he says.
What helps him sleep at night: the labs have “successfully attacked” capabilities one degree removed from their core competency so far, and accounting isn’t that.
“I think all companies can only focus on so many things, no matter how much money you have. Maybe that’s a lesson that sometimes the labs have learned recently, which is that if you try to go in too many directions, you start losing your core thing.”
Documents, text generation, Excel and PowerPoint, internet research – all of these are easy pickings, Harpe says. Even differentiated, deeper features in this areas will face pricing pressure. (This is where Writer CEO May Habib’s comments on the show about how Writer trained its own models, and Abridge CEO Shiv Rao on how his team uses internal AI tools 40% to 60% of the time to keep down costs, resonate.)
“Accounting has never been a text in, text out thing,” Harpe argues. This is not a Microsoft Word, or Google Docs loving crowd. The sign-offs and permissions aren’t easy to do with a chatbot, he adds, a line that echoes the arguments of one of our Season 1 guests, Winston Weinberg at Harvey.
How far away your business is from a text box could help determine its long-term value, Harpe goes on to say, along with the usual need for good execution.
I mostly agree with Harpe. Like with Henry AI, the commercial real estate-focused startup we covered earlier this week, I’d rather find a wonky category that has historically not been exciting or seemingly big enough for founders and investors, and test the limits of growing its category.
Better to sell such a business for single digit billions at a best-case scenario than get gobbled up. But you have to raise relatively sane amounts of capital, like Basis’s previous $100 million round, for that to be a viable exit route.
The strongest point here is that Basis isn’t selling AI or tokens. It’s selling completed accounting work that humans can verify. That outcome-based approach feels far more durable than another chatbot with a monthly seat price.