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“Abandon all hope ye who enter here” was the inscription Dante read when passing through the gates of hell. Apparently, it’s also true of anyone but the big cloud providers when it comes to artificial intelligence, according to an analysis by Bain & Company. “The CSPs [cloud service providers] are best positioned because of the significant head start they have in using AI on a large scale,” the report authors stated. Given that FirstMark investor Matt Turck recently called out how well startups have done in the shadows of the cloud giants, it’s worth diving deeper into the strengths the clouds bring to AI. For one, lots of data.

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The AI rich are getting richer

“CSPs’ cloud and digital services have given them access to the enormous amounts of data required to effectively train AI models,” the authors concluded. Such economies of scale have been an asset to the cloud providers for years. Years ago, RedMonk analyst Stephen O’Grady highlighted the “relentless economies of scale” that the cloud providers brought to hardware–they could simply build more cheaply than any enterprise could hope to replicate in their own data centers. Now the CSPs enjoy a similar advantage with data.

But it’s not merely a matter of raw data.

The CSPs also have more experience using that data on a large scale. The CSPs have products (e.g., Amazon Alexa to assist with natural language processing, or Google Search to help with recommendation systems). Lots of data feeding ever-smarter applications feeding more data into the applications… it’s a self-reinforcing cycle.

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Oh, and that hardware mentioned earlier? The CSPs also have more experience tuning hardware to process machine learning workloads at scale. According to Bain, based on conversations with CSP employees, more than 15% of the big CSPs’ servers are focused on AI computing workloads, which could rise to more than 30% by 2025.

All this experience, in turn, is leading the CSPs to dive ever deeper into increasingly sophisticated commercial AI models. As noted in the report, the complexity of their deep-learning models is more than doubling every three to four months (Figure A).

Figure A

Image: Bain & Company

And the CSPs are not hoarding this expertise. In fact, CSPs like Google (TensorFlow) and Facebook (PyTorch) have released these projects as open source, providing on-ramps to help drive demand for their platforms. Brookings Institution Fellow Alex Engler pointed to this trend, saying that “for Google and Facebook, the open sourcing of their deep learning tools (TensorFlow and PyTorch, respectively), may have [the effect of] further entrenching them in their already fortified positions.”

And, finally, people. I’m not sure how Bain calculated this statistic, but the report suggested that the number of AI employees at five U.S. CSPs (Amazon, Microsoft, Alphabet, Facebook and IBM) exceeds the size of the AI workforces at the next 45 U.S. companies combined. Again, it’s unclear how one designates an “AI employee,” but even if it’s off by a considerable margin, it’s not hard to believe that the CSPs tend to attract many talented engineers/others proficient in machine learning/other aspects of AI (and who can afford to pay their not insubstantial salaries).

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Back to Dante. Is all hope lost? Will the CSPs dominate ML/AI?

If so, no one seems to have informed startup entrepreneurs and the VCs who fund them. As Turck highlighted, “[T]he pace of innovation is just too explosive in the space for things to remain static for too long. Founders launch new startups, Big Tech companies create internal data/AI tools and then open source them, and for every established technology or product, a new one seems to emerge weekly.” A bevy of AI-related startups went public over the past year, including, UiPath, SentinelOne, Recursion and Darktrace. These companies span security, pharmaceuticals and more.

So, yes, the clouds have significant advantages in AI, but, no, that doesn’t mean they’ll dominate. More likely is that many companies will benefit from the tooling/services they provide, even as the market remains open for other entrants to help developers build machine learning and other elements of AI into their applications.

Disclosure: I work for MongoDB, but the views expressed herein are mine.