Fintech

VCs are supposed to be patient money. What if they need cash now?

Pipe, which started out helping venture-backed SaaS companies turn future revenues into capital, is now helping investors too.

Harry Hurst, CEO of Pipe

Pipe co-CEO Harry Hurst has a deal for VCs.

Photo: Pipe

Pipe built a $2 billion business by turning companies' ARR into cash advances with the promise of repayment down the line from a growing stream of customer payments. But in a deal with AngelList, Pipe is targeting a new kind of business: venture firms.

While venture firms don't have the same notion of "revenue" that you'd see at a software startup, venture firms do get management fees, paid by their LPs over time, to cover the cost of the firm's operations. Now Pipe is giving general partners the chance to take more of those management fees upfront.

"For a GP that draws a management fee in cash over a number of years, Pipe provides a way for them to access that capital upfront for a number of use cases, for example, but not limited to, diversifying their personal investments into spaces outside of their fund's investment thesis, using the capital for a down payment on a house and investing into operating expenses like staffing to scale up for the next fund," Pipe co-CEO Harry Hurst wrote in an email to Protocol.

Venture funds typically operate under a "2 and 20" fee structure where firms receive 2% of the assets under management as an operations fee and 20% of the profits on exits. For larger funds or well-established firms with multiple funds, that 2% management fee can be plenty of money. But smaller funds and emerging managers have had to get creative on how to get more funding upfront to cover the cost of launching operations.

Some smaller funds, for example, charge 3% fees and front-load fees to offset initial costs. In a much more unconventional path, both Backstage Capital and Earnest Capital conducted crowdfunding campaigns to help raise money for operations in exchange for future profits.

The Pipe-AngelList deal will provide venture funds with another option: GPs can pay a small fee to Pipe and get up to four years of management fees in advance. It's not a loan and there's no interest, according to AngelList. (Revenue advances or merchant cash advances, as products like this are known, are generally viewed as the sale of future revenue and hence a commercial transaction versus a more heavily regulated loan.)

For Pipe, it's a push beyond its original market of SaaS companies. While Pipe focused on that market at first, Hurst said that more than 50% of its business now has evolved into other categories: direct-to-consumer companies, streaming services and even service-based businesses like pest control and gyms. While venture fees may not be a traditional "revenue" stream, for Pipe, it's another flow of cash it can tap in to.

Fintech

Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. Strangelove." That’s because he wants you — yes, you — to read them.

The ways Zia Faruqui (right) has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Photo: Carolyn Van Houten/The Washington Post via Getty Images

“Cryptocurrency and related software analytics tools are ‘The wave of the future, Dude. One hundred percent electronic.’”

That’s not a quote from "The Big Lebowski" — at least, not directly. It’s a quote from a Washington, D.C., district court memorandum opinion on the role cryptocurrency analytics tools can play in government investigations. The author is Magistrate Judge Zia Faruqui.

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Veronica Irwin

Veronica Irwin (@vronirwin) is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc.

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FTA
The Financial Technology Association (FTA) represents industry leaders shaping the future of finance. We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation.
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Photo: Noah Berger/Getty Images for Amazon Web Services

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Donna Goodison

Donna Goodison (@dgoodison) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers.

Image: Protocol

We launched Protocol in February 2020 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

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Bennett Richardson ( @bennettrich) is the president of Protocol. Prior to joining Protocol in 2019, Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University.

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Why large enterprises struggle to find suitable platforms for MLops

As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

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Photo: artpartner-images via Getty Images

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.

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