Hello and welcome to Protocol Enterprise! Today: how demand for AI chips has led to a surge in chip investing, why federated containers are poised to take over enterprise tech, and how not to conduct security training.

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The modern enterprise tech landscape doesn’t exist without open-source software, but as we’ve learned over the past year, securing and managing open-source software is a huge challenge. According to new research from Tidelift, only 15% of companies are “extremely confident” in their open-source management practices, while 22% are “not very” or “not at all” confident in that discipline.

Return on AI

The raw computational power necessary to use machine learning has dwarfed everything else we use computer chips to accomplish by an order of magnitude. And that appetite for power has created a booming market for chip startups for the first time in years and helped double venture capital investments over the past five years.

AI market leader Nvidia estimates that most machine-learning or AI tasks have spurred a 25-fold increase in the need for processing power every two years, while one of the most advanced natural-language-processing models needs 275 times the compute power every two years to work. The boom has also created opportunities for a new breed of chip company, one that is focused on making specialized chips for AI.

• “As these [AI] workloads started to grow and expand, it gives the startups an opportunity to come in with purpose-built semiconductor devices that can meet the needs better than the general-purpose-type devices,” said Celesta Capital founding partner Nicholas Brathwaite.
• According to data from PitchBook, global sales of AI chips soared 60% last year to $35.9 billion compared to 2020, with roughly half that total coming from specialized AI chips in mobile phones.
• By 2024, PitchBook expects the market to grow at just over 20% a year, suggesting it could reach $64.9 billion by 2024. Allied Market Research forecasts that number could rise to $194.9 billion by 2030.
• “With the advent of artificial intelligence, and the theme of AI, there has been this resurgence in semiconductor investing,” long-time chip industry watcher and Fidelity portfolio manager Adam Benjamin told Protocol in an interview.

Prior to 2015 only a small handful of venture capitalists saw the opportunity that AI presented, and there was little overall interest in funding chip companies.

• The costs associated with new chip factories are measured by the tens of billions of dollars, and even companies that outsource fabrication to the likes of TSMC bear chip development costs that start at $30 million to $40 million and top out above $500 million for the most-advanced processors.
• Today, chips are still expensive to develop and require more startup cash from venture investors to get off the ground. But this no longer dissuades investors.
• Venture funding for semiconductor companies has more than doubled from 2017 to $1.8 billion last year, according to PitchBook data.
• And this year is on track to rise again, with nearly $1 billion in funding through early April.

With the advent of artificial intelligence has come a resurgence in semiconductor investing.

• “Data center startups began to trial commercially viable chips [in 2021] after years of research and development — that goes for a number of the unicorn companies in the space that shipped trial chips to customers,” PitchBook analyst Brendan Burke said in an interview.
• For AI applications, that typically has meant looking at startups developing chips and software around parallel processing, which takes on simpler tasks at far greater volume compared to a traditional processor that completes a single, usually more complex, calculation at a time.
• The chip giants themselves already dump billions into annual research and development spending, including on parallel processing technology found in graphics chips made by AMD, Intel and Nvidia.
• That means investors want to fund companies that take a different approach, an idea that can’t be easily replicated by an incumbent semiconductor company.

“I’m looking for companies that are offering a fundamentally different use case,” Lux Capital partner Shahin Farshchi said. As an example, Farshchi offered the firm’s portfolio company Mythic, which uses an older chip technology to make a chip that performs AI processing at a fraction of the power required to run the type of typical graphics chip that’s used in data centers.

• “The way they accomplished that wasn’t just kind of optimizing or tweaking circuits, it was by taking a whole new approach to computation,” Farshchi said.
• “This new approach didn’t require some fancy new process, or fancy new type of physics, it was taking an existing technology that tens of billions of dollars [have] already gone into, and repurposing it to do something else.”

— Max A. Cherney (email | twitter)