Nvidia's $20 billion Groq deal looks a lot like an acquisition in disguise

The licensing pact gives Nvidia talent, tech access, and industry leverage without triggering regulators

by · TechSpot

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The big picture: Nvidia has just locked in a $20 billion licensing deal with AI startup Groq that gives it strategic access to both technical knowledge and key personnel who might otherwise power competing designs. The agreement doesn't rise to the level of a legal merger, but it sure looks like one and could still raise regulatory scrutiny.

When Nvidia confirmed a $20 billion deal with Groq on December 24, industry headlines initially hailed it as the largest acquisition in the company's history. Within hours, Nvidia clarified that it had not bought the specialized AI hardware startup. Instead, the company described the arrangement as a "non-exclusive licensing agreement" for Groq's inference technology – language that, while technically accurate, raises familiar questions about where partnership ends and acquisition quietly begins.

The $20 billion price tag makes this one of the most expensive licensing agreements in tech history. For Nvidia, which reported $32 billion in profit last quarter, the deal underscores both the strategic importance of securing AI inference technology and the growing pressure from emerging chip architectures outside its dominant GPU ecosystem.

The Groq deal fits a pattern Silicon Valley observers call a hackquisition – a transaction that isn't legally an acquisition but operates similarly in practice. Such deals often combine large cash payments, intellectual property licensing, and selective hiring of key executives. Microsoft, Google, Amazon, and Meta have used this approach to secure technology or talent that might face regulatory scrutiny if acquired outright. In Nvidia's case, the targets appear to be Groq's CEO Jonathan Ross, president Sunny Madra, and several core engineers specializing in ultra-efficient AI inference chips.

Groq built its reputation on a distinctive compute architecture designed for low-latency inference at scale. The company's first batch of chips drew mixed reactions. Some analysts viewed the design as a breakthrough path beyond GPUs for inference tasks; others doubted its scalability and viability in data center environments dominated by Nvidia hardware. Still, Nvidia's decision to license Groq's technology gives it the ability to integrate or adapt key aspects of Groq's design for future products.

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Jonathan Ross brings a technical pedigree that adds weight to the move. Before founding Groq, he helped develop Google's tensor processing unit (TPU) – the chip line that enabled Google to train and run large-scale AI models without relying on Nvidia GPUs. That experience makes him one of the few engineers with firsthand expertise in designing specialized AI processors capable of rivaling GPU performance.

Google's success with TPUs has forced Nvidia to reckon with the possibility that future AI workloads could shift from general-purpose GPUs to dedicated inference hardware. Energy efficiency and inference speed are now key competitive benchmarks, and TPUs are showing growing advantages on both fronts. Although Nvidia still commands roughly 90 percent of the AI chip market, it cannot ignore these signals.

Bringing Ross and his team into Nvidia's orbit – even under the guise of a non-exclusive deal – effectively neutralizes Groq as an independent challenger while importing its expertise into Nvidia's engineering operation. Whether or not Nvidia ever releases a TPU-style product, executives have made clear they intend to lead both training and inference segments.

The structure of the Groq agreement may be as consequential as the technology itself. An outright acquisition would likely have triggered antitrust scrutiny, given Nvidia's dominant position in AI hardware. By opting for a licensing deal instead, Nvidia gains access without changing formal ownership. This maneuver has drawn attention across Big Tech, as regulators debate whether such transactions skirt merger reviews.

Groq CEO Jonathan Ross helped create Google's tensor processing unit.

Critics describe such deals as functional acquisitions that consolidate power without formally transferring equity, often letting the buyer selectively hire talent and integrate technology while leaving the selling company hollowed out. A notable example is Meta's $15 billion agreement with Scale AI, which reduced the startup's independence as key leaders joined Meta's AI division.

Whether the Groq agreement sparks regulatory pushback remains uncertain. Its holiday-week announcement may limit immediate public attention, but antitrust concerns are unlikely to disappear. Nvidia's deal, structured for speed and discretion, could face long-term scrutiny if regulators see it as undermining competition in the AI hardware market.