When Physics Meets Silicon Valley
Some post-NeurIPS thoughts + predictions
The poster session on the second day of NeurIPS (a big annual ML conference) had the usual chaos: researchers jockeying for position, coffee cups abandoned on windowsills, the low hum of a hundred simultaneous conversations about gradient descent and attention mechanisms.
But one exchange stuck with me. A young chemist presenting work on catalyst discovery mentioned, almost offhandedly, that she’d be starting at Periodic Labs in January. Her collaborator, still at MIT, asked if she’d keep her adjunct appointment. “Maybe,” she said. “Depends on whether I have time.”
That casual fluidity between institutions would have been remarkable a decade ago. At NeurIPS last week in San Diego, it was everywhere. The boundaries that once separated academic research from commercial application, that kept university labs and corporate R&D in distinct orbits, have become porous in ways that are reshaping how science gets done, who funds it, and where the next generation of breakthroughs will come from.
The New Physics
A category of company is emerging that doesn’t fit neatly into existing taxonomies. Call it physical AI, or science AI, or what Lila Sciences boldly terms “scientific superintelligence.” These ventures share a common conviction: that the next frontier of artificial intelligence lies not in generating text or images, but in understanding and manipulating the physical world.
Periodic Labs surfaced in October with $300M raised and a pedigree that reads like a roster of AI royalty. Liam Fedus helped create ChatGPT. Ekin Dogus Cubuk led materials and chemistry research at Google DeepMind, where his team discovered 2.2 million new inorganic crystals. Their thesis is stark: large language models trained on internet data will plateau. Genuine scientific reasoning requires AI systems that can formulate hypotheses, design experiments, and learn from physical outcomes. More than twenty researchers left OpenAI, DeepMind, Meta, and Apple to bet their careers on that idea.
Physical Intelligence approaches the problem from the robotics side. Founded in 2024 by former DeepMind researchers and academics from Stanford and Berkeley, the company raised $600 million in November at a $5.6 billion valuation. Alphabet’s CapitalG led the round. Jeff Bezos came back for more. The goal, as CEO Karol Hausman has described it, is building “a single generalist brain that can control any robot.” Their reinforcement learning techniques have doubled robotic throughput in testing, with machines now handling tasks from espresso preparation to laundry folding.
Lila Sciences emerged from Flagship Pioneering, the firm behind Moderna, with a $200 million seed round in March. A $350 million Series A followed in October, with Nvidia participating, bringing total funding north of $550 million and valuation past $1.3 billion. The company operates what it calls AI Science Factories: autonomous laboratories where robotic systems conduct thousands of experiments without human intervention, generating proprietary datasets no competitor can access. The early results include novel antibodies, catalysts for green hydrogen production, and carbon capture materials that outperform commercial alternatives.
PhysicsX takes yet another angle. Founded by Robin Tuluie, former head of R&D at Mercedes and Renault F1, and Jacomo Corbo, former chief scientist at QuantumBlack, the London-based company builds AI tools for engineering simulation. A $135 million Series B in June pushed valuation to just under $1 billion. Revenue has more than quadrupled over two years. The core offering: physics predictions that run 10,000 to a million times faster than traditional numerical simulation.
The New York Times, The Quest for A.I. ‘Scientific Superintelligence’
Following the Money
The capital flowing into physical AI reflects broader patterns in venture funding, but with distinct characteristics that signal something beyond typical hype cycles.
U.S. startup funding rose 75.6% in the first half of 2025, reaching $162.8B. AI deals now represent more than half of total venture capital allocation globally. Robotics startups have pulled in over $6B this year, on pace to exceed 2024.
What distinguishes physical AI investment is the composition of the cap tables. PhysicsX counts Siemens, Temasek, and Applied Materials among its backers, alongside traditional venture firms like Atomico and General Catalyst. That mix of industrial giants, sovereign wealth, and Silicon Valley capital suggests conviction across investor categories that rarely align.
Defense applications are also accelerating the trend. Northrop Grumman partnered with Luminary Cloud to apply physics-based AI to spacecraft design, compressing development timelines from years to months. The underlying model, built on Nvidia’s PhysicsNeMo framework, generates high-fidelity thruster simulations in seconds. Juan Alonso, Luminary’s CTO and chair of aeronautics at Stanford, frames the opportunity bluntly: “We can’t find the data for the latest rocket thruster on the internet.”
The Great Reshuffling
Scientific talent is leaving academia faster than at any point in recent memory, and the causes trace directly to Washington.
The money matters. Research scientists at Series D startups command stock grants between $2 million and $4 million. Universities can’t match that. But compensation alone doesn’t explain the speed. Federal research funding collapsed this year. The NIST layoffs in February gutted the U.S. AI Safety Institute. Johns Hopkins cut 2,000 workers after $800 million in federal funding disappeared. Graduate programs rescinded PhD offers. Scientists applied for international jobs at a 32% higher rate than last year.
The private sector is catching them. In November, Yann LeCun, Meta’s chief AI scientist and a Turing Award winner, announced he’s leaving to start his own company. When Periodic Labs launched in October, more than twenty researchers from OpenAI, DeepMind, Meta, and Apple joined them. These aren’t junior hires chasing better salaries. These are people who built the field.
So is this what policy intended? The CHIPS Act passed to rebuild domestic manufacturing. Defense modernization assumes access to engineering talent. But the same administration that championed those goals gutted the institutions that produce the people. One read: they expect the private sector to absorb researchers more productively. And maybe that’s right. The startups profiled above move faster and have produced real results. The other read: foundational science takes decades to pay off, and venture capital doesn’t wait that long. Bell Labs could afford patience because AT&T was a regulated monopoly. Startups answer to different pressures.
What Gets Built
The practical iYou are not allowed to have any words, and the photos must be overlapping. It must be like inspired by this. mplications of physical AI extend well beyond academic interest. These systems promise to transform how products get designed, how materials get discovered, and how scientific research itself gets conducted.
PhysicsX already works on turbine optimization and aerospace applications, cutting simulation times from hours to seconds. Their Large Physics Models, trained on data from Siemens simulations, encode engineering knowledge in ways that compound over time. Lila’s autonomous labs have produced discoveries across chemistry, biology, and materials science. Physical Intelligence pushes toward robots capable of operating in unstructured environments, learning from experience rather than explicit programming.
The energy transition offers perhaps the clearest application domain. Catalyst discovery for green hydrogen. Materials optimization for carbon capture. Battery chemistry exploration. Grid management. These problems share characteristics that make them well-suited to AI-driven approaches: vast search spaces, expensive experimentation, and high economic stakes.
Defense applications carry similar logic. When Northrop Grumman can iterate on thruster designs in seconds rather than months, development cycles compress and design possibilities expand. The same capability applied to autonomous systems, materials science, or electronic warfare creates advantages that compound over time.
The Institutional Question
Whether venture-backed labs can sustain foundational research remains genuinely uncertain. The concern among academics is that commercial pressure biases toward near-term applications at the expense of fundamental inquiry. The counterargument, articulated by founders like Lila’s Geoffrey von Maltzahn, is that internet-scale training data has inherent limits. Breakthroughs require generating new data through experimentation, which requires resources that exceed what most universities can provide.
Periodic Labs positions itself explicitly as a successor to Bell Labs, the institution that produced the transistor, the laser, Unix, and seven Nobel Prizes. The comparison is aspirational, but it captures something real about the ambition. These companies are not building incremental improvements. They are attempting to change how scientific discovery happens.
The hybrid models emerging at NeurIPS suggest the binary framing of academia versus industry may already be obsolete. Researchers hold simultaneous appointments. Startups fund university collaborations. Corporate labs publish in top venues. The boundaries blur not because anyone planned it, but because the problems demand resources and talent that no single institution can provide.
What Comes Next
The chemist I met at the poster session will start at Periodic Labs next month. Her MIT collaborator may or may not keep the adjunct appointment. Neither seemed particularly anxious about the ambiguity. The interesting problems are wherever the interesting problems are.
That pragmatism captures something about the current moment. The institutional frameworks that organized scientific research for decades are not collapsing so much as becoming optional. Talent flows toward capability, capital follows talent, and capability concentrates in new configurations that don’t map cleanly onto existing categories.
Whether this produces a new golden age of applied science or a fragmented landscape where capability concentrates in a handful of well-funded ventures while the broader research ecosystem atrophies likely depends on decisions that haven’t been made yet. Policy choices about research funding. Corporate choices about long-term investment. Individual choices about where to build careers.
What seems clear, walking out of NeurIPS into the sunny San Diego afternoon, is that the old equilibrium is gone. The new one is still taking shape.
Really interesting framing on the instituional reshuffling. The Bell Labs comparison keeps coming up but what's actually different now is the feedback loop speed between capital and talent. Periodic's $300M seed is basicaly a bet that private labs can replicate what took AT&T's monopoly rents to fund, except compressed into like 3-5 year horizons instead of decades.