Why Open Standards Matter for Physical AI in 2026
Physical AI is no longer a concept from a research lab. By 2026, we see autonomous robots in warehouses, drones doing last mile delivery, and smart manufacturing lines that adapt in real time. These systems combine sensors, actuators, and AI models that run at the edge, often with strict latency and power constraints. The hardware underneath these systems is as important as the software, and that is where open standards become critical.
Open standards in hardware mean that the instruction set architecture (ISA) is publicly documented, extensible, and not controlled by a single company. For developers, this translates to predictable behavior across chips, better tooling support, and the freedom to optimize for specific workloads without waiting for a vendor to release a proprietary SDK. When MIPS, a long standing player in embedded processors, joins RISC-V International as a Premier Member, it signals that even established companies see the value in collaborating on open foundations rather than going it alone.
For founders and engineering leads, the takeaway is simple: the days of building on fully proprietary hardware stacks for AI are fading. Open standards reduce risk. If you are designing a product that will ship in 2027 or later, you want to build on an architecture that will have community support, multiple silicon vendors, and a clear upgrade path. RISC-V, with MIPS now contributing, offers exactly that.
MIPS and RISC-V: What the Premier Membership Actually Changes
When a company like MIPS becomes a Premier Member of RISC-V International, it is not just a ceremonial move. Premier membership comes with a seat on the board and a strong voice in the technical direction of the ISA. MIPS brings decades of experience in embedded and high performance computing, particularly in areas like DSP (digital signal processing) and vector extensions, which are directly relevant to AI workloads.
Concretely, MIPS will contribute to the development of RISC-V extensions for AI and machine learning. This could include vector operations, matrix math, and specialized instructions for neural network inference. For developers, this means that future RISC-V chips will have better support for running AI models efficiently, without requiring a separate GPU or NPU in every device. That is a big deal for physical AI platforms where power and space are limited.
Another change is the acceleration of toolchain maturity. With MIPS on board, we can expect more mature compilers, debuggers, and performance analysis tools for RISC-V. If you have ever struggled with a proprietary toolchain that only works on one vendor's chip, you know how liberating it is to have a standard that every vendor supports. This collaboration reduces the fragmentation that has historically plagued embedded development.
How Physical AI Platforms Differ from Traditional AI Software
Most AI development today happens in the cloud. You train a model on a GPU cluster, deploy it to a server, and call it via an API. Physical AI is different. The model runs on a device that is moving, sensing, and interacting with the real world. The constraints are tighter: latency must be in milliseconds, power consumption must be low enough to run on batteries, and the system must operate reliably in unpredictable environments.
Consider a warehouse robot that uses computer vision to pick items from shelves. The AI model must process camera feeds in real time, identify objects, and send control signals to the arm, all within a few milliseconds. If the model runs on a cloud server, the network round trip alone would make the robot too slow and unreliable. So the inference must happen on the edge, on a chip that is optimized for both performance and energy efficiency.
Open standards help here because they allow developers to tune the hardware to the specific needs of their application. With RISC-V, you can add custom instructions for your exact workload. For example, if your model uses a particular activation function, you can implement it in hardware and get a speedup that is impossible with a fixed architecture. This level of customization is a key reason why physical AI platforms are moving toward open standards.
Practical Implications for Developers Building AI Solutions
If you are a developer or a founder building AI solutions, the shift toward open standards has several practical implications. First, you will have more choices in hardware. Instead of being locked into a single vendor's chip, you can select from multiple RISC-V implementations that meet your performance and cost requirements. This competition drives down prices and accelerates innovation.
Second, you can expect better software support. The RISC-V ecosystem now includes mature Linux distributions, real time operating systems, and AI frameworks like PyTorch and TensorFlow that run on RISC-V. MIPS's involvement will likely improve performance for AI workloads, making it a viable option for edge inference.
Third, you should start thinking about portability. If you design your software to be architecture agnostic, you can easily migrate between different RISC-V chips as new ones appear. This is similar to writing code that runs on both x86 and ARM, but with more flexibility because RISC-V is extensible. Use abstraction layers and standard libraries to avoid tying your code to a specific vendor's extensions.
At Devs & Logics, we have seen clients benefit from this approach in their SaaS MVP development projects. By building with open standards from the start, they avoid rework when hardware changes or when they need to scale to new devices.
Choosing the Right Architecture for Your AI Product
When you are choosing an architecture for your AI product, the decision is not just about raw performance. You need to consider the entire ecosystem: tools, community, long term viability, and the ability to customize. RISC-V offers a compelling option, especially for physical AI, because it is open and extensible. But it is not the only option. ARM and x86 are still strong, especially for cloud based AI.
For edge devices, RISC-V is becoming increasingly attractive. The vector extensions, which are now being enhanced with contributions from MIPS, provide the SIMD (single instruction, multiple data) capabilities needed for AI inference. If you are building a product that requires real time processing on a low power device, RISC-V is worth serious consideration.
However, there are tradeoffs. The RISC-V ecosystem is younger than ARM's, so you might encounter fewer ready made IP blocks and peripherals. You may need to invest more in custom hardware design or work with a partner who specializes in RISC-V. But the long term benefits, including no licensing fees and full control over the ISA, often outweigh the initial friction.
For founders, the advice is to prototype early. Get a development board, run your AI workload on it, and measure the performance. You might be surprised at how well it works, and you will have concrete data to guide your decision.
The Role of Open Standards in Reducing Vendor Lock-In
Vendor lock-in is a silent killer for many AI startups. You build on a proprietary platform, and as you scale, the costs and limitations become apparent. Open standards are the antidote. When the underlying architecture is open, you can switch vendors, negotiate better pricing, and even modify the hardware to suit your needs.
Consider the software stack. With RISC-V, you have access to open source compilers, operating systems, and libraries. This means you are not dependent on a single company's SDK. If a vendor goes out of business or changes their roadmap, you can move to another implementation without rewriting your entire codebase.
This is especially important for physical AI platforms, which often have a long life cycle. A robot deployed in a factory might need to run for 10 years. If the chip vendor disappears, you need to be able to source replacements or migrate to a new chip. Open standards make that migration feasible.
At Devs & Logics, we emphasize this principle in our consulting work. We help clients design systems that are portable and standards compliant, so they are not held hostage by any single vendor. This approach has saved our clients significant time and money over the long run.
What This Means for SaaS and Web Platform Development
You might be thinking: I build SaaS products, not robots. Why should I care about hardware standards? The answer is that AI is increasingly moving to the edge, and even SaaS platforms need to support edge devices. If your SaaS product controls a fleet of IoT devices or uses on device inference, the hardware choices of your devices affect your backend architecture.
For example, imagine you are building a SaaS platform for smart home security. The cameras in the home run AI models locally to detect motion and recognize faces. These models are trained in the cloud, but they must run efficiently on the device. The choice of hardware for those cameras impacts the model size, the update frequency, and the cost of the device. By using open standards like RISC-V, you have more flexibility in selecting hardware that meets your cost and performance targets.
Moreover, open standards can simplify your development pipeline. If you use standard interfaces and formats, you can deploy models to a wide range of devices without custom integration. This is similar to the benefits of using a standardized API in web development. It reduces complexity and accelerates time to market.
If you are just starting a SaaS product that will incorporate AI, consider the full stack. Our AI integration guide covers how to plan for edge AI and open standards from the beginning. It will save you from painful pivots later.
Getting Started with Open Standards in Your Next AI Project
So, how do you actually get started with open standards in your AI project? First, educate yourself on the RISC-V ecosystem. There are many development boards available, from low cost options to high performance ones. Pick one that matches your target workload and start experimenting.
Second, engage with the community. RISC-V has a vibrant community of developers and companies. Attend conferences, join mailing lists, and contribute to open source projects. MIPS's involvement will only strengthen this community, providing more resources and expertise.
Third, design your software to be portable. Use standard libraries, avoid vendor specific extensions unless absolutely necessary, and abstract the hardware layer. This will make it easier to move between different RISC-V implementations and even to other architectures if needed.
Finally, consider partnering with experts. At Devs & Logics, we have helped many clients navigate the complexities of AI hardware and software integration. Whether you are building a physical AI platform or a SaaS product that needs edge AI, we can provide guidance and development support. Feel free to reach out for a consultation.
The move by MIPS to lead open standards development for physical AI platforms is a clear signal that the future of AI is collaborative and open. For developers, this is an opportunity to build more flexible, cost effective, and innovative products. The tools and standards are here in 2026, and the time to start is now.