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Top funding rounds week of August 10th. Databricks led a record-breaking week with a $5B raise. Fundable analyze the surge in physical AI, robotics, and energy infrastructure investments. Published August 17, 2026 by Jacob Klionsky * 01 Databricks Artificial Intelligence (AI) - Data and Analytics - California $5BEquity Databricks, a Data and AI company, raised $5 billion in a strategic funding round at a $190 billion valuation led by Coatue with participation from Blackstone, MGX, accounts advised by T. Rowe Price Associates and T. Rowe Price Investment Management, and new investor Sixth Street Growth, alongside BOND, Clearlake Capital, Point72, Premji Invest, TPG and existing investors including Andreessen Horowitz, Dragoneer, Fidelity, Franklin Templeton, GIC, Goldman Sachs Growth Equity, Insight Partners, J.P. Morgan Private Capital, Kinetic, Morgan Stanley Investment Management, NEA, Ontario Teachers' Pension Plan, Temasek, Thrive Capital, and WCM Investment Management * 02 Thrive Holdings Artificial Intelligence (AI) - Data and Analytics - New York $2BEquity Thrive Holdings, an AI-powered business roll-up acquiring service businesses, raised $2 billion in new funding at a $12 billion valuation in a round led by SoftBank, D1 Capital Partners and Altimeter Capital. * 03 Physical Intelligence Artificial Intelligence (AI) - Data and Analytics - California $1.6BEquity Physical Intelligence, a robotics 'robot brain' startup, raised roughly $1.6B across two rounds at an $11.2B valuation with Founders Fund and Lightspeed joining and participation from Jeff Bezos and OpenAI. * 04 River AI Artificial Intelligence (AI) - Data and Analytics - California $1.1BSeries A River AI, an AI startup, raised $1.1 billion in a seed/Series A round led by General Catalyst and AMP PBC with participation from Nvidia, AMD Ventures, Y Combinator, and Temasek * 05 H&MV Engineering Consumer Electronics - Energy - Limerick $866.2MEquity H&MV Engineering, a Limerick-based provider of specialist high-voltage engineering and critical power infrastructure services, raised approximately €750M in a continuation vehicle transaction at a €1.4 billion valuation led by Exponent with participation from Apollo S3, Pantheon, and SQ Capital. * 06 Form Energy Energy - Sustainability - Massachusetts $750MSeries G Form Energy, a long-duration battery startup, raised $750M in a Series G led by T. Rowe Price with participation from Sequoia Capital, Janus Henderson, Franklin Templeton, PEAK6 Investments, Prelude Ventures, Engine Ventures, TPG Rise Climate, Capricorn's Technology Impact Funds, Breakthrough Energy Ventures, Dustin Moskovitz and Cari Tuna, Gigascale Capital, Coatue, Energy Impact Partners, NGP, GE Vernova, Blindspot Ventures, and M&G Catalyst Fund * 07 Lovable Artificial Intelligence (AI) - Data and Analytics - Stockholms Lan $400MSeries C Lovable, a Swedish vibe-coding startup that enables non-coders to build software, raised $400M in a Series C at a $13.3B valuation co-led by Menlo Ventures and the Scaleup Europe Fund (managed by EQT) with participation from Balderton Capital, Carmignac, Kaszek Ventures, LTS Growth, Tencent, World Innovation Lab, Regent and returning investors including Accel and others. * 08 Doral Renewables Energy - Natural Resources - Pennsylvania $400MEquity Doral Renewables, a Philadelphia-based renewable energy developer, secured a $400 million common equity investment from Doral Group Renewable Energy Resources Ltd. * 09 Cambridge Aerospace Hardware - Manufacturing - Cambridgeshire $300MSeries C Cambridge Aerospace, a UK-based air defence company, raised €259.7 million Series C at a €2.94 billion valuation led by DFJ Growth with participation from Lux, Accel, Lakestar, Never Lift, Ora Global and Elad Gil & Co. * 10 Neros Technologies Consumer Electronics - Consumer Goods - California $250MSeries C Neros Technologies, a domestic drone manufacturer, raised $250M Series C at a $2.5B post-money valuation co-led by Sequoia Capital and American Strategic Technology Fund (ASTF) with participation by Interlagos, Valor Equity Partners, Allen & Company, Thiel Capital, Spark Capital, and Dylan Field. * 11 Sonablate Health Care - North Carolina $200MEquity Sonablate, an AI-powered precision medicine and therapeutic ultrasound company, raised $200M in funding with backers including Eleven Ventures. * 12 Inox Clean Energy Energy - Manufacturing - Uttar Pradesh $157.2MEquity Inox Clean Energy, an integrated renewable energy platform of the INOXGFL Group, received a ₹15 billion commitment from Motilal Oswal Group, including ₹10 billion already invested through compulsorily convertible debentures. * 13 Vaderis Therapeutics Biotechnology - Science and Engineering - Basel-Stadt $152MSeries B Vaderis Therapeutics, a Swiss clinical-stage biotechnology company, raised USD 152 million in a Series B co-led by Life Sciences at Goldman Sachs Alternatives and TCGX with participation from EQT Life Sciences, Omega Funds, Perceptive Advisors, Kalehua Capital, Medicxi and Droia. * 14 Periodic Labs Artificial Intelligence (AI) - Data and Analytics - California $150MEquity Periodic Labs, an AI company building systems paired with robotic laboratories, raised $150 million from Andreessen Horowitz, Accel, Jeff Bezos and Eric Schmidt. * 15 CodeRabbit Artificial Intelligence (AI) - Data and Analytics - California $143MSeries C CodeRabbit, an AI-enabled code-review startup, raised $143M in a Series C led by Atomico and Smash Capital with participation from BMW i Ventures, Datadog, Hirtle Callaghan, SineWave Ventures, CRV, Scale Venture Partners, Flex Capital, Pelion Venture Partners and angel investors including senior executives from Apple and Amazon. * 16 Erco Energía Energy - Natural Resources - Antioquia $129MSeries C Erco Energía, a Colombian solar energy platform, raised $129 million in a Series C led by Axon Partners Group through Next Utility Ventures with participation from Augment Infrastructure and Norfund * 17 RoboStrategy Financial Services - Hardware - NA - Puerto Rico $122.9MEquity RoboStrategy, an investor focused on physical AI, raised $122.9 million via share issuances. * 18 Bridge to Life Biotechnology - Science and Engineering - Illinois $110MSeries C Bridge to Life, organ preservation and perfusion technology company, raised $110 million in a Series C equity financing plus debt financing led by Soleus Capital with participation from Lauxera Capital Partners, company directors, officers and employees, and debt provided by Soleus Capital Credit Opportunities Fund. * 19 Aureka Biotechnologies Biotechnology - Health Care - California $100MSeries B Aureka Biotechnologies, an AI-native TechBio company, raised US$100 million in a Series B with Granite Asia funding the first tranche exclusively and a prominent strategic investor leading a subsequent tranche, with participation from HighLight Capital and follow-on investments from MPCi and NRL Capital * 20 Emerald AI Artificial Intelligence (AI) - Data and Analytics - District of Columbia $90MEquity Emerald AI, a data center energy technology company, raised $90 million in a financing round (investors not disclosed).

Physical AI in 2026: what Robotics Foundation models actually mean for engineers. What changed between robotics software in 2021 and robotics software in 2026? The center of gravity moved from hand-built deterministic pipelines toward learned systems that perceive, interpret instructions, and generate physical actions. Physical AI is defined as AI that operates through sensors and machines to reason about - and manipulate - the real world. Physical Intelligence's π-series helped establish this approach, while its 2026 π0.7 release showed stronger instruction following and generalization across robots and tasks. Classical robotics depended on controlled environments, explicit coordinates, and engineered trajectories. Generalist models now offer a path toward handling unfamiliar objects, clutter, and changing task language. Generalist AI reported that GEN-1 averaged 99% success across selected simple manipulation tasks, while noting that the result does not extend to every task. I see Physical AI in 2026 as a practical software shift, not a claim that humanoids will suddenly replace workers. What Robotics Foundation models actually do. A robotics foundation model, or RFM, is a broadly trained model that can be adapted across tasks and environments. Many RFMs use a Vision-Language-Action architecture. A VLA receives images, language instructions, and robot-state information, then produces joint positions, velocities, or gripper commands. Physical Intelligence's π0 paired a vision-language backbone with an action system that outputs low-level motor commands across several robot types. First Speakers Announced for ODSC AI West 2026! Build full-stack AI skills alongside AI's leading voices. Explore specialized technical tracks designed to elevate your expertise and take your models from concept to deployment. Action tokens, memory, and grounding. The central challenge is grounding: turning "pick up the red mug" into accurate movement, contact, and grip force. Physical Intelligence's FAST tokenizer compresses action sequences using the discrete cosine transform and byte-pair encoding. The team reported training up to five times faster than its earlier approach while preserving dexterity on tasks such as folding laundry. Long-horizon work also requires memory. In 2026, Physical Intelligence introduced Multi-scale Embodied Memory, which stores recent observations alongside longer-term notes about task progress. The system supports tasks lasting up to 15 minutes while limiting inference context. | Dimension | Large Language Model | Robotics Foundation Model | | Input | Text and sometimes images | Images, language, proprioception, sensors | | Output | Text or software tokens | Joint targets, trajectories, velocities, or torques | | Latency budget | Often hundreds of milliseconds or more | Tight, continuous real-time control | | Failure consequence | Incorrect information | Equipment damage or human injury | How the robotics engineering stack changes. The older stack typically followed computer vision, pose estimation, inverse kinematics, trajectory planning, and a low-level PID controller. The embodied AI stack starts with a pretrained RFM, adds post-training through teleoperation, imitation learning, or reinforcement learning, and deploys the policy behind runtime safety controls. Engineering effort therefore moves upstream. Teams spend more time curating demonstrations, defining evaluations, and capturing failure trajectories. Simulation becomes core infrastructure. NVIDIA describes Isaac Sim as an open framework for robotics simulation, testing, and synthetic-data generation. Deployment adds distillation, quantization, compilation, and edge optimization. A learned policy may generate actions at tens of hertz while classical servo and safety loops run faster beneath it. The model remains one component inside a real-time system. Latency, safety, and non-determinism. Latency is the most immediate constraint. A chatbot can pause; a robot arm cannot pause while holding a fragile object. Real-time action chunking lets a model prepare future actions while the robot executes the current sequence, reducing discontinuities between chunks. Non-determinism creates another problem. Neural policies can behave unexpectedly when lighting, geometry, contact, or sensor data differs from training conditions. Hybrid stacks are essential: collision detection, torque limits, emergency stops, control barrier functions, and fallback controllers must operate beneath the learned policy. A 2026 review identifies latency, physical-data scarcity, safety guarantees, and the embodiment gap as persistent barriers. Physical data is also expensive. Manipulation trajectories require hardware, operators, and time. Human-video pretraining, teleoperation, simulation, and reinforcement learning reduce the burden, but representative real-world data remains necessary. RFMs therefore make classical controls engineers more important because deterministic safeguards must constrain probabilistic policies. First Speakers Announced for ODSC AI West 2026! Build full-stack AI skills alongside AI's leading voices. Explore specialized technical tracks designed to elevate your expertise and take your models from concept to deployment. Where Physical AI is reaching production. Warehousing offers the clearest near-term use case. Ambi Robotics trained PRIME-1 on more than 20 million images spanning over 150,000 hours of warehouse operations, then applied it to 3D perception, picking, placement, and quality control. This can help systems adapt to package variation without a custom CAD model for every object. Manufacturing is moving toward high-mix, low-volume assembly, where mobile manipulators switch between parts and instructions. Humanoid development is advancing through integrated platforms. NVIDIA's 2026 Isaac GR00T reference design combines humanoid hardware, dexterous hands, Jetson Thor computing, and an open workflow for training, evaluation, and deployment. Conclusion: the competitive edge has moved. Physical AI is not replacing hardware engineering, controls, or safety validation. It is changing where the greatest engineering advantage comes from. As robotics systems become more intelligent, the ability to connect foundation models, simulation, edge inference, evaluation, and deterministic safeguards will become increasingly valuable. The competitive edge will belong to engineers who can do more than optimize one component of a robotics stack. It will belong to those who understand how to build, test, and deploy complete Physical AI systems that operate reliably in the real world. Build the skills behind Physical AI at ODSC AI West 2026. If Physical AI is becoming part of your roadmap, now is the time to strengthen the skills behind it. At ODSC AI West 2026, you can learn directly from practitioners, AI engineers, and researchers working across AI engineering, agentic systems, infrastructure, machine learning, and Physical AI. Join us October 27-29, 2026, at the Hyatt Regency SFO in Burlingame, California, for hands-on training and technical sessions designed to help you turn emerging AI capabilities into practical engineering skills.