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Rescale’s $115 Million Series D: Accelerating AI-Driven Physics Simulations

Rescale’s $115 Million Series D: Accelerating AI-Driven Physics Simulations

Rescale’s Journey and Early Growth

Founding and Mission: Rescale was founded in 2011 by Joris Poort and Adam McKenzie in San Francisco with the vision to bring high-performance computing (HPC) to the cloud for engineering and R&D. Poort, a former Boeing engineer, saw firsthand the need for massive computing power in design (he helped save Boeing $180 M by optimizing the 787’s design) and set out to “eliminate computing complexity” for scientists and engineers​. Rescale’s platform, often described as “Intelligent Computing for Digital R&D,” enables organizations to run complex simulations on hybrid cloud infrastructure with ease​.

Early Support and Funding: The startup quickly attracted high-profile backers. It joined Y Combinator in its early daysand secured a $6.4 M seed round in 2015, backed by prominent angel investors including Sam Altman, Jeff Bezos, Richard Branson, and Peter Thiel​. This was followed by a Series A of $14 M in 2016, led by TransLink Capital with participation from Microsoft’s venture arm and others​g. These early investments validated Rescale’s approach and funded its expansion into global markets and support for more industries (“from automotive design to drug discovery and even rocket science,” as Poort noted)​.

Scaling Up (Series B and C): In July 2018, Rescale raised $32 M in Series B financing led by Initialized Capital, alongside Keen Venture Partners and SineWave​. This brought its total funding at the time to about $52 M. The Series B round helped Rescale build out its cloud HPC platform and onboard large enterprise customers, helping them migrate “95% on-premise” legacy simulation workloads to hybrid and cloud environments​. In 2021, Rescale’s Series C funding totaled $105 M, pushing its valuation above $1 billion and making it a cloud-HPC unicorn​. The Series C came in two parts (an initial $50 M and an expanded round to $105 M later in 2021) and drew in new strategic investors like Microsoft’s M12, Samsung, and Hitachi Ventures. By 2022, Rescale had raised over $200 M in total and was recognized as a leader in cloud HPC, partnering with major cloud providers (AWS, Azure, Google Cloud, Oracle) and simulation software vendors (ANSYS, Siemens, etc.) to offer a comprehensive platform​.

Funding Timeline


Rescale’s funding rounds from 2015 to 2025 show a steady growth in investor support. Early rounds (seed, Series A, B) provided single- to double-digit millions, while later rounds (Series C in 2021 and Series D in 2025) jumped into triple digits, reflecting Rescale’s accelerating momentum. Series D is the largest infusion yet at $115 M. This sustained funding trajectory underscores investor confidence in Rescale’s vision and the rising importance of cloud HPC in industry.

Inside the $115 M Series D Funding Round

Round Highlights: In April 2025, Rescale announced a Series D raise of $115 million in venture financing​. This round brings Rescale’s total funding to over $260 million to date​. The infusion was led by strategic investors from both the hardware and software sectors, signaling broad confidence in Rescale’s approach. Key participants include Applied Ventures (the VC arm of Applied Materials) and NVIDIA – the world’s leading AI chipmaker – as well as Hitachi Ventures, Foxconn, Hanwha, NEC’s Orchestrating Future Fund, Prosperity7 (Aramco’s fund), Translink Capital, SineWave Ventures, the University of Michigan, and Y Combinator’s Continuity Fund, among others. Notably, this round did not include new funding from some earlier big-name backers like Amazon or Microsoft, but their involvement in prior rounds set the stage for this diverse investor mix​. The inclusion of manufacturing giants (Applied, Foxconn), electronics leaders (NVIDIA, Hitachi), and even an academic institution (University of Michigan) reflects how critical AI-driven simulation is across domains — from semiconductors to industrial design and beyond.

Investors’ Perspectives: The new investors provided strong endorsements of Rescale’s strategy. NVIDIA’s CEO Jensen Huang has highlighted Rescale’s platform as “a way for industries to push the limits of AI-driven modeling and simulation”, underscoring the synergy between Rescale’s cloud HPC environment and NVIDIA’s AI-centric hardware​. Applied Materials, as a leader in semiconductor equipment, sees value in Rescale to accelerate design of chips and materials via simulation. This strategic backing suggests Rescale’s technology is viewed as enabling next-generation innovation in hardware development. Furthermore, repeat participation by funds like Y Combinator (which originally incubated Rescale) signals continued conviction in the company’s growth path.

Use of Proceeds – Accelerating AI+Engineering: According to Rescale’s founder and CEO Joris Poort, the Series D funds will be used to “expedite the growth of [Rescale’s] engineering platform for advanced computing, intelligent data, and applied AI”​. In practice, this means Rescale is investing heavily to establish a unified data framework and “digital thread” that connects all modeling and simulation workflows on its platform​. By unifying simulation data, Rescale can introduce powerful AI-driven search, tagging, and automation features on top of it, making it easier for engineers to find insights and reuse knowledge across projects. A key goal cited is to “enable more organizations to automate the use of AI-enabled physics tools and libraries,” which can yield “1000× speed improvements in design validation”​. Essentially, Rescale plans to leverage AI to dramatically speed up how companies test and validate designs. Poort emphasized that today’s innovators face bottlenecks in computing capacity, siloed data, and the complexity of deploying AI – and that “Rescale removes these barriers, empowering every engineer and scientist to accelerate discovery, scale impact, and shape the future faster”​. This statement from leadership encapsulates the intent behind the funding: to break through current limits in simulation by marrying HPC with AI.

Leadership and Stakeholder Quotes: In the official announcement, Joris Poort framed the funding as a means to “turn ideas into reality faster” for engineers and scientists, highlighting Rescale’s mission to eliminate constraints of compute and data. This aligns with Poort’s long-term vision of democratizing access to supercomputing-level resources for R&D. The fact that early tech visionaries like Sam Altman and Jeff Bezos were seed investors​, and now industry incumbents like NVIDIA and Applied are leading Series D, speaks volumes. It suggests a handoff from visionary early capital to strategic industry capital as Rescale matures. Stakeholders expect Rescale to become the de facto platform for AI-driven engineering simulation, and their quotes reflect that optimism. (For example, investors noted Rescale’s role in “empowering R&D teams using applied AI, advanced computing, and intelligent data management”​.) This round’s success also positions Rescale for a potential future IPO or expansion into new markets, given the scale of funding and stakeholder expectations.

AI-Driven Physics Simulations – Rescale’s Technology & Impact

Rescale’s Cloud HPC Platform: At its core, Rescale provides a cloud platform that seamlessly connects engineers to massive computing power for simulations. It integrates with all major cloud providers and on-premise HPC clusters, acting as a “single pane of glass” to run computing jobs on the best available resources​. Through a web-based interface, users can deploy thousands of CPUs or GPUs on demand, running industry-standard simulation software (from computational fluid dynamics to finite element analysis) without worrying about the underlying IT complexity. This platform-as-a-service approach is secured and optimized for performance – as described by one user, “Rescale’s cloud HPC platform lets us be simulation-driven throughout product design… [it] quickly disseminates simulation results to all stakeholders”​. In practice, an engineer can take a legacy on-prem simulation workload and, with minimal changes, run it on Rescale’s cloud environment, often much faster or in parallel batches. Rescale automates the heavy lifting of scheduling, scaling, and tuning HPC jobs​, so organizations can focus on engineering, not on managing supercomputers.

AI Integration in Simulation Workflows: What truly sets Rescale apart now is how it is infusing AI into physics simulation workflows. Traditional simulation yields tons of data (e.g. terabytes of results from many design iterations). Rescale is building technology to leverage this data by training AI surrogate models – machine learning models that approximate the behavior of complex physical systems​​. For example, Rescale can take the results of many high-fidelity fluid dynamics simulations and train an AI model to predict outcomes (like drag or lift) for new designs almost instantly. Poort explained that while a single high-fidelity simulation might take 3 days on a supercomputer, the trained AI model can run in under a second and predict results with ~98% accuracy. Engineers can thus explore a much larger design space quickly using the AI predictions, then validate the top candidates with one final detailed simulation run. This approach of combining AI with HPC yields the best of both worlds – orders-of-magnitude speed gain without sacrificing accuracy at the end. Indeed, Rescale reports that using AI-enabled physics tools could offer “1000× speed improvements” in design cycles for certain problems​. Beyond surrogate models, Rescale is also integrating AI for things like intelligent job scheduling (choosing optimal hardware for a given task) and automatic metadata tagging of simulation results for easier search and reuse. These features essentially make Rescale’s platform “AI-aware”, turning raw simulation data into a growing knowledge base that AI can mine for insights.

Key Use Cases and Benefits: Rescale’s AI-driven simulation capabilities are benefitting a range of industries:

  • Automotive & Motorsports: Companies like General Motors (Motorsports division) use Rescale to rapidly iterate car designs​. For example, Rescale can simulate how air flows around a race car, then use an AI model to quickly evaluate hundreds of aerodynamic tweaks. This dramatically shortens the design of high-performance vehicles. Engineers can achieve faster design validation (days or weeks saved per iteration) and improve race car aerodynamics under tight competition timelines.
  • Aerospace & Defense: Aerospace firms (including the U.S. Dept. of Defense and companies working on aircraft design) leverage Rescale for fluid dynamics and structural simulations​. An aircraft wing or rocket component can be optimized with far fewer physical prototypes because AI-guided simulations explore the design space thoroughly. Boeing’s experience (where Rescale’s founders saved $180 M by weight optimization) is a testament to the cost savings possible​. Now, with AI surrogates, digital prototyping is even more powerful – for instance, testing hundreds of wing shapes via AI in the time it used to take to test one. National defense projects also benefit from secure, scalable compute for things like computational chemistry (e.g. new materials or energetics) with AI accelerating the discovery of viable candidates.
  • Semiconductors & Electronics: With investor ties to Applied Materials and Samsung, it’s clear chip designers and electronics firms are using Rescale. Simulation of semiconductor processes or electronic circuits can be extremely compute-intensive. Rescale’s platform lets these firms tap into cloud supercomputers for chip design verification, and now AI-trained models can predict outcomes of manufacturing process tweaks or circuit configurations faster. This speeds up R&D for next-gen chips. As an example, materials science simulations (like how a new material behaves under stress or heat) can be sped up by AI “supermodels” that incorporate physics knowledge​, yielding up to 100× faster discovery of materials​ – critical for the semiconductor industry’s fast innovation cycles.
  • Life Sciences & Pharma: Drug discovery and biotech companies use physics-based simulations (e.g. molecular dynamics, protein folding) to discover new therapeutics. Rescale provides the GPU computing muscle for these tasks. With AI, Rescale can help create surrogate models of biochemical simulations – for instance, predicting protein-ligand binding affinities without running a full simulation every time. This can accelerate drug candidate screening dramatically. One reported case in the industry is AI models running 10,000× faster than traditional biology simulations in certain scenarios​(e.g. neural nets predicting drug molecule interactions). Such speedups mean researchers can consider vastly larger libraries of compounds, increasing the chance of finding effective drugs.
  • Energy & Manufacturing: Rescale’s platform is used in energy (e.g. by SLB in oil & gas engineering​) to simulate reservoirs or new tools, and in advanced manufacturing to simulate processes like 3D printing or climate systems. AI-enhanced simulations in these fields allow companies to optimize complex processes (like a factory production line or an oilfield extraction method) much faster. Digital twin approaches – where a virtual model of a physical system runs and updates in parallel to the real system – are empowered by Rescale’s cloud HPC. With AI, these digital twins can update in real-time or predict future system states quickly, aiding in proactive maintenance and design tweaks. The Series D funding’s focus on a unified digital thread will particularly help these use cases: all the data from design, simulation, and real-world operation can feed into AI models that continuously improve product performance.

Competitive Differentiators: Rescale distinguishes itself from both traditional on-premise HPC setups and other cloud providers by a combination of technology and strategy:

  • Turnkey HPC & Software Ecosystem: Rescale offers a library of 800+ pre-integrated engineering software applications on its platform (from ANSYS to MATLAB), ready to run at scale. Enterprises don’t have to install or license these tools on their own cluster – Rescale’s partnerships ensure software is available and optimized. This “one-stop-shop” for simulation software in the cloud is a huge convenience versus the patchwork one might deal with on a generic cloud service.
  • Hybrid & Multi-Cloud Flexibility: Unlike a single-cloud vendor, Rescale is cloud-agnostic and hybrid by design​. It can route jobs to AWS, Azure, Google, or even on-prem clusters based on efficiency, cost, or data locality. This means a customer isn’t locked in and can always access the best price-performance or specific hardware needed. Traditional HPC environments usually have fixed capacity and hardware; Rescale can always tap into more resources and the latest processors available on the market.
  • AI-Driven Optimization: Rescale’s platform uses AI not just for simulation models but also for operational efficiency. It intelligently suggests optimal compute configurations for a given workload (choosing among various CPU types, GPUs, interconnects) to minimize runtime and cost. This kind of compute recommendation engine is unique, leveraging the platform’s data on thousands of runs. Over time, the system learns which hardware or cloud region runs a particular simulation fastest or cheapest. Enterprises thus benefit from continuous optimization – a level of efficiency hard to achieve in static on-prem clusters.
  • Enterprise-Grade Controls and Security: Large companies choose Rescale in part because it provides fine-grained controls (security, compliance, budgeting) akin to on-prem IT, but with cloud agility​. Rescale integrates with corporate authentication, encrypts data in transit and at rest, and offers audit logs – crucial for sectors like defense and aerospace (hence DoD’s usage​). Traditional HPC might keep data on-site for security, but Rescale has proven its cloud security robust enough for even government clients, while also relieving those clients of maintaining aging infrastructure.
  • Rescale vs. Traditional HPC – Feature Comparison: The table below summarizes how Rescale’s AI-driven HPC platform compares to a traditional on-premises HPC approach:

The Broader AI + HPC Simulation Landscape

Market Growth and Size: Rescale’s big funding round is not happening in isolation – it reflects a broader boom in the convergence of AI and high-performance computing for simulation. The market for AI-driven simulation and digital twin technology is soaring, with one forecast projecting growth from about $3.7 B in 2024 to over $81 B by 2034​. This represents an astonishing ~36% CAGR over the decade​, making it one of the fastest-growing segments in enterprise tech. Driving this growth is the realization across industries that AI can unlock much more value from simulation data (faster design cycles, predictive maintenance via digital twins, etc.). Meanwhile, the broader HPC market itself continues to expand (expected to reach ~$50 B in coming years) with cloud-based HPC growing 2.5× faster than on-premise HPC infrastructure as organizations shed traditional datacenter constraints​. The infusion of AI is turbocharging what was once a steady, niche market (technical computing) into a mainstream priority for innovation.

Market Forecast Chart – AI-Driven Simulation:


Analysts project exponential growth in the global market for AI-enhanced simulations and digital twins. The chart above illustrates a forecasted jump from $3.7 B in 2024 to $81.3 B in 2034, as AI becomes integral to engineering and industrial simulations​. This surge (over 20× growth in a decade) is fueled by rapid adoption of AI/ML techniques alongside physics-based modeling in industries like manufacturing, aerospace, and energy. Investors pouring funds into companies like Rescale echo this optimistic outlook – expecting AI-driven simulation to become a cornerstone of R&D, analogous to how CAD and CAE tools revolutionized design in earlier decades.

Competitive and Partner Ecosystem: The trend of AI+HPC in simulation has spurred activity from both startups and tech giants:

  • Cloud Providers: Hyperscalers like Amazon AWS, Microsoft Azure, and Google Cloud have all launched specialized HPC services and even digital twin platforms to capture this market. They offer GPU and high-speed interconnect instances and often partner with companies like Rescale (indeed, Rescale runs atop these clouds) to reach enterprise simulation users. Azure’s acquisition of Cycle Computing in 2017 (a Rescale-like startup) and AWS’s Direct Connect for HPC are examples of big cloud investing in this space​. However, many enterprises find value in an independent platform like Rescale that can broker across clouds and provide software expertise rather than using each cloud’s tools separately.
  • HPC Hardware Firms: Hardware vendors (NVIDIA, Intel, AMD, etc.) are deeply interested in this domain because AI-driven simulation creates demand for cutting-edge chips. NVIDIA’s investment in Rescale’s Series D is a case in point – they foresee increased use of GPUs not just for AI training, but for running hybrid AI-physics workloads on platforms like Rescale​. Similarly, companies like Graphcore or Cerebras (AI accelerator startups) might align with simulation platforms to showcase their hardware on novel workloads (e.g. using AI chips to run physics-informed neural networks). This ecosystem ensures that as new chips emerge, the simulation community gets access to them through cloud services.
  • CAE Software Companies: Traditional simulation software leaders (ANSYS, Dassault Systèmes, Siemens PLM, etc.) are also adding AI features to their products. Some have launched their own cloud offerings or marketplaces. For example, ANSYS has been embedding AI to accelerate meshing and solver convergence. These incumbents often partner with Rescale so that mutual customers can run ANSYS (or others) on Rescale’s cloud, and possibly leverage Rescale’s AI capabilities on top. The collaboration between CAE software and cloud HPC is symbiotic: software vendors provide the domain-specific tools, while platforms like Rescale provide the scalable execution and data layer.
  • Startups and New Entrants: Apart from Rescale, there are startups focusing on niche aspects of AI in simulation. For instance, companies working on physics-informed neural networks (PINNs) offer frameworks to solve partial differential equations with AI. Others focus on specific domains (e.g., OnScale – acquired by Ansys – for cloud simulation in MEMS, or Mirantis for CFD as a service). While some enterprises could assemble point solutions from these, Rescale’s broad platform approach gives it an edge in offering an integrated solution. The large Series D funding for Rescale might spur consolidation in the industry, as smaller players might align with bigger platforms or get acquired to fill technology gaps.

Trends Driving Adoption: Several broader trends have reinforced the importance of AI-driven HPC simulations:

  • Digital Transformation & Remote Work: The COVID-19 pandemic forced many engineering teams to work remotely, which in turn accelerated the shift to cloud-based simulation tools (admins couldn’t access on-prem clusters easily)​. This period proved that cloud HPC can deliver, and many companies did not revert back fully to old ways. Now, with cloud-first strategies, adding AI into the mix is simpler – companies have their data centralized and accessible to train AI models.
  • Increasing Model Complexity: Whether it’s a finer mesh in a CFD simulation or a more detailed multi-physics model of a system, the computational demand for state-of-the-art simulation is growing. Pure brute-force approaches are costly and time-consuming, so there’s strong incentive to incorporate reduced-order models or AI approximations to handle complexity. For example, instead of simulating every second of a rocket engine firing, engineers can simulate key points and let an AI interpolate the behavior in between. This need dovetails with what Rescale is enabling through its new funding – making AI a co-pilot in every simulation workflow, to handle the repetitive or high-dimensional searches.
  • Enterprise AI Investment: Companies worldwide are investing in AI strategies, and a lot of focus has been on business analytics or computer vision. Now that low-hanging fruit has been picked, attention is turning to “scientific AI” – using machine learning for engineering and scientific problems. The term “AI for Science” has gained traction, with initiatives to apply AI to things like climate modeling, material discovery, and yes, physics simulations. This trend means that R&D budgets are allocating funds to projects that combine simulation experts with data scientists. A platform that already merges these worlds (like Rescale) is poised to capture this shift.
  • Regulatory and Market Pressure: In sectors like automotive and aerospace, regulators are encouraging more simulation to ensure safety before physical testing (for example, virtual crash tests or flight simulations to certify designs). At the same time, market competition pushes companies to reduce time-to-market. AI-accelerated simulation addresses both needs: it allows more thorough testing virtually (improving safety and compliance) and cuts down development cycles (a competitive advantage). The recent funding in Rescale indicates that investors see long-term value in a company that sits at the intersection of these pressures, providing the tools to do more with simulation in less time.

Future Outlook

Implications and What’s Next for Rescale

Impact on Industry and Enterprise R&D: Rescale’s rise – exemplified by this Series D milestone – is a bellwether for how industries will conduct R&D in the coming years. The infusion of AI into simulation promises to shorten product development cycles significantly. For enterprises, this means faster time-to-market for innovations: cars, planes, chips, drugs, and more can be developed with fewer physical prototypes and testing loops. A claim of “1000× faster design validation”​hints at a future where what used to take weeks might be done in hours, dramatically speeding up innovation pipelines. This could redefine competitiveness – companies that harness AI-driven simulation will out-innovate those that stick to traditional methods. For example, an automaker might be able to virtually crash-test a new car design hundreds of times via AI models before ever building a single physical unit, identifying optimal safety improvements in days. This level of rapid iteration can lead to safer, more efficient, and more reliable products across industries. Enterprises will need to invest in upskilling their workforce – blending domain experts with AI expertise – to fully leverage these new tools. We may see the role of “simulation engineer” evolve to “simulation data scientist,” where understanding how to interpret and train AI models becomes as important as knowing physics theory.

Democratization of Supercomputing: A significant implication of Rescale’s model is the democratization of capabilities that were once the domain of only well-funded corporations or government labs. With cloud accessibility and pay-per-use pricing, smaller companies or startups can access top-tier simulation and AI resources without owning a supercomputer. This flattens the playing field in sectors like aerospace or biotech, where historically only big players could afford extensive simulation. In turn, we can anticipate an upswing in innovation from new entrants – imagine a startup designing a new rocket engine entirely through AI-guided cloud simulations, something that would have been prohibitively expensive a decade ago. By lowering barriers, Rescale and similar platforms could catalyze a new wave of distributed R&D, where even geographically dispersed teams collaborate in a virtual high-performance lab. The Series D funding will likely go into further enhancing Rescale’s user experience and lowering the entry hurdles (through more automation and guided workflows), making advanced simulation tech more user-friendly for non-HPC-experts.

Ecosystem and Workforce Transformation: As AI-driven simulation becomes mainstream, the ecosystem around it will adapt. We’ll see closer collaboration between hardware vendors, software providers, and platform companies to ensure seamless solutions. Industries like testing and certification may develop new standards for validating AI-generated simulation results (to ensure that regulatory requirements are met when using surrogate models, for instance). The engineering software industry might shift pricing models (perhaps charging for AI augmentation or cloud use) as opposed to traditional per-seat licenses, in response to platforms like Rescale. On the workforce side, companies might reorganize their R&D departments to integrate data science teams with CAE (computer-aided engineering) teams. There will be a learning curve – as one Reddit discussion noted, “a surrogate model is only as accurate as the training data”, meaning engineers must learn to trust but verify AI results​. Over time, best practices will emerge for validating AI predictions against physical tests, and roles will adapt to this new workflow.

What to Expect Next from Rescale: With $115 M fresh in the bank, Rescale has a robust roadmap ahead:

  • Product Innovation: Expect Rescale to launch more AI-driven features on its platform. This could include a marketplace of pre-trained physics AI models for common simulation types, so users can plug in a ready-made surrogate (e.g., a fluid flow predictor) into their projects. They will also likely enhance their data analytics dashboard, giving R&D managers higher-level insights (trends across thousands of simulations, anomaly detection, etc.). The integration of NVIDIA’s AI infrastructure (like NVIDIA DGX Cloud)​ hints that Rescale might offer specialized AI training environments so customers can train large physics ML models entirely on the platform.
  • Scaling Customer Reach: Rescale will probably use part of the funding to grow its global sales and support teams. Having “hundreds of enterprise customers” now​, they may target thousands in the next few years, including more government agencies and mid-sized firms. We might see industry-specific solutions or templates to attract new users (for example, a pre-configured workflow for automotive crash simulation with AI, or a package for pharmaceutical molecular modeling). This vertical focus helps speak the language of each industry’s problems.
  • Partnerships and Ecosystem: Given the strategic investors, new partnerships are likely. We could see closer collaborations with Applied Materials (perhaps offering unique features for semiconductor manufacturing simulation on Rescale) or joint solutions with NVIDIA for digital twins. Rescale might also partner with consulting firms to help enterprises implement AI-driven simulation in their processes (essentially building services around the platform). Additionally, they may pursue partnerships in academia, providing their platform to universities for research – this seeds future industry users and also builds credibility via published scientific results achieved on Rescale.
  • Open Source and Community: An interesting direction could be Rescale contributing to open-source AI-for-science libraries or supporting open datasets for simulation, to position itself as a leader in the community. This would align with attracting AI researchers to use Rescale for large-scale experiments (e.g., training a physics-informed neural net that requires a cluster of GPUs).
  • Long-Term Trajectory: Investors in a Series D of this size are often looking toward an exit or massive scale-up in the coming 2–3 years. It wouldn’t be surprising if Rescale gears up for an IPO once it demonstrates strong revenue growth and market dominance in this niche. By fortifying its technology moat now (with AI and data), Rescale is making itself not just a cloud reseller but a unique platform that could justify a public valuation. In the meantime, competitors will be watching: this funding may spur others (like cloud providers or big CAE companies) to consider acquisitions or increased investment in their own offerings. Rescale will need to stay ahead by continuously innovating. If they execute well, Rescale could become the backbone of how next-generation products — from supersonic jets to personalized medicine — are engineered.

In summary, Rescale’s recent $115 M Series D round is a strong endorsement of the convergence of cloud HPC and AI for physics-based simulations. It marks not just a funding milestone for the company, but a validation of a broader shift in how we approach engineering problem-solving. By enabling simulations to run faster and smarter (with AI), Rescale is helping break the traditional trade-off between accuracy and speed. The ripple effects will be felt across enterprises as they adopt these tools to innovate more rapidly. As Rescale continues to develop its platform post-Series D, we can expect the frontier between simulation and artificial intelligence to blur even further – leading to an era where the phrase “tested in simulation” carries much more weight, thanks to the intelligence gleaned from all those virtual experiments. The coming years will be exciting as we watch Rescale and its peers push the boundaries of what’s possible in digital R&D, fundamentally reshaping how products are designed and perfected in the age of AI.

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