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Stability AI, the company behind the Stable Diffusion image model, said Tuesday it has raised $76 million in Series B funding, lifting its cumulative total to $232 million. The composition of the round is more interesting than the figure.

New money came primarily from the entertainment industry rather than traditional venture capital. Universal Music Group, Sony Music Group and Warner Music Group all invested, as did the gaming publisher Electronic Arts. AMD Ventures and Pacific Alliance Ventures participated on the technology side, alongside existing backers including Coatue, Greycroft, former Google chief executive Eric Schmidt and Napster co-founder Sean Parker, who sits on the company's board with the film director James Cameron.

Customers turned shareholders

That roster is less a funding syndicate than a list of the partners Stability now depends on for content licensing and distribution. The company has spent the past year converting adversaries into collaborators: it signed deals with Universal Music and Electronic Arts last October and with Warner Music in November, arrangements that give those companies a hand in co-developing Stability's tools rather than merely licensing the output.

Chief executive Prem Akkaraju, who took over in 2024, cast the round as an affirmation of a vision in which generative AI serves producers, musicians and storytellers rather than displacing them. Reframing the label relationship from rights-holder-versus-scraper to co-development is the strategic core of that argument, and equity is the strongest available signal that the labels accept it.

Where the money goes

Stability says the capital will fund continued work on its creative production product suite, deepen its applied research operation and expand its professional services arm. The company currently offers models for image, video and music generation, and its recent Stable Audio 3.0 release was trained on licensed data, a detail that matters considerably to the investors who just wrote cheques.

The professional services expansion is the quieter signal. Selling model access is a commodity business; embedding engineers inside a studio's or label's production workflow is not, and it is the kind of relationship the new shareholders are positioned to supply.

Legal weather clearing, slowly

The funding arrives after a comparatively good stretch in court. Stability largely prevailed in the United Kingdom against Getty Images, which had accused the company of infringing its rights by training an image model on its photographs. A judge ruled substantially in Stability's favour. A parallel Getty suit in the United States remains unresolved.

The company also faced a 2023 claim from co-founder Cyrus Hodes, who alleged that fellow co-founder Emad Mostaque tricked him into selling his stake. Mostaque departed in 2024, and Akkaraju's tenure has been defined by the pivot from open-model provocateur to licensed-content partner.

Outlook

Founded in London in 2019, Stability spent its early years as the most conspicuous target in the generative AI copyright fight. A round underwritten by three major record companies and a large game publisher suggests that fight has, for this company at least, been substantially renegotiated rather than won or lost.

The open question is durability. Strategic investors buy influence over a roadmap as much as a return, and the same labels backing Stability are simultaneously pursuing their own AI initiatives and litigation against other model developers. For Stability, $76 million buys runway and, more valuably, the appearance of legitimacy in a market where licensing terms increasingly decide who gets to ship.

20:Te69,

AMD has signed a definitive agreement to acquire Taalas, a Toronto-based chip startup building silicon that hardwires AI model weights directly into the die. The deal gives AMD a bet on a radically different approach to inference economics at a moment when the cost of serving large models has become the industry's central constraint.

Financial terms were not disclosed. It is AMD's third AI-related acquisition in nine months, following MK1 in November and memory optimization startup Mext in June.

What Taalas Actually Builds

Founded in 2023, Taalas takes an approach that runs against the grain of general-purpose accelerator design. Rather than building a flexible chip that loads model weights from external memory at inference time, Taalas etches those weights into the silicon itself, producing what amounts to a model-specific integrated circuit.

The payoff is the elimination of the memory bandwidth bottleneck that dominates modern inference. Moving weights between high-bandwidth memory and compute units consumes a large share of both time and power in a conventional GPU serving pipeline. A chip that already holds the model in its physical structure sidesteps that traffic entirely.

Taalas emerged from stealth in February with a demonstration chip clocking more than 16,000 tokens per second per user on Llama 3.1-8B, with early technical demos reported at up to 17,000 tokens per second. Those figures are an order of magnitude beyond typical GPU-served throughput for a single user on a model of that size.

The Obvious Catch

The trade-off is equally stark. A chip with a model baked into it cannot run a different model. In a field where flagship releases arrive every few months and fine-tuned variants proliferate constantly, committing silicon to a specific set of weights is a significant wager on that model's staying power.

That constraint shapes where the technology fits. It makes most sense for high-volume, long-lived workloads where a stable model serves enormous request traffic and the per-token economics dominate every other consideration. It makes far less sense for research environments or products that swap models frequently.

A Familiar Playbook

AMD said it plans to fold Taalas technology into its accelerator roadmap and develop system-level products combining it with AMD Instinct GPUs. That pairing hints at the likely architecture: flexible GPUs handling general workloads alongside fixed-function silicon absorbing the highest-volume, most predictable inference traffic.

The move echoes Nvidia's roughly $20 billion licensing arrangement with Groq last December, another bet on specialized inference hardware from a company that already dominates training. Both deals reflect the same underlying shift. Training capacity was the scarce resource that defined the last cycle; inference cost is defining this one, and the economics of serving models at scale increasingly favor silicon designed for exactly one job.

For AMD, which has spent years working to establish Instinct as a credible alternative to Nvidia's data center line, the acquisition offers a lane where it is not simply chasing a competitor's roadmap. Investors reading the deal framed it as an attempt to compete more directly with Nvidia rather than incrementally narrow the gap.

Whether model-specific silicon becomes a mainstream deployment tier or stays a niche for the largest inference providers will depend on how quickly AMD can move Taalas designs from demonstration parts into shipping products, and on whether model release cadence slows enough to make the commitment worthwhile.

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Stability AI Raises $76M From the Music Industry That Once Feared It

AMD Buys Taalas to Etch AI Models Directly Into Silicon

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Stability AI Raises $76M From the Music Industry That Once Feared It

AMD Buys Taalas to Etch AI Models Directly Into Silicon