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Apps



CARTESIA CLAIMS ITS AI IS EFFICIENT ENOUGH TO RUN PRETTY MUCH ANYWHERE

Kyle Wiggers
4:00 AM PST · December 12, 2024



It’s becoming increasingly costly to develop and run AI. OpenAI’s AI operations
costs could reach $7 billion this year, while Anthropic’s CEO recently suggested
that models costing over $10 billion could arrive soon.

So the hunt is on for ways to make AI cheaper.




Some researchers are focusing on techniques to optimize existing model
architectures — i.e. the structure and components that make models tick. Others
are developing new architectures they believe have a better shot of scaling up
affordably.

Karan Goel is in the latter camp. At the startup he helped co-found, Cartesia,
Goel’s working on what he calls state space models (SSMs), a newer, highly
efficient model architecture that can handle large amounts of data — text,
images, and so on — at once.

“We believe new model architectures are necessary to build truly useful AI
models,” Goel told TechCrunch. “The AI industry is a competitive space, both
commercial and open source, and building the best model is crucial to success.”


ACADEMIC ROOTS

Before joining Cartesia, Goel was a PhD candidate in Stanford’s AI lab, where he
worked under the supervision of computer scientist Christopher Ré, among others.
While at Stanford, Goel met Albert Gu, a fellow PhD candidate in the lab, and
the two sketched out what would become the SSM.

Goel eventually took part-time jobs at Snorkel AI, then Salesforce, while Gu
became assistant professor at Carnegie Mellon. But Gu and Goel went on studying
SSMs, releasing several pivotal research papers on the architecture.



In 2023, Gu and Goel — along with two of their former Stanford peers, Arjun
Desai and Brandon Yang — decided to join forces to launch Cartesia to
commercialize their research.

Cartesia’s founding team. From left to right: Brandon Yang, Karan Goel, Albert
Gu, and Arjun Desai. Image Credits:Cartesia

Cartesia, whose founding team also includes Ré, is behind many derivatives of
Mamba, perhaps the most popular SSM today. Gu and Princeton professor Tri Dao
started Mamba as an open research project last December, and continue to refine
it through subsequent releases.

Cartesia builds on top of Mamba in addition to training its own SSMs. Like all
SSMs, Cartesia’s give AI something like a working memory, making the models
faster — and potentially more efficient — in how they draw on computing power.


SSMS VS. TRANSFORMERS

Most AI apps today, from ChatGPT to Sora, are powered by models with a
transformer architecture. As a transformer processes data, it adds entries to
something called a “hidden state” to “remember” what it processed. For instance,
if the model is working its way through a book, the hidden state values might be
representations of words in the book.




The hidden state is part of the reason transformers are so powerful. But it’s
also the cause of their inefficiency. To “say” even a single word about a book a
transformer just ingested, the model would have to scan through its entire
hidden state — a task as computationally demanding as rereading the whole book.

In contrast, SSMs compress every prior data point into a sort of summary of
everything they’ve seen before. As new data streams in, the model’s “state” gets
updated, and the SSM discards most previous data.

The result? SSMs can handle large amounts of data while outperforming
transformers on certain data generation tasks. With inference costs going the
way they are, that’s an attractive proposition indeed.


ETHICAL CONCERNS

Cartesia operates like a community research lab, developing SSMs in partnership
with outside organizations as well as in-house. Sonic, the company’s latest
project, is an SSM that can clone a person’s voice or generate a new voice and
adjust the tone and cadence in the recording.

Goel claims that Sonic, which is available through an API and web dashboard, is
the fastest model in its class. “Sonic is a demonstration of how SSMs excel on
long-context data, like audio, while maintaining the highest performance bar
when it comes to stability and accuracy,” he said.

Cartesia’s Sonic model can customize speech to a fair degree, including the
PROSODY. Image Credits:Cartesia

While Cartesia has managed to ship products quickly, it’s stumbled into many of
the same ethical pitfalls that’ve plagued other AI model-makers.

Cartesia trained at least some of its SSMs on The Pile, an opendata set known to
contain unlicensed copyrighted books. Many AI companies argue
that fair-use doctrine shields them from infringement claims. But that hasn’t
stopped authors from suing Meta and Microsoft, plus others, for allegedly
training models on The Pile.

And Cartesia has few apparent safeguards for its Sonic-powered voice cloner. A
few weeks back, I was able to create a clone of Vice President Kamala Harris’
voice using campaign speeches (listen below). Cartesia’s tool only requires that
you check a box indicating that you’ll abide by the startup’s ToS.



Cartesia isn’t necessarily worse in this regard than other voice cloning tools
on the market. With reports of voice clones beating bank security checks,
however, the optics aren’t amazing.




Goel wouldn’t say Cartesia is no longer training models on The Pile. But he did
address the moderation issues, telling TechCrunch that Cartesia has “automated
and manual review” systems in place and is “working on systems for voice
verification and watermarking.”

“We have dedicated teams testing for aspects like technical performance, misuse,
and bias,” Goel said. “We’re also establishing partnerships with external
auditors to provide additional independent verification of our models’ safety
and reliability …  We recognize this is an ongoing process that requires
constant refinement.”


BUDDING BUSINESS

Goel says that “hundreds” of customers are paying for Sonic API access,
Cartesia’s primary line of revenue, including automated calling app Goodcall.
Cartesia’s API is free for up to 100,000 characters read aloud, with the most
expensive plan topping out at $299 per month for 8 million characters. (Cartesia
also offers an enterprise tier with dedicated support and custom limits.)

By default, Cartesia uses customer data to train its models — a not-unheard-of
policy, but one unlikely to sit well with privacy-conscious users. Goel notes
that users can opt out if they wish, and that Cartesia offers custom retention
policies for larger orgs.

Cartesia’s data practices don’t appear to be hurting business, for what it’s
worth — at least not while Cartesia has a technical advantage. Goodcall CEO Bob
Summers says that he chose Sonic because it was the only voice generation model
with a latency under 90 milliseconds.

“[It] outperformed its next best alternative by a factor of four,” Summers
added.

Goodcall’s AI “agent” service relies on Cartesia’s Sonic API. Image
Credits:Goodcall

Today, Sonic’s being used for gaming, voice dubbing, and more. But Goel thinks
it’s only scratching the surface of what SSMs can do.

His vision is models that run on any device and understand and generate any
modality of data — text, images, videos, and so on — almost instantly. In a
small step toward this, Cartesia this summer launched a beta of Sonic On-Device,
a version of Sonic optimized to run on phones and other mobile devices for
applications like real-time translation.




Alongside Sonic On-Device, Cartesia published Edge, a software library to
optimize SSMs for different hardware configurations, and Rene, a compact
language model.

“We have a big, long-term vision of becoming the go-to multimodal foundation
model for every device,” Goel said. “Our long-term roadmap includes developing
multimodal AI models, with the goal of creating real-time intelligence that can
reason over massive contexts.”

If that’s to come to pass, Cartesia will have to convince potential new clients
its architecture is worth suffering the learning curve. It’ll also have to stay
ahead of other vendors experimenting with alternatives to the transformer.

Startups Zephyra, Mistral, and AI21 Labs have trained hybrid Mamba-based models.
Elsewhere, Liquid AI, led by robotics luminary Daniela Rus, is developing its
own architecture.

Goel asserts that 26-employee Cartesia is positioned for success, though —
thanks in part to a new cash infusion. The company this month closed a $22
million funding round led by Index Ventures, bringing Cartesia’s total raised to
$27 million.

Shardul Shah, partner at Index Ventures, sees Cartesia’s tech one day driving
apps for customer service, sales and marketing, robotics, security, and more.

“By challenging the traditional reliance on transformer-based architectures,
Cartesia has unlocked new ways to build real-time, cost-effective, and scalable
AI applications,” he said. “The market is demanding faster, more efficient
models that can run anywhere — from data centers to devices. Cartesia’s
technology is uniquely poised to deliver on this promise and drive the next wave
of AI innovation.”

A* Capital, Conviction, General Catalyst, Lightspeed, and SV Angel also
participated in San Francisco-based Cartesia’s latest funding round.




TechCrunch has an AI-focused newsletter! Sign up here to get it in your inbox
every Wednesday.

Topics

AI, Apps, Cartesia, edge AI, Enterprise, Exclusive, Funding, Generative AI,
Index Ventures, speech generation, SSM, Startups, state space models, voice
generation


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Kyle Wiggers

Senior Reporter, Enterprise

@Kyle_L_Wiggers
Kyle Wiggers is a senior reporter at TechCrunch with a special interest in
artificial intelligence. His writing has appeared in VentureBeat and Digital
Trends, as well as a range of gadget blogs including Android Police, Android
Authority, Droid-Life, and XDA-Developers. He lives in Brooklyn with his
partner, a piano educator, and dabbles in piano himself. occasionally — if
mostly unsuccessfully.
View Bio



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