When Jensen Huang told 30,000 attendees at GTC last week that the future data centre is a “token factory,” he was describing a world that a small Israeli startup has been quietly building toward for months. NeuReality, the Caesarea-based company behind the NR-NEXUS inference operating system, has appointed Shalini Agarwal, a product management director at Google Labs, as a strategic adviser charged with shaping how NR-NEXUS reaches enterprise buyers, according to a press release distributed on Monday.
The hire signals a shift in ambition for a company that began life designing custom silicon for AI inference and has since pivoted toward software that promises to turn fragmented GPU clusters into production-grade inference engines.
Agarwal brings roughly two decades of experience in product strategy across major technology companies. At Google Labs, she has directed product management for AI-focused initiatives. Before that, she spent nearly a decade at eBay, according to publicly available professional records, and holds a degree in computer science, electrical engineering, and management science from MIT. Her appointment is advisory rather than operational, but it places a recognisable Silicon Valley name alongside NeuReality’s existing leadership: co-founder and CEO Moshe Tanach and president Hiren Majmudar, a former GlobalFoundries and Intel Capital executive who joined in September 2024.
The timing is deliberate. On 12 March, NeuReality unveiled NR-NEXUS, describing it as a hardware-agnostic operating system for what the company calls AI factories. The platform disaggregates prefill and decode tasks across heterogeneous hardware, including GPUs, CPUs, and network interface cards, aiming to squeeze more useful work out of expensive accelerators that often sit partially idle. Beta customers are already running the software, according to the company, though NeuReality has not disclosed which organisations are in the programme.
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The product arrives at a moment when inference economics have become one of the most closely watched metrics in enterprise AI. Deloitte estimates that inference workloads accounted for half of all AI compute in 2025 and will reach two-thirds this year. Hyperscalers are responding with enormous capital expenditure, with Amazon projecting $200 billion in 2026 spending and Google budgeting between $175 billion and $185 billion, according to recent earnings disclosures. Yet much of that investment flows through a small number of vertically integrated stacks, leaving enterprises that want to run inference across mixed hardware with limited options.
That gap is where NeuReality is placing its bet. NR-NEXUS is designed to work across any CPU, GPU, or NIC, including NVIDIA’s forthcoming Vera Rubin architecture, and targets three buyer categories: neocloud providers, enterprises building their own inference capacity, and semiconductor vendors looking to offer a complete software layer atop their chips.
The company has raised approximately $70 million to date. A $35 million Series A in late 2022, led by Samsung Ventures with participation from OurCrowd and SK Hynix among others, was followed by a $20 million round in March 2024 anchored by the European Innovation Council Fund and existing investors. That EU backing positioned NeuReality as part of a broader European push to develop sovereign AI infrastructure, though the company’s engineering centre remains in Israel.
Agarwal’s advisory role appears focused on go-to-market strategy rather than product engineering, a recognition that building an inference operating system is only half the challenge. The other half is persuading infrastructure buyers, many of whom have deep relationships with NVIDIA’s own software ecosystem, that a startup’s orchestration layer is worth the integration effort.
Whether NR-NEXUS can gain traction will depend on execution in a market that is attracting well-funded competition. Modal Labs is raising at a reported $2.5 billion valuation. Baseten announced a $300 million round at $5 billion. Fireworks AI secured $250 million. Each approaches inference optimisation from a slightly different angle, but all are chasing the same fundamental opportunity: as AI moves from training to deployment, whoever controls the inference layer controls a growing share of the value chain.
For NeuReality, the appointment of an adviser with Google-grade product instincts may be a modest move on paper. In practice, it is a bet that the next phase of AI infrastructure will reward companies that can bridge the gap between silicon and the enterprises that need to run models at scale, efficiently, and across hardware they already own.
