Those Big AI Funding Rounds May Be Making AI Companies Distracted—While Making Nvidia and Growth Investors Rich (At Least for Now)

Artificial intelligence is supposed to bring massive efficiencies to companies across industries. So why are so many of the large AI-model companies raising huge amounts of capital that threaten to make them operate inefficiently?

Decades of experience in tech and venture capital have convinced me that raising too much money often makes companies distracted from the core task of company-building and can provide a disincentive for them to run lean and get profitable faster. But these days, many of the market’s most high-profile, foundational AI companies are raising truly eye-popping—I would say unprecedented—sums: Last month, OpenAI raised $6.6 billion from investors including Microsoft and Nvidia, the AI chipmaker.

According to a recent Crunchbase analysis, more than 20 private, AI-focused startups have raised more than $1 billion each in equity financing in the last couple of years. Five have raised over $6 billion total. The AI-spending frenzy is so great that there are now very real worries about the strain this new AI activity will place on the nation’s rickety power grid.

To be sure, AI is a capital-intensive business, unlike many of the software businesses some venture capitalists have traditionally funded; OpenAI and Anthropic need lots of cash (so they say) to fund the technology infrastructure required to train their powerful models and get them into production. AI is, no doubt, a huge technology revolution, not dissimilar to the platform shifts we saw in the last decades related to the rise of mobile computing/the iPhone and the cloud. (See Battery’s 2024 “State of the OpenCloud” report for more on this.)

Regardless, this orgy of spending is a stark contrast to the more-thrifty strategies of many of the B2B companies that managed to successfully stage IPOs in the 2010-2020 period—and it could raise questions about the long-term viability of today’s highly unprofitable AI titans, as well as who exactly benefits from their multi-billion-dollar fundraising rounds. My hunch is that it may not be the AI companies themselves.

A more-frugal era for B2B tech

Most of those frugal B2B companies from a decade or so ago, such as Datadog*, Atlassian and ServiceNow, operated quite differently. They often raised small rounds (or stayed bootstrapped, in the case of Atlassian) initially to focus on customer validation. Then, by the time they raised more financing, many had been fairly de-risked and were able to generate sizable returns for investors who backed them, even in later-stage rounds.

Of the 10 biggest B2B-tech IPOs of venture-backed companies in the 2010s, according to our own analysis using Pitchbook data, the median amount of funding raised was $554 million. And some of those companies raised much less: Datadog* raised just $148 million before its IPO, and Zoom $161 million, according to Pitchbook.

Contrast that to the current AI boom. Today, many of these hot companies seem to be relying on intense fundraising, instead of hard, company-building work, to subsidize growth—and then funneling all that investor cash into more GPUs.

The inefficiency of raising too much money

Overall, here’s why I think this type of overfunding, in general, is bad for companies and founders:

• The notion that raising mega-funding rounds distances you from your competition, and gives you a leg up, is a head fake. You still need a product that customers love and sales teams that can sell it. Raising a large round can make you complacent, rather than being paranoid about product usage and sales efficiency. Case in point, in my view: Lacework, the cybersecurity company that raised over $1.3 billion in funding but likely sold for only $200 million to $230 million, according to a Forrester Research estimate. I believe the company burned way too much cash and couldn’t reverse its bad spending habits. There are plenty of other examples, like WeWork, which crashed so badly after raising more than $13 billion that there was a whole television show about it.

• Founders think that large rounds help their brand, and, in turn, help recruit the best people. That can sometimes be true, but often those same recruits are too focused on their stock options and may lose interest if their strike price is too high, which could undercut their eventual profits. This can make them more likely to leave at the first sign of trouble rather than stay focused on the end-user problem their company is addressing and creating delightful products.

• Raising lots of money helps you hire extensive go-to-market resources, like sales teams. It’s certainly a virtue if your product-market fit is strong, but in most cases, hiring too many salespeople before your product is truly ready for prime time can take your focus away from selling to your ideal customer profile. Marketing will drive more leads that don’t ultimately convert, while your competitors stay nimble and outexecute you.

Meanwhile, this cash train does have its beneficiaries. These include some mega-sized investment funds which have convinced their investors they need to raise enormous vehicles to fund the new AI giants. And raising huge funds, of course, results in large management fees. Many of these same investors have high marks from previous mega rounds that haven’t been realized, and they stand to benefit from a large fund before the last one gets marked to market.

In addition, the founders of some of these companies often want to sell secondary shares as part of new funding rounds, which is fine in moderation but can backfire if founders become demotivated. And of course, Nvidia makes out well when AI companies raise cash to buy GPUs. I think a better strategy might be for these AI companies to lease back computing power from AWS and other big cloud providers, which would be cheaper and more flexible. Why buy a Ferrari to drop your kids at school if you can send them in a cheaper UberX instead?

It’s not always bad, as a private tech company, to raise a lot of money. Once you’ve achieved strong product-market fit, are a clear leader in your market and have more demand for your product than you can satisfy, raising money “ahead of the curve” can make sense. But my advice is to separate your ability to raise money from your desire to spend it, even if you’re on the cutting edge of AI. Think twice about whether you really need that next $100 million in funding; you might be better off focusing on your next 100 customers.

The information contained in this market commentary is based solely on the opinion of Dharmesh Thakker, and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as legal tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity. The views expressed here are solely those of the authors.

The information above may contain projections or other forward-looking statements regarding future events or expectations. Predictions, opinions and other information discussed in this publication are subject to change continually and without notice of any kind and may no longer be true after the date indicated. Battery Ventures assumes no duty to and does not undertake to update forward-looking statements. Companies mentioned are for illustrative purposes only. *Denotes a Battery portfolio company. For a full list of all Battery investments, click here.