Insiders and VCs expect a wave of machine learning mergers and acquisitions
- As corporate valuations plummet amid the market downturn, insiders and VCs are predicting a wave of mergers and acquisitions.
- Machine learning startups in the “10 million dollar ARR club” are prime targets, they say.
- Data giant Snowflake says it is considering strategic acquisitions, which further signals a shake-up.
Company valuations are being massively reduced as the market deteriorates, with many fundraising increases and layoffs. Insiders and investors are now expecting a big wave of acquisitions, and many machine learning startups are prime targets.
Some of the startups most likely to be scooped up are part of what investors and insiders sometimes refer to as the “symbolic $10 million ARR club,” which refers to companies that have nabbed a few large initial customers but don’t have not yet entered the mainstream. . With an impending downturn, their customers may seek to cut costs quickly – which would come at the expense of startups that rely on their offerings.
As with most emerging technologies, new machine learning startups usually find a sweet spot in the rapidly changing field and create a product like a wedge in customers. Those who succeed can then launch additional products and gradually grow until they own a large portion of their customers’ machine learning workflows. Those that cannot build a sustainable business are acquired or eventually closed.
With current market conditions and declining startup prices, this means that there are likely to be many opportunities for acquisitions, either in the form of “acquisitions” or to buy back key technology. Snowflake, in particular, will likely consider acquisitions after spending $800 million on a machine learning platform called Streamlit.
“I think the next six months, if things stay where they are, there could be some interesting opportunities on the M&A front. Not necessarily big M&As, but I think there will be some valuation resets on some of the private companies there could create some interesting opportunities,” Snowflake Chief Financial Officer Mike Scarpelli said during the company’s latest earnings call.
Scarpelli went on to explain that there are certain areas on the company’s roadmap where it may make sense to consider acquisitions for both additional staff and technology purchases.
“We are not looking for revenue but for good teams and technology at a more reasonable valuation,” he said.
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The data catalog space, which includes startups like the $1.2 billion company Alation and the $5.25 billion company Collibra, is one of many areas of the learning industry automatic which sources say can be difficult to prove as a compelling standalone product, which makes it ripe for acquisitions. Another part of this workflow that comes up frequently is feature stores.
Feature stores allow developers to avoid performing massive recalculations unnecessarily when deploying a machine learning component of a product. The biggest player is Tecton, which runs the open-source feature store tool Feast. Tecton was founded in 2019 by the creators of Uber’s Michelangelo machine learning tools.
Tecton has since moved beyond feature stores into other products, and like many open source tools, Feast serves as an on-ramp to a more sophisticated (and lucrative) tool. But insiders wonder if a feature store — which at the time was enough to net Tecton $60 million in funding from investors like Sequoia and Andreessen Horowitz — can be a standalone product. Tecton and Rasgo, another startup that launched on the momentum of feature stores, have since pushed into new areas.
“That terminology was a little tricky for us. It’s really easy to hear the word ‘store’ and think of a database table,” Tecton CEO Michael Del Balso told Insider. “What we’ve seen, and we see again and again, is teams putting machine learning into production. They’re underestimating this problem.”
It’s in many ways a throwback to the age-old question of whether something is a feature or a product. Machine learning startup Dataiku, for example, has a feature store component, while Tecton has been quick to try to expand beyond feature stores. Both are backed by Snowflake after Tecton raised $100 million earlier this month in a round that also included Databricks.
Snowflake and Databricks could develop the same features as these billion-dollar startups
While Snowflake and Databricks have both bet on Tecton and others, a shadow exists over whether they will launch their own products. Insiders say that as long as they serve as a vehicle to drive usage of Snowflake and Databricks, they expect businesses to remain supported. But Snowflake and Databricks may also consider parts of the workflow, like a feature store, as a component they could add to their own products.
Not all machine learning startups are in this position. Many investors and insiders said several startups likely built enough momentum to avoid being part of a rollup. Hugging Face, recently valued at $2 billion, is one that comes up frequently due to its large community, alongside experience tracking startup Weights & Biases.
At the same time, acquisitions that pop up can offer lucrative results for investors, who rarely see the lightning results of a WhatsApp or a Red Hat. Small acquisitions and initial public offerings are largely the ones that deliver the expected results at high enough volume.
“Machine learning is really exciting, but sometimes it’s hard to relate to ROI for some of these companies,” said an insider close to Tecton and other startups. “Who knows, with this market environment, consolidation could happen very quickly.”