FuriosaAI founder and CEO June Paik said his startup’s architecture can reshape an industry dominated by Nvidia (NASDAQ:NVDA). He told Benzinga he is determined to make FuriosaAI the company that powers the transition to sustainable AI, while declining to comment on reports of an $800 million acquisition offer from Meta (NASDAQ:META).
Yet, his message was clear. “We believe Furiosa will have the greatest impact as an independent company," Paik said. "The world urgently needs new, much more energy-efficient ways to run advanced AI. We are committed to building the technology that will bring the benefits of sustainable AI to everyone.”
Paik added that being mission-driven has simplified key strategic choices, guiding Furiosa to remain independent and focused on long-term impact.
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The Inherent Inefficiency Nvidia Can’t Fix
Paik sees Nvidia’s market dominance as both formidable and fundamentally vulnerable.
“Nvidia’s greatest strength—a legacy built on general-purpose [graphics processing units]—is also its Achilles’ heel,” he declared.”There is inherent inefficiency and architectural complexity that stems from using a general-purpose GPU architecture for specialized AI computing."
Paik added, "GPU makers have developed extremely sophisticated hardware and software workarounds to mitigate these limitations. But their efforts are directed at advancing on their current path, making it difficult for them to pivot to a new paradigm that is uniquely suited to AI.”
FuriosaAI started from first principles in 2017, designing the Tensor Contraction Processor architecture specifically for AI computations. “Our chip architecture, the Tensor Contraction Processor, works at a higher abstraction level,” Paik told Benzinga. “Tensor contraction is the fundamental operation in deep learning, which makes complex optimization problems much more tractable compared to the lower-level matrix multiplication used by GPUs.”
The architectural choice delivers practical advantages. “This means that when new AI models emerge, our compiler can run them efficiently without a massive effort to write hundreds of new, hand-tuned kernels,” Paik said. “It also means our chips minimize wasteful data movement, resulting in breakthrough power efficiency.”
Paik compared the competition to the shift from gas-powered cars to electric vehicles. Legacy automakers spent a century refining the combustion engine, while newer entrants began with a clean slate and designed around an entirely different paradigm.
According to Paik, Furiosa is following a similar path in AI compute, developing an architecture that delivers performance and cost savings beyond the reach of GPU-based systems.
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The Electricity Bottleneck Blocking AI Growth—And FuriosaAI’s Solution
FuriosaAI’s RNGD chip has been described as the “foundational compute layer of the AI stack,” a positioning that Paik believes will fundamentally alter the balance of power in the chip industry.
“We empower enterprises economically,” Paik told Benzinga. “RNGD’s superior efficiency translates directly into lower total cost of ownership and significant operational cost savings. This allows customers to get 3.5x more tokens per rack and run powerful AI applications within standard air-cooled data center racks, where power is often limited.”
The implications extend beyond cost savings. “We enable Sovereign and On-Prem AI,” Paik said. “By providing efficient, locally deployable infrastructure, RNGD directly addresses the cost and data privacy concerns of enterprises. This architecture enables customers, such as LG AI Research, to control and own their own AI stack rather than being reliant solely on large chipmakers and hyperscalers.”
For hyperscalers, Paik said RNGD provides crucial flexibility by increasing compute density in data centers, allowing more tokens per second from the same number of racks. The design also reduces the bottleneck of generating and delivering electricity to power AI services, enabling faster scaling in regions that cannot support the massive energy demands of hundreds of advanced GPU racks.
Three Pillars Of Sustainable AI Computing
Paik’s vision of sustainable AI extends across economic, societal, and environmental dimensions that address interconnected global challenges.
Economic sustainability tackles the fundamental cost crisis. “Economic sustainability is primarily about the fundamental cost of running AI,” Paik told Benzinga. “Because the current infrastructure is prohibitively costly, Furiosa’s superior power delivers substantial operational cost savings and a lower Total Cost of Ownership. This is critical because many AI applications are currently losing money due to high infrastructure costs.”
Societal and infrastructure sustainability emphasizes democratizing access while advancing sovereignty. Paik frames sustainable AI as the ability to scale technology for everyone by removing economic and infrastructure barriers.
This approach supports global sovereign AI initiatives by providing secure, efficient, and locally deployable infrastructure, reducing reliance on a small group of centralized cloud providers and chipmakers.
Environmental sustainability centers on relieving the energy grid crisis that threatens AI growth. Furiosa's RNGD accelerator delivers significantly higher energy efficiency than advanced GPUs, consuming only 180 watts, which lowers the environmental burden from data centers.
Paik told Benzinga the company plans to extend these efficiency gains with future products designed to further reduce strain on global energy systems.
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The Fundamental Mismatch Costing GPU Makers Thousands Of Hand-Tuned Kernels
The technical breakthrough that FuriosaAI achieved addresses what Paik sees as a fundamental mismatch in how AI computations are mapped to silicon.
“We saw a fundamental opportunity in the abstraction layer that the incumbents had either overlooked or could not pursue due to their legacy architecture,” Paik told Benzinga. “The core challenge is the difficulty of mapping massive-scale AI computing to semiconductor hardware. GPU solutions rely on small matrix multiplications as their basic building block, which is a two-dimensional primitive.”
This architectural choice creates cascading challenges, as programmers and compilers must translate complex, multi-dimensional tensor operations into a lower-level primitive. That mismatch makes optimization and scheduling far more difficult, demanding thousands of hand-tuned kernels and extensive engineering effort.
The Tensor Contraction Processor takes a fundamentally different approach. “Our Tensor Contraction Processor architecture is built around tensor contraction as the fundamental primitive,” Paik said. “By using this optimal abstraction, we make the underlying optimization and scheduling problem much more tractable for our compiler.”
The Breaking Point Is Here Now
Converting enterprises away from Nvidia's ecosystem depends on reducing friction while proving tangible benefits.
Paik emphasizes that Furiosa's chip architecture delivers strong real-world performance with far lower power consumption, cutting electricity costs and reducing total cost of ownership. Since energy is the fundamental expense of running AI, this efficiency advantage creates substantial operational savings.
Paik also stresses the ease of adoption. "We don’t ask developers to learn a whole new ecosystem. Our software stack offers native PyTorch integration, an OpenAI-compatible [application programming interface], and a v[large language model] drop-in replacement," Paik told Benzinga.
Customer validation has become the final piece of proof. "LG AI Research’s adoption of RNGD shows potential customers that we deliver real-world performance with straightforward integration. We look forward to sharing more customer news soon," Paik said.
Cloudflare (NYSE:NET), another FuriosaAI customer operating power-limited edge data centers, is prioritizing efficiency in new accelerators, reinforcing that demand for sustainable AI has become a critical industry need.
While competitors pursue incremental improvements, Paik argues the entire GPU business model faces an existential crisis unfolding in real time. “The breaking point is here now,” he said. “The fundamental cost of running AI is determined by energy consumption, and many AI applications are already losing money on infrastructure. Companies must achieve a return on investment from inference to be sustainable. If they cannot, they must scale back or find alternatives.”
Paik told Benzinga the company's focus is on delivering high-performance, energy-efficient compute that supports both global sustainability and local sovereignty. He added that Furiosa's strength lies in the deep integration of its hardware, compiler, and software stack, a system far harder to replicate than any single idea.
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FuriosaAI's $246 Million War Chest And Path To 2026 Series D
FuriosaAI has raised $246 million to date with a valuation of approximately $735 million, including a $125 million Series C round closed in July. The funding enables production scaling and next-generation chip development.
“Furiosa has the investor support it needs to make rapid progress toward achieving our mission,” Paik told Benzinga. “In 2026, we plan to raise a Series D round to accelerate that momentum.”
The company plans to share additional details in early 2026, positioning the new chip to address emerging AI paradigms that demand dramatically different computational profiles than today’s language models.
The investor relationships reflect careful alignment around long-term vision rather than short-term monetization pressure. “We have prioritized working with investors who are aligned with our mission and strategy,” Paik said.
The collaboration with OpenAI surprised many observers, but Paik sees it as a natural alignment between cutting-edge algorithms and efficient hardware.
FuriosaAI’s partnership strategy encompasses both American technology giants like OpenAI and Asian enterprises seeking strategic independence from U.S. technology providers. “To achieve our mission to make AI sustainable, Furiosa must be a global company that works with leading enterprises everywhere. Great technology is an equalizer," Paik told Benzinga.
June Paik's Gamble 5,000 Miles From Silicon Valley
During FuriosaAI's early years, limited resources made the choice of architecture the most critical decision.
Paik said the team spent nearly three years validating its Tensor Contraction Processor design before scaling, comparing the process to drafting and redrafting a script before production. "This forced us to be ruthless about designing the right foundation for the future of AI compute,"he told Benzinga, adding that prioritizing first principles under constraint became the company's core strength.
Despite FuriosaAI’s aggressive approach, Paik wonders whether even greater audacity might have accelerated progress. “We’ve been a very aggressive, bold company right from the start – as you’d expect with the name ‘FuriosaAI,'” he reflected. “Even the decision to start an AI chip company 5,000 miles from Silicon Valley was seen as a little crazy. Likewise, I think it was pretty daring to start from first principles to design a new kind of AI chip."
The prescient bets paid off, yet Paik sees room for even greater boldness. “I still wonder if I could have been even bolder and moved even faster,” he admitted. “These questions are top of mind as we develop our third-gen chip. We approach every decision with fearlessness and urgency."
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Paik's Leadership: No Short-Term Compromises For PR Wins
Paik draws clear boundaries around what FuriosaAI will and will not do, even under external pressure. “Furiosa is committed to engineering excellence and unconstrained innovation based on first principles, rather than making short-term compromises for PR wins or chasing unsustainable business models,” he told Benzinga.
FuriosaAI has maintained exceptional stability while growing from a small engineering group into a global enterprise, retaining more than 90% of its hires over the past eight years and keeping all three co-founders in leadership roles.
That consistency has preserved the company's focus on engineering excellence and open collaboration, while also enabling the addition of top-tier executives to strengthen its leadership team, Paik said.
Building The Sagrada Familia of AI
Paik’s ultimate ambition transcends financial metrics or competitive victories, reaching toward lasting contribution. “I want Furiosa to be remembered as the company that powered the transition to Sustainable AI and made powerful AI accessible to everyone on Earth,” he told Benzinga. “If we succeed in eliminating the economic and infrastructure roadblocks holding AI back, the valuation will naturally follow.”
The inspiration comes from architecture that endures across generations. “Before starting Furiosa, I attended an AI conference in Barcelona, and I was really struck by the city’s unique architecture,” Paik recalled. “My goal for Furiosa is to build something as beautiful as the Sagrada Familia — a foundational contribution to the world that stands the test of time.”
With RNGD shipping to customers and strong pipeline momentum across telecommunications, financial services, and cloud computing, Paik’s vision of democratized, sustainable AI computing is transitioning from ambitious promise to operational reality.
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