Venture funding for chip startups has doubled in the last five years thanks to AI

Funding for chip startups used to be rare. But in the last five years it’s more than doubled. Here’s why.

Chip startups.

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.

Illustration: Protocol

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. By contrast, Gordon Moore famously predicted that every two years central processors used inside desktops and servers would merely double their performance.

The big jump in demand for computing horsepower helped make Nvidia the most valuable chip company in the U.S., as its graphics chips and software stack can be harnessed for machine learning. But 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,” Celesta Capital founding partner Nicholas Brathwaite said in an interview.

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. “It requires a lot of capital, and it is a big problem that drives a lot of investment not only in the public side, but the private side too.”

New chips on the block

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.

Semiconductors are expensive to make: The costs associated with new 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. And chip design talent is scarce and costly.

Today some of that has changed. As Benjamin said, 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. The figures include funding from chip companies such as Intel, Samsung and Qualcomm, which operate their own venture units in part to keep tabs on the latest tech and open the option to make acquisitions.

With the advent of artificial intelligence, and the theme of AI, there has been this 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. “The high valuation of Nvidia and AMD showed how much growth there is in data center AI, and increased the comparable valuation for startups greatly.”

Micron Ventures Senior Director Gayathri Radhakrishnan told Protocol that the memory-maker is interested in the financial success of its startup investments, but also sees the possibility of becoming a customer. And, occasionally, when Micron is interested in technology for its own purposes, those investments give Radhakrishnan a unique way of completing diligence: deploying the tech inside of Microns’s factories, and seeing how well it performs.

“We understand some of the pain points or gaps that our manufacturing teams are looking for,” Radhakrishnan said. “It [can] almost end up serving as our technical diligence if they can meet the requirements.”

As with just about any venture capital investment, the people writing the checks are looking for exponential returns and technology that can deliver, in some cases, exponential differences in performance. “It’s not enough to be 30% better, it needs to be five to 10 [times] better,” Eclipse Ventures partner Greg Reichow said.

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. Reichow pointed to Cerebras Systems, which doesn’t use the most-advanced manufacturing techniques for its silicon-wafer-sized AI chips, but rather is rethinking how chips communicate with one another.

Keeping up with the Moores

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.”


Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. Strangelove." That’s because he wants you — yes, you — to read them.

The ways Zia Faruqui (right) has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Photo: Carolyn Van Houten/The Washington Post via Getty Images

“Cryptocurrency and related software analytics tools are ‘The wave of the future, Dude. One hundred percent electronic.’”

That’s not a quote from "The Big Lebowski" — at least, not directly. It’s a quote from a Washington, D.C., district court memorandum opinion on the role cryptocurrency analytics tools can play in government investigations. The author is Magistrate Judge Zia Faruqui.

Keep ReadingShow less
Veronica Irwin

Veronica Irwin (@vronirwin) is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc.

The financial technology transformation is driving competition, creating consumer choice, and shaping the future of finance. Hear from seven fintech leaders who are reshaping the future of finance, and join the inaugural Financial Technology Association Fintech Summit to learn more.

Keep ReadingShow less
The Financial Technology Association (FTA) represents industry leaders shaping the future of finance. We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation.

AWS CEO: The cloud isn’t just about technology

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times.

Photo: Noah Berger/Getty Images for Amazon Web Services

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.

It will be the second re:Invent with CEO Adam Selipsky as leader of the industry’s largest cloud provider after his return last year to AWS from data visualization company Tableau Software.

Keep ReadingShow less
Donna Goodison

Donna Goodison (@dgoodison) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers.

Image: Protocol

We launched Protocol in February 2020 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Keep ReadingShow less
Bennett Richardson

Bennett Richardson ( @bennettrich) is the president of Protocol. Prior to joining Protocol in 2019, Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University.


Why large enterprises struggle to find suitable platforms for MLops

As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

Photo: artpartner-images via Getty Images

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.

Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI’s product intelligence team, told Protocol earlier this year. “The duct tape starts to show,” he said.

Keep ReadingShow less
Kate Kaye

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.org and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data.

Latest Stories