Data-labeling startup Surge Labs Inc. is hoping to capitalize on the recent customer exodus at its main rival Scale AI Inc., and to do that it’s reportedly seeking up to $1 billion in venture capital funding.

That’s according to an exclusive report by Reuters, which cites anonymous sources as saying Surge AI has hired advisers to help out with the first capital raise in its history, targeting a $15 billion valuation. The sources added that the funding would be a mix of primary and secondary capital that provides liquidity for employees. The talks are still in their early phases, and the final amount raised could yet be much higher, the report added.

San Francisco-based Surge AI was founded and bootstrapped by the former Google LLC and Meta Platforms Inc. artificial intelligence engineer Edwin Chen back in 2020. Despite not attracting any funding itself, it has already grown its annual revenue to more than $1 billion and is also said to be profitable.

That suggests the company may have a market lead over the better-known data labeling firm Scale AI, which generated $870 million in sales last year and was recently acquired by Meta Platforms in a $14.3 billion deal. Upon conclusion of that deal, Scale AI was valued at almost $29 billion, even though founder and Chief Executive Alexandr Wong left the company to head up Meta’s new AI Superintelligence Labs.

Surge AI has reportedly benefited from Scale AI’s decision to partner with Meta, grabbing a number of the latter’s former customers, including OpenAI and Google. Those companies are worried that if they continue using Scale AI’s datasets, they could ultimately end up exposing their AI research and priorities to Meta, hence the need for an alternative provider.

For its part, Scale AI says its customer base remains strong despite the Meta deal, and it insists it’s fully committed to protecting their data. But the bigger players seemingly aren’t convinced.

In any case, Surge AI’s offering seems to be every bit as good as the services provided by Scale AI. Since being founded just over five years ago, it has largely operated under the radar, with its founder Chen shying away from the limelight, in stark contrast to the publicity-seeking Wong, who has made a concerted effort to cozy up to the White House. The company itself has barely made any headlines either, and even its website remains noticeably threadbare, with little detail provided on the services it offers.

Yet Reuters says it has still managed to establish a reputation among major AI players for its premium, high-end data labeling services, which are also used by Anthropic PBC.

Surge AI is betting on the need for even more nuanced and meticulously-labeled data in future due to the rise of reinforcement learning techniques in AI development. It creates these datasets by drawing on a network of highly trained contractors, as opposed to the low-wage labor that makes up Scale AI’s primary workforce.

That said, analyst Holger Mueller of Constellation Research Inc. told SiliconANGLE that there’s likely lots of room for improvement in Surge AI’s services, and the reports that it’s seeking a billion dollars in capital underscore how bootstrapped startups can only go so far before they need to obtain more financial muscle.

“While bootstrappers need everybody’s respect, they often end up short-changed in the quality of their outcomes,” Mueller noted. “Surge AI is the latest example, and although it’s likely larger than Scale AI, it still needs to tap the markets to fund its mission. So it makes you wonder why it didn’t go down the venture capital route in the first place.”

The startup is trying to entice investors with claims that it offers more concrete assurances in terms of data protection and a broader roster of customers, who prefer its pay-as-you-grow business model.

However, Surge AI will also need to show it’s able to absorb the $1 billion capital it’s seeking and use it to accelerate its growth, which won’t be easy amid the rising competition it faces from new entrants and incumbent data labelers. At the same time, it will need to explain how it intends to remain relevant, given that its reliance on human labor puts it at risk of automation as AI advances.

Image: SiliconANGLE/Dreamina