Six months ago, we were leaning into the robotics space, proactively sourcing opportunities — but still only seeing a few inbound pitches a month. Today, that number has skyrocketed. In just half a year, we’ve met with robotics companies spanning the gamut – from those building robotics foundation models (RFMs) to full-stack robots, humanoids, and the tooling that powers them.
The industry is booming, with venture capitalists pouring over $7 billion into robotics companies in 2024 alone. Mega-rounds in companies like Figure ($675M Series B), Physical Intelligence ($400M Series A), and Skild ($300M Series A) signal a major surge in investor appetite for robotics. The global robotics market is forecasted to grow exponentially, with industrial robotics alone projected to reach around $60 billion by 2034 and service robotics expected to grow to about $99 billion by 2029.
While robotics is quickly becoming one of the most dynamic and fast-moving categories in AI, it’s also one of the most technically complex, with a steep learning curve – particularly for investors evaluating new players. Unlike LLMs — where standardized benchmarks provide clear performance metrics — robotics does not have a universally accepted framework for comparing capabilities across companies. This complexity stems from the field’s unique position at the crossroads of AI, hardware design and engineering, supply chain, manufacturing, and real-world deployment – all of which require different expertise to build towards a successful company, as well as a different set of criteria for investors to assess. In short, bringing AI to the physical world is harder than bringing AI to the digital world.
As investors, we aim to engage early — not only to support promising businesses, but to play a constructive role in how this technology develops. Robotics is no longer science fiction; it’s a rapidly unfolding reality with the potential to transform how we live, work, and build.
As AI begins to shape the physical world, we see a rare convergence of technological progress and meaningful opportunity. From warehouse automation to generalist robotic form factors, these systems don’t just execute tasks — they can learn, adapt, and improve in real-world environments. The companies building them are laying the groundwork for a future that’s more efficient and more resilient — and, if developed thoughtfully, one that augments work without losing the critical role people play.
To support others exploring this space, we recently put together a primer on the market opportunity, the unique challenges of investing in robotics, and our framework for evaluating companies in the category. It’s a deep dive, so we’ve outlined our top three takeaways for evaluating robotics startups here:
1. Look for interdisciplinary excellence and future-facing leadership.
Robotics isn’t just an AI problem — it’s a convergence of software, hardware, data, manufacturing, and operations. Winning companies need top-tier talent across each of these disciplines early, but pedigree isn’t enough. We look for teams who operate with first-principles thinking, build on modern technical architectures, and have a long-term vision aligned with where the industry is headed — not where it’s been.
2. Don’t trust the demo — interrogate it.
To truly gauge a robot’s capabilities, it’s important to understand the context behind the demo. Is the system operating fully autonomously or with some degree of teleoperation? Are the objects or environments arranged to simplify the task? Whenever possible, observe the system in person. Performance in uncontrolled environments — especially when things don’t go exactly as planned — is often a more useful signal than a polished demo. If appropriate, gently interrupt the robot’s workflow to see how it responds.
3. Evaluate real-world performance, not just potential.
With no universal benchmarks, investors must rely on a company’s own definitions of success. Ask about measurable metrics like task success rates, throughput, and autonomy duration. Understand how long deployments take, what training is required, and whether the data strategy creates a feedback loop for continual improvement. Ultimately, the most promising robotics startups pair technical depth with scalable deployment models and a clear ROI narrative for customers. This is one of the learnings from the last wave of robotics – being stuck in POC purgatory.
As the AI generation of robotics startups matures, VCs need to learn from previous cycles. Many robotics companies from the 2014-2015 era got trapped performing one-off integrations for each customer without clear paths to broader implementation and scale. Current robotics companies benefit from dramatically improved hardware efficiency, scalable data collection methods, and AI capabilities that weren’t available in previous cycles. The convergence of progress in these areas puts robotics in a position to finally go mainstream.
As digital AI advances rapidly, the physical world represents the next major automation frontier. While AI models augment white-collar workers across software engineering, customer support, and data analysis, physical labor solutions remain largely untapped. Technical moats that are eroding in software, where AI democratizes development, remain strong in robotics due to the complexity of physical world integration.
The promise isn’t about automating labor, but about building systems that augment human capabilities and continuously learn and improve through real-world deployment. These are long-arc, highly technical businesses — and over time, their compounding data advantage and deep integration with physical environments create competitive moats that purely software-based models will find increasingly difficult to replicate.
Investors willing to thoughtfully evaluate these multidisciplinary companies will be the ones helping build and transform the physical world for our future.
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.
