⚙️ Hugging Face is donating $10 million of GPUs to developers

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Good Morning. An overwhelming majority of you (65%) are concerned about the departure of top safety researchers from OpenAI. The consensus is that Sam Altman will now have to deal with fewer dissenting voices, something that has a lot of you worried.

In other news, Sony Music dipped its toe into the copyright question, sending a letter to 700 AI companies demanding to know if its music was used to train AI models. Sony said it has “reason to believe” its copyright has been violated by these companies.

In today’s newsletter:

Stability AI is discussing a potential sale

Stability AI – the startup behind the popular Stable Diffusion image generator – has, according to The Information, talked to at least one potential buyer about a sale. Stability declined my request for comment on the report. 

The potential sale comes as Stability’s revenue (and expenses) are growing. The problem is that the expenses are outpacing the revenue growth. 

Meanwhile, Stability lost $30 million during the first quarter of this year, and reportedly owes around $100 million to cloud providers.

Gartner: The biggest barrier to enterprise AI adoption 

If you listen to the analysts, the value proposition of AI is all about the enterprise. Investors — Dan Ives prominent among them — have been really excited about the idea of widespread enterprise adoption, which could result in enormous, sustained revenue streams for the tech firms building AI. 

The revenue, however, isn’t there yet. 

A recent Gartner survey of corporations across the U.S., U.K. and Germany found that the biggest barrier to enterprise AI adoption is estimating & demonstrating the value of AI projects. 

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Hugging Face is committing $10 million to get GPUs to developers

Hugging Face, the open-source AI community, said Thursday that it is donating $10 million worth of GPUs to the developer community through a new ZeroGPU program. 

ZeroGPU is accessible through Hugging Face’s Spaces, where multiple users and applications can split portions of available GPU capacity. 

One of the biggest barriers in the development side of the AI world has to do with the cost of compute. 

This cost barrier plays a significant role in the ability of established Big Tech giants to continue dominating the competition. It also makes it difficult for university researchers to stay ahead.

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RingCentral: AI and the business of transparency 

As I mentioned above, some analysts, venture capitalists and investors are starting to look at the current environment as an AI bubble, akin to the dot-com bubble of the late ‘90s. Part of the reason has to do with privacy and security concerns; a recent Cisco study found that 27% of businesses have banned generative AI because of privacy/security concerns. 

For cloud communications provider RingCentral, the core focus is on transparency and privacy, not just from an ethical standpoint, but as a tenet of its business in AI.

This transparency goes beyond breakdowns of what customer information RingCentral collects, and goes into the specifics of the data inputs, processing and outputs for the company’s third-party AI-powered tools: how a tool works, and how a tool interacts with customer data.

Zeni said that for enterprise customers dealing with AI, understanding the risks and potential legal violations — compliance with Europe’s AI Act, for instance — is top of mind. 

My Thoughts: 

There is a fascinating dichotomy between many of the companies producing AI models and the businesses that feel such pressure to adopt them. Broadly speaking, the tech companies are in the business of selling magic. 

OpenAI’s GPT-4o demonstration – in everything from the livestream to Sam Altman’s tweets – was designed to show an instance of science fiction made real. Google’s I/O demos the next day were very much in the same vein. 

But this magical, opaque approach — while good at attracting VC cash — is just no good for businesses; this sets up a big roadblock for these companies that are struggling to generate big returns on their huge AI investments, all because they can’t satisfy many enterprise requirements of privacy, security, transparency and explainability. 

What these AI companies need the most right now is solid adoption in the enterprise, but what enterprises need is reliable, understandable tech. As Zeni said, not magic. Right now, what the enterprises need for adoption doesn’t seem to be in line with what Big Tech is selling.

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-Ian Krietzberg, Editor-in-Chief, The Deep View