Eric joined F-Prime in 2021 as a Venture Partner focused on executive leadership and go-to-market functions in technology and cybersecurity companies. Eric has founded and helped lead organizations from startup to $100 million plus revenue, including RiskRecon (acquired by Mastercard), Trusteer (acquired by IBM), Imprivata (IPO), and Centra Software (IPO).
Eric earned his undergraduate degree from Wharton School of University of Pennsylvania and his MBA from MIT Sloan School of Management.
It took longer than many thought, but robotic systems are starting to show up in average people’s lives.
Self-driving taxis now ply the streets of major American cities, it’s now commonplace to see shelf-scanning robots roaming grocery store aisles, and humanoids dance, flip, and carry objects in videos on our social media feeds. As we’ve tracked for several years now, investors are pouring money into robotics startups — some with practical business cases, others explicitly chasing a massive disruption of the entire world economy. As long-time investors in the robotics space, it’s clear to us that the industry is now experiencing a long-sought period of momentum. So what’s fueling it?
We recently hosted two of the world’s leading roboticists — iRobot co-founder Colin Angle and Director at MIT’s Laboratory for Information and Decision Systems Sertac Karaman — in F-Prime’s Cambridge offices to discuss how advances in AI are unlocking new opportunities for robotics startups. And while the general sentiment is that we are in the early days of this AI wave, we have a “golden opportunity” to capitalize and usher in a robotics “golden age.” Here’s how.
Golden Opportunity vs Golden Age
As exciting as self-driving cars and humanoids are, most of the robotics companies receiving all that attention are not yet solving problems at scale. To move from media hype to real value creation, entrepreneurs must avoid the traps that have befallen previous waves of robotics innovation: start with real-world problems in need of a solution, not cool technology in search of an application. AI is dramatically enhancing machines’ ability to perceive, plan, and make decisions, creating a window for companies that can build genuinely useful robots instead of a generation of machines in search of a reason to exist.
So what is the role of humanoids? For now, they are generating excitement and showcasing what could be possible with AI-powered perception and control. However, commercialization is still a long way off. Nevertheless, the R&D work at the cutting edge of building humanoids is delivering technology advances that smart entrepreneurs can leverage to build viable businesses, like dual-arm manipulators, real-time human interaction, spatial awareness. Industrial cleaning, infrastructure inspection, goods delivery, and elder care are all ripe for robotic automation and technically viable with AI systems that can navigate spaces, operate in unstructured environments, and work alongside humans,.
Smart entrepreneurs are already mapping physical AI opportunities by asking: what level of dexterity is required? Does it need emotional intelligence? How generalizable must the system be? From that framework, they’re building task-specific robots that solve pressing problems now, not 10 years from now.
The Role of AI Foundation Models in Robotics
Think of robotics foundation models as a generative pre-trained transformer (GPT) for motion and manipulation. The challenge here is that while robotics data is available, it’s expensive. Entrepreneurs can’t scrape the internet for robotic grasping demos. Collecting and labeling real-world interaction data at scale is hard, slow, and costly, with an uncertain payoff for startups. It is ultimately a game of who raises the most capital, which is out of reach for most startups.
As a result, smaller, skill-specific models will be much more valuable in the near term. Foundation models for warehouse navigation, or picking and placing in cluttered environments, or understanding facial expressions and verbal language will be cheaper to build, quicker to deploy, and easier to tune. For businesses going after a specific, real-world use case, these smaller foundation models will offer early traction and real ROI without waiting on a billion-dollar general-purpose robotics brain.
From Tools to Real-World Impact
Recent AI breakthroughs are answering a long-standing question in robotics, which is whether the technology’s limits lay in software or hardware. As we’ve seen, today’s robots can walk, run, and even do backflips because of smarter code and learning systems, not some radical new motor.
AI is creating a new toolbox for robotics engineers. Ten years ago, basic robotic manipulation was unreliable, but AI has enabled the creation of perception stacks that understand 3D environments, reinforce learning to teach agile motion, and adjust in real-time via smart control systems. Thanks to these tools, robots can now fold laundry and scramble over rubble — not exactly headline-grabbing use cases, but they are the foundations of real businesses with real revenue.
AI-Assisted Design
When designing chips, AI can optimize transistor placement far better than human engineers. We’re now seeing the same dynamic in robotics. AI-enabled simulators are starting to bridge the sim-to-real gap, making robot training faster and easier. At the same time, simulations combined 3D printing and rapid prototyping enable engineers to significantly shorten design cycles.
As a result, the future of robotic design may not be intelligible to humans. AI systems trained through simulation and reinforcement learning could generate optimal algorithms and designs that weren’t obvious to human engineers, just like AlphaGo will play moves that no human had previously imagined.
The real transformation in robotics is happening under the hood, in the tools, workflows, and enabling technologies that AI has brought to life. The robotics industry currently faces a golden opportunity. But the golden age will follow after a few standout successes — built on the most useful system, not the flashiest robot — show what’s possible.
Spinwheel is revolutionizing the consumer credit ecosystem. The company partners with lenders, marketplaces and personal financial management platforms to provide real-time, verified consumer credit data to process payments as part of their clients’ existing workflow and operations via APIs and its agentic AI platform.
Most users are very willing to link their bank accounts with popular financial applications like Venmo, Coinbase, and Robinhood. However, the data exchange that underpins those seamless monetary transfers is merely the first iteration of a new paradigm in the financial world: open finance.
Open finance enables a world where consumers and financial service providers can access financial data like spending, investments, payroll, loans, and tax information via API. With it, they can understand their financial situation and make better decisions about budgeting, borrowing, lending, and financial products. With questions of technical capability and regulatory permission mostly settled, the ability of data aggregators to deliver timely, comprehensive consumer financial data now steers the industry’s movement towards a world of open finance.
Yet while open finance tools now provide consumers with access to information about their financial assets, information about their debts and liabilities remains far less accessible, more opaque, and more scattered. The consumer debt market is an enormous part of the US financial ecosystem. Federal Reserve Data reports $19T of outstanding consumer liabilities. Equifax data shows that Americans are on track to originate 134M consumer loans across auto, bank and private label cards, mortgages, HELOC, consumer finance, and student categories in 2025 alone. Consumer debt is also a critical source of stress for many American households.
Unlike the asset side of their lives, data about the average American’s debts and liabilities are spread across multiple loan providers running legacy tech stacks. All this in an industry where nuanced, granular, and real-time data is essential for consumers to know exactly how much they owe, to whom, and when it’s due. Consumers are eager to budget, manage debt, and improve their credit scores, and banks and FIs are eager to meet the need with budgeting applications, credit monitoring, and credit-building tools. All that’s missing is the data aggregation connecting the two.
At F-Prime, we’re proud to have backed foundational companies like Quovo, Even Financial, and Kensho that allowed consumers and financial services providers unprecedented visibility and access to users’ financial assets. Now, we’re excited to partner with Tomás Campos, Tushar Vaish, and the Spinwheel team to do the same for their liabilities. Spinwheel is an API for consumer liability data that provides consumers, banks, lenders, and fintech companies with the ability to aggregate, understand, and manage consumer debt. It uses proprietary direct integrations and agentic workflow to collect permissioned, consumer liability data across credit card transactions, mortgages, auto loans, student loans, personal loans, and more, with the highest degree of coverage and accuracy in the market today. We’re excited to lead Spinwheel’s $30M Series A and join them at the forefront of financial infrastructure.
Originally published in Forbes
The AI revolution is moving so much faster than previous technological shifts. While the mobile internet took nearly a decade to reach 90 percent household adoption, ChatGPT achieved the same user penetration in just two years. This accelerated cycle is creating companies that reach incredible scale in record time, but it’s also rewriting the venture capital playbook. The traditional rules of SaaS investing are being challenged, and the moats we once relied on are becoming less defensible. Based on recent discussions my Eight Roads Ventures colleague, Michael Treskow, and I have had with our team, here are ten ways investors are changing how they evaluate AI startups today.
1. Agents Are the Future — Not Just Co-Pilots
The first wave of AI applications was dominated by “co-pilots” — tools that assist humans. The next, more powerful wave is characterized by “agents” — autonomous systems that complete tasks from beginning to end. These agents are transforming traditional “systems of record” into “systems of action.” As an investor, the key question goes beyond the earlier paradigm of “does this make a workflow more efficient?” Now, investors must ask, “can this automate the workflow entirely?” How (and to what degree) humans are involved will depend on the AI-use-case fit, enterprise risk appetite, and the existing workflow. As an example, Roo Code has multiple modes, from code mode to architect mode, based on customers’ specific needs. Early breakouts are already emerging in specialized fields like cybersecurity (penetration testing agents), DevOps (debugging agents), and financial services (memo generation agents), showing the power of vertical agents.
2. Traditional SaaS Moats are Diminishing
The three defensive moats that defined the SaaS era are eroding:
Implementation Friction: In the past, the high cost and complexity of implementing enterprise software, especially in regulated industries, created stickiness. Today, AI agents can write code and automate implementation, drastically lowering switching costs.
Workflow stickiness: SaaS used to be the system of record, deeply embedded in the enterprise workflow. But now that agents are performing the workflow entirely, it could reduce the friction of migrating.
Data Gravity: The effort of migrating data from one system to another created a powerful lock-in. Now, AI models can automatically ingest and structure data from various sources (including emails, calendars, and documents) making it far easier to populate a new system, and thereby reducing the stickiness of the incumbent.
3. Enterprise Knowledge, Trust, and Observability Are the New Defensibility
With the underlying models increasingly turning into an API-accessible commodity, differentiation is shifting up the stack to the application layer. The most defensible companies are building new moats around enterprise knowledge, trust, and observability.
When considering workflow integration, investors must figure out how deeply the product is embedded within a customer’s core business processes, or how well the agents internalize the enterprise knowledge if there is forward-deployed engineering. Just like a service provider, the more an agent has absorbed the enterprise’s organizational and operational intricacies and preferences, the harder it is to replace. The second moat, centered on becoming a trusted, default partner, is related to an older sales and marketing principle: In a confusing market, enterprises are looking for a trusted guide to shape their AI strategy. The first vendor to gain a customer’s trust and become their “default” AI partner gains an immense advantage, with the ability to expand across the organization.
The low barrier to entry means that for any given problem, a dozen well-funded players can emerge almost overnight. This has made product-market fit (PMF) a potentially transient advantage. A company might find a temporary fit and grow to a few million in ARR, only to be outflanked by a competitor with a new feature or a slight improvement in the model. As an investor, you must constantly ask: is this PMF durable?
5. The “Incumbents Are Slow” Argument Is No Longer a Given
Two ideas — that incumbents will be slow to act and that customers building in-house solutions will fail — that once formed foundational pillars of venture investing have now been turned on their head.
Incumbents now have access to the same powerful APIs as startups. And while cultural inertia at enterprises remains a challenge, the technical barrier to entry has been lowered, and the proprietary data they have accumulated over the years will give them a head start. Similarly, with modern orchestration tools like Thread, Onyx, or n8n, it’s becoming more feasible for customers to build their own bespoke AI agents in-house. A startup’s competition is no longer just other startups, but also its own customers and the very incumbents it aims to disrupt.
6. TAM May Increase, but Advantages Become Less Obvious Once Pricing Normalizes
A critical shift in the AI era is the expansion of the total addressable market (TAM) beyond traditional software budgets. AI companies can now tap into two distinct enterprise spending pools. “Co-pilot” models, which assist human users, are typically sold on a per-seat basis and compete for existing software budgets. Autonomous “agent” models complete workflows end-to-end, are sold on a per-outcome basis, and hold more transformative potential.
AI agents are positioned to capture a share of the much larger services budget, effectively replacing costs previously allocated to human labor or outsourced services. However, while the opportunity to capture the services budget is immense, it is not a blank check. As some founders have noted, many are generating eight-figure savings while charging customers six-figure prices. As agent-based solutions become more common, the price for automated labor will inevitably face downward pressure and normalize, meaning the initial advantage of charging rates comparable to human labor may not be sustainable long-term.
7. Team Composition Looks Different at AI Companies
AI-native companies are operating with unprecedented efficiency. While a company like Cursor can have great PLG motion and reach $100M ARR with around 30 employees, most enterprise AI companies build a GTM team to reach scale. In a confusing market with intense competition where perceived product differentiation is limited, GTM makes all the difference. On the tech side, CTOs with an ML background will be more essential in the foundational model and middleware layer than in the application layer. Having a Head of AI to stay on top of the latest feature releases and skate to the right opportunity will create a nice complement as the CTO scales the technical organization and infrastructure.
8. SaaS Metrics Still Matter, but in a Different Way
LTV/CAC is still relevant, but velocity matters more. The “Triple, Triple, Double, Double, Double” (T2D3) growth model for top-tier SaaS is being replaced by an even more aggressive trajectory. Some have suggested the new top-quartile metric is “Quintuple, Quadruple, Triple.” For example, a company would grow from $1M to $5M, $5M to $20M, and $20M to $60M over three years. While this velocity is exciting, it can also be misleading. Rapid adoption in a hot market doesn’t guarantee a large TAM or durable revenue. While there is no public benchmark for churn metrics for AI companies yet, we know some of enterprise AI companies’ net revenue retention (NRR) at month 12 is well above 100 percent to compensate for the logo churns — see Glean at 120 percent, Writer at 160 percent, and Jasper for enterprise at 163 percent.
9. Scrutinize Gross Margins and Unit Economics
AI companies often have high compute and model inference costs. While we see margins improve over time, investors must be vigilant about how costs are reported. As others have noted, companies may claim impressive gross margins even though a closer look at their P&L reveals millions in API calls and compute costs categorized under R&D. When re-categorized correctly, their margin was actually negative. Investors must always dig into the P&L to understand the true cost of goods sold.
10. Customer Love Doesn’t Guarantee Retention. Product Usage Is The True PMF
In a normal market, a high net promoter score (NPS) is a strong signal of future retention, but not necessarily in the current AI landscape. Customers may unanimously love a product today, but the market is evolving so quickly that a better alternative may appear in six months. Many enterprises are intentionally building flexibility into their tech stacks to easily swap vendors, so founders and investors alike should beware of “vibe revenue.” Therefore, look beyond NPS to metrics like product usage, which is a leading indicator of retention. Beware of “stealth churn,” where customers who are still paying see less frequent usage, or use a product for a lower percentage of their entire workflow.
dataplor provides point-of-interest (POI) data for businesses across the economy for geospatial analyses, operational workflows, and growth initiatives. The company’s data service is refreshed regularly with rigorous quality checks, and goes far beyond basic business names and addresses, enriching each location with brand, transaction, persona, foot-traffic estimates, hours of operation, sentiment scores, popularity metrics, and more, providing an unmatched combination of depth, breadth, and global coverage.
LeadIQ is a workflow centric lead data and sales prospecting SaaS platform focused on enterprise and mid-market clients. LeadIQ allows users to research and capture potential leads easily, enrich leads with further details, and integrates into various sales acceleration and customer management platforms like Salesloft, Outreach, Hubspot and Salesforce.
An Eight Roads investment. Eight Roads is F-Prime’s sister venture capital investment group, with which F-Prime collaborates on investments outside of the Americas and Europe.
* Denotes activity with the non-F-Prime investment team.
Roark automates the creation of voice agent test suites from real customer journeys and AI-driven edge-case predictions, so businesses can validate every path before deployment and ship reliable voice AI faster.
Myolab builds personalized human digital twins with embodied intelligence to accurately predict an individual’s physiology, cognition, and behavior, and enable hyper-personalized experiences in health, search, and e-commerce.
We’re incredibly excited to share our 2025 update to our annual State of Robotics report. The report is a comprehensive analysis of more than 1,500 robotics companies globally, including a company-by-company exploration of the use case each is pursuing.
We invite you to download the report here, and reach out to authors Sanjay Aggarwal and Betsy Mulé.