Traditionally, creating hardware has been slow, costly, and reliant on lengthy cycles of prototyping and testing. But today, artificial intelligence (AI) and large language models (LLMs) are reshaping the way innovators approach every stage of the process.
From customer discovery to prototyping, AI-driven tools allow teams to move faster, smarter, and more closely aligned with customer needs. For early-stage innovators trying to get hardtech products off the ground, embracing these tools isn’t just about efficiency — it’s about staying competitive in a market where leveraging every available tool gets you to market faster than your competitor, without losing intention.
Together, AI and LLMs are becoming critical co-pilots for physical product development — extending a founder’s capacity and unlocking insights that would otherwise take weeks of manual work.
Artificial Intelligence (AI) refers to a broad set of technologies that automate, predict, optimize, and generate outcomes. In hardware development, this might mean an AI simulation predicting material fatigue, a robot using machine vision to recognize an object and deciding how to pick it up, or a generative design algorithm optimizing a bracket’s geometry.
Large Language Models (LLMs) like OpenAI’s GPT-4 or Anthropic’s Claude specialize in processing and generating text. They can research markets, draft technical documents and code, assist in ideation, and accelerate repetitive tasks such as writing product manuals or grant applications. Claude and Curor AI are particularly useful at generating computer code complex scripts and in-depth debugging.
One of the most immediate ways AI and LLMs are changing hardware product development is in writing and debugging code. Firmware and embedded software often require developers to juggle multiple languages (C, Python, etc.), manage complex dependencies, and repeatedly test against physical hardware. LLMs can dramatically streamline this process.
For example, an engineer might begin by prompting an LLM to generate a script that initializes sensors or controls a motor driver. Instead of combing through hundreds of pages of documentation, the developer can describe the hardware environment and receive a tailored code scaffold that already follows best practices.
Once code is running, debugging is often one of the most time-intensive tasks. AI-assisted tools can analyze error messages, trace logs, and even suggest fixes in real time. For instance, if a microcontroller consistently crashes after a certain interrupt, an LLM can point out memory handling issues or suggest alternative coding structures. This creates a faster “test–fix–deploy” cycle, where developers spend less time searching for solutions on forums and more time fine-tuning performance. The end result is accelerated iteration speed and a significant reduction in costly trial-and-error.
“This creates a faster “test–fix–deploy” cycle, where developers spend less time searching for solutions on forums and more time fine-tuning performance.”
AI also supports more advanced tasks like automated code reviews. Tools can evaluate scripts for efficiency, flag potential vulnerabilities, and even refactor blocks of code into cleaner, more maintainable formats. In the context of hardware startups, where firmware engineers are often stretched thin, this becomes a force multiplier: the AI acts like an extra set of eyes on every commit, raising the overall quality of the embedded systems that bring products to life.
A startup building a wearable health device needed to program firmware that managed both a heart-rate sensor and Bluetooth Low Energy (BLE) communication. Traditionally, this would have required weeks of developer time to sift through sensor datasheets, configure libraries, and debug connection issues. Instead, the team leaned on an LLM to generate an initial driver script for the sensor, with the correct register configurations and initialization sequences already in place.
When they ran into repeated BLE disconnects, they pasted the error logs into the LLM, which suggested memory buffer adjustments and corrected how the interrupt service routines were structured. This not only fixed the issue but also optimized battery usage which is a critical factor for a wearable. By using AI as a coding partner, the team cut their firmware development cycle from six weeks to under three, freeing engineering time to focus on industrial design and user testing.
“When they ran into repeated BLE disconnects, they pasted the error logs into the LLM, which suggested memory buffer adjustments and corrected how the interrupt service routines were structured.”
One of the hardest parts of developing a new product is knowing whether you’re solving the right problem. Traditionally, product teams rely on surveys, focus groups, or costly market studies to test their assumptions. These methods are effective, but they’re also time-consuming and often out of reach for early-stage startups. LLMs now make it possible to dramatically accelerate this process.
By simulating a large number of respondents, LLMs can run early-stage exercises such as surveys, Kano analyses, or conjoint studies. For example, a founder can describe their target customer persona and then prompt the AI to “role-play” as that customer across hundreds of variations. The output doesn’t replace real-world customer validation, but it helps uncover blind spots, generate hypotheses, and highlight potential priorities before investing heavily in outreach.
This approach allows developers to quickly test trade-offs: Would customers value durability over weight? Would they pay more for faster performance, or do they prioritize affordability? Instead of waiting weeks for data, AI can provide directional insights in hours, guiding what to test with actual customers next. The ability to rapidly and cheaply identify promising features, eliminate low-priority ones, and refine value propositions gives resource-constrained teams a powerful head start in shaping their solution.
“The ability to rapidly and cheaply identify promising features, eliminate low-priority ones, and refine value propositions gives resource-constrained teams a powerful head start in shaping their solution.”
At its best, AI functions as an “idea stress test” that exposes assumptions, reveals unexpected patterns, and surfaces which problems are most worth solving. When combined with even a small amount of real-world validation, these AI-driven exercises make early hypothesis testing both faster and more informed, ensuring that limited resources are pointed toward the opportunities with the highest likelihood of impact.
AI and LLMs are no longer experimental add-ons to the product development process. They are becoming essential tools for teams building physical products. Whether it’s identifying customer needs before a single prototype is built or accelerating firmware development, these technologies are collapsing timelines and lowering barriers that used to slow innovators down.
The real opportunity lies in using AI not as a replacement for human creativity and engineering, but as a multiplier to extend what founders, engineers, and designers can accomplish.
As the hardware landscape grows more competitive, the teams that win will be the ones that learn how to blend human intuition with AI-driven speed and precision. The message is clear: the future of hardtech belongs to those who embrace these tools early and learn to co-create with them.
mHUB helps innovators put these tools into practice every day, from AI-driven prototyping to and beyond. If you’re looking to accelerate your own product development journey, connect with mHUB to explore how to bring your ideas to market faster.