Blog Archive - F-Prime Capital

The State of Fintech in 2026

It’s here! All subscribers to Fintech Prime Time can access the full 2026 State of Fintech report via the F-Prime Fintech Index.

But first, save your spot with the F-Prime team for a virtual presentation and discussion of our findings on Tuesday, February 24 at 12pm ET / 9am PT.

The fintech industry has experienced its ups and downs over the last five years. In 2021, the F-Prime Fintech Index market cap rose to $1.3T, followed by a swift correction in 2022 when the Index bottomed out below $400B. The effects of that correction lingered into 2023, but started a slow and steady rebound in 2024. By the end of 2025, the F-Prime Fintech Index was almost back to $1T.

At the same time, 2025 was the year we could definitively say three things. First, the fintech investments of the last decade have produced multiple new industry giants that lead in their respective categories — Nubank, Affirm, Stripe, Toast, and Robinhood, to name a few. Second, crypto has earned its seat next to traditional finance (TradFi). We expand on both these points in the State of Fintech report. Finally, 2025 was not the year of AI in financial services, at least relative to its early adoption in other industries and functions like coding, customer service, and legal. However, it is coming quickly and we anticipate future State of Fintech reports will show a lot more adoption.

The first months of 2026 brought sharper market discipline than many expected, eliminating over 80% of the Fintech Index market cap gain between year-end 2024 and 2025. Despite the Q1 2026 sell-off, we believe financial services providers will ultimately benefit more from AI than be disrupted by it. The outlook is less forgiving for legacy technology vendors serving financial institutions, many of whom risk being displaced by native agentic architectures. For now, however, public markets appear to be painting the sector with a broad brush.

A Thaw in Public Fintech Markets

16 fintech companies went public in 2025, 11 of which were VC-backed. Despite subpar performance for many of these companies in the public markets (as of 12/31/2025 only two traded above their IPO price, and six traded above their last private round valuation), the IPO window is officially open. More public listings are on their way — already three more in 2026!. Meanwhile, fintech M&As are showing even greater signs of health, rebounding to pre-2021 levels.

Revenue multiples also continue to rise — over the last two years, investors have prioritized so-called “goldilocks” companies that are neither growing too fast nor too slow while approaching profitability. As for the companies comprising the F-Prime Fintech Index, fundamentals continue to strengthen. They grew at an average of 29% over the last year, with every sector seeing meaningful increases in net income margins since the growth-at-all-costs mindset that characterized the 2021 peak.

A New Generation of Financial Services Giants

The last 15 years have produced new industry heavyweights. Much like Uber, PayPal, and Square were initially dismissed yet came to lead their respective industries, so too have companies like Nubank, Affirm, Stripe, Toast, and Robinhood become leaders in theirs.

If measured against US standards, Revolut, SoFi, and Nubank would now rank in the top 1.5% of American banks if they were chartered in the US. Each has nearly $30B in deposits. In payments, Stripe and Adyen were tied for fifth place in the list of top global merchant acquirers, each with around $1.4T in TPV, while Toast processes an estimated 15% of the restaurant industry’s payment volume.

So the fintech wave of the 2010s has now officially produced its first generation of giants, but there are many others still waiting in the wings. Roughly $1.8T of venture capital has been invested in the category over the last decade, returning an estimated $2.4T. But $4.2T remains locked up in innovative private companies, with fintech making up around $0.6T of that total, including some of the most valuable fintech companies like Stripe ($107B), Revolut ($75B), and Ramp ($32B).

Crypto Grows Up

As of 2025, we can officially say that the crypto industry has earned a front-row seat alongside TradFi, crossing a number of thresholds that show real integration with the broader economy. For starters, issuers like Blackrock and Fidelity contributed to a total of more than 75 new crypto ETFs launched in 2025. This marks a structural shift in the makeup of the crypto market. At the same time, regulators’ posture towards crypto meaningfully shifted in 2025, paving the way for further institutional adoption moving forward.

And then there are stablecoins, which crossed $1T in monthly volume in 2025. Stablecoins may be the best example of a “killer use case” in crypto. Stablecoins could reduce the cost of remitting $200 from $20-30 via bank transfer to less than $1.

Following the initial adoption of stablecoins and tokenized treasuries, we can now wonder whether any financial asset will not be tokenized in the next 10 years. The next few years will see an expansion of tokenization across a wider spectrum of asset classes, including real estate, private credit, and other private funds.

AI Has Not Transformed Fintech (Yet)

There has been a lot of hype, but 2025 was not the year of AI in fintech. For now it remains a huge, mostly untapped opportunity — financial services is responsible for more than 20% of GDP in the US, but the industry currently has one of the lowest adoption rates for AI agents.

We knew that financial services would lag behind other industries, and for good reason. Accelerated AI adoption works for industries where:

Context is text-heavy instead of numbers-heavy,
Existing systems of record are easy to integrate with,
Stakes are relatively low and imprecise values are still valuable, and
There is low regulatory exposure.

Financial services strike out on most of these points.

In the broader enterprise software space, nearly three quarters of every dollar invested now goes to AI companies. In the fintech vertical, that number is closer to one third. Since the launch of ChatGPT, fintech has produced a lower percentage of unicorn companies, and those that reach unicorn status are usually not AI-native.

However, we know that financial services is a worthy vertical for AI to tackle. The large models are already building for financial services — OpenAI in payments, Anthropic in financial research — but we believe startups can differentiate on workflow, integrations, and domain knowledge.

By the end of 2025, the primitives for nearly every sector of fintech have been put in place, and they are now ready for a new AI-native application layer to be built on top. We expect the coming years to be exciting and critical ones for AI in financial services and commerce, and it’s time to put the next generation of building blocks in place.

We’ve Never Been More Excited for the Future of Financial Services

If you’re as passionate about fintech as we are, there are so many reasons for excitement.

The regulatory landscape has never been more open to crypto innovation and adoption, and stablecoins are revolutionizing the way money flows around the world. Crypto ETFs are unlocking new pools of capital, and tokenization promises to create a more efficient infrastructure for all asset classes.

It’s still early days for AI in fintech, but the technology is already redesigning the way financial services businesses underwrite risk, design products, allocate capital, and serve their customers. And that’s before we consider AI’s role in determining how consumers earn and save, spend and pay, borrow and build wealth.

The last decade forged the next generation of great financial services companies, and AI is going to create the next.

Go deeper: Access the full report via the F-Prime Fintech Index here.

The Uncomfortable Truth About FDEs

Forward-deployed engineers (FDEs) are having a moment. Whether called “agent engineering” at Sierra, “customer engineering” at Shield, solution and sales engineering elsewhere, or their original name, “Delta”, they sit at the intersection of sales, engineering, and customer success, translating real-world complexity into product insight.

As AI products collide with messy enterprise reality – legacy systems, ambiguous workflows, and proprietary data – companies are rediscovering a Palantir-era tactic: embed engineers directly with customers to make the product actually work.

The appeal is obvious. FDEs compress sales cycles and bridge the gap between elegant demos and operational reality (hence their original name “Delta”), but they are a double-edged sword. At best, they create a tight feedback loop between the customer and product, but if done wrong, they can quietly transform software companies into bespoke service firms with bloated CAC, fragile margins, and roadmaps dictated by their loudest customers.

The question is not whether to build an FDE team, but how to design one without undermining the very economics that make software valuable.

When FDEs Make Sense, And When They Don’t

FDEs are most effective when the product is powerful but the “last mile” is highly contextual. This describes the industry standard in AI and data infrastructure, where value depends on wiring into proprietary workflows and compliance constraints that no roadmap can fully anticipate.

They are also vital in design-partner markets. When early customers effectively co-create the product, FDEs become the fastest feedback loop between reality and code. In competitive markets, the second-best product with strong FDE support often outperforms the technically superior product that customers cannot operationalize.

The Danger Zone: FDEs become toxic when custom work becomes the default. If every deal requires bespoke engineering, you don’t have a product; you have a consultancy with a logo. When “we’ll just throw an FDE at it” becomes an organizational reflex, product debt accumulates silently. Customers outsource their thinking to your engineers, and you inherit their complexity.
Rule of Thumb: If more than 30-40% of deployments require significant FDE effort, the problem is no longer go-to-market. It’s product design.

Pricing: Don’t Hide The Cost

The central tension here is economic. FDEs create real cost, but professional services revenue hurts valuation multiples.

The Services Model: Billing time-and-materials keeps margins clean but dilutes valuation. What’s worse is that customers anchor on hourly rates rather than product value.
The Bundled Model: Bundle FDE costs into the subscription price. It preserves “software-only” optics and simplifies procurement. It’s the pragmatic choice in early stages. However, it inflates subscription pricing and obscures the true drivers of CAC and gross margin.

The Solution: A milestone-based embedded model. FDE support is included in the deal but tied to defined milestones (e.g., “successful deployment”) rather than open-ended engagement. Embedding must be time-bound — usually three to six months. If customers cannot graduate from FDEs, the product is not ready.

The Metrics Trap: ARR, CAC, And Margins

For companies offering FDEs, financial planning and analysis are usually more troublesome than the actual engineering.

The uncomfortable truth is that FDEs often make metrics look better externally, but worse internally.

Revenue: Only software counts as ARR. FDE revenue should be internally unbundled, even if external reporting lumps it together.
CAC vs. COGS: Pre-sales FDE work is CAC. If you don’t track this, you will drastically overestimate your GTM efficiency. Post-sales work is Services COGS.

Finance teams must enforce an honest distinction between software margins and deployment margins. If FDEs are essential to closing every deal, your product is not yet self-serve at the enterprise level, and your P&L should reflect that.

Code Ownership And Org Design

The legal stance must be absolute: The company retains full IP ownership of all FDE work. Customers get a royalty-free license, but reusable components must flow back into the core product, not remain trapped in customer-specific forks.

Where FDEs actually sit is equally consequential:

Reporting to Engineering: Better code quality, weaker revenue alignment.
Reporting to Sales: Higher responsiveness, but a high risk of “short-term hacks” that create technical debt.

Best Practice: A dual-reporting model where FDEs sit within a Customer Engineering org but maintain a dotted line to Product. Crucially, rotate FDEs between customer sites and core development. This prevents “maintenance mode” burnout and ensures the FDE team doesn’t drift into a consulting mindset.

Compensation: Incentives Shape Architecture

Forward-deployed engineers embody your product strategy, and your comp plan will dictate their behavior.

The Sales Model: Paying FDEs commissions on closed deals encourages them to optimize for immediacy. Custom solutions multiply, and the company scales exceptions rather than a platform.
The Core Model: Paying high base salaries with no variable component produces clean code but low urgency. Architectural purity takes precedence over customer timelines.

The companies that win must reject both extremes. They pay FDEs at engineering levels (read: equity-heavy) but introduce a restrained variable component tied to outcomes that signal maturity: successful deployment, retention, and the conversion of custom work into core product capabilities.

Three FDE Archetypes In Practice

The Activator (e.g., Sierra): In AI platforms, FDEs act as “agent product managers,” translating enterprise complexity into deployable systems. This is powerful but fragile, and must therefore be temporary.
The Integrator (e.g., Ramp): In fintech, FDEs bridge the gap between modern software and legacy ERPs, banks, and internal tech stacks. They are the difference between a mid-market deal and a multi-million-dollar enterprise contract, provided they don’t let big customers hijack the roadmap.
The Infrastructure (e.g., Palantir): When every customer requires embedded engineers forever, product velocity dies. Palantir built a giant business this way, but they operate in a market with extreme switching costs and existential stakes. Most startups do not have that luxury.

The Ideal End State: Scaffolding, Not Architecture

Many startups today use FDEs to compensate for immature products, unclear positioning, weak onboarding, missing integrations, and unrealistic enterprise promises.

In the ideal model, FDEs feed R&D. Each deployment generates insight into data schemas, workflows, edge cases, and constraints. Those insights become reusable features. If three FDEs solve the same problem, the solution becomes a native capability.

The real question is not whether to build an FDE team. It is how long you plan to depend on one.

FDEs are a mirror. They reveal the gap between what your product promises and what customers actually need. The companies that win treat FDEs as scaffolding – never as architecture.

Originally published on Forbes.

From Text To Tables: Why Structured Data Is AI’s Next $600 Billion Frontier

Thanks to Chance Mathisen for his contribution.

In the current wave of generative AI innovation, industries that live in documents and text — legal, healthcare, customer support, sales, marketing — have been riding the crest. The technology transformed legal workflows overnight, and companies like Harvey and OpenEvidence scaled to roughly $100 million in ARR in just three years. Customer support followed closely behind, with AI-native players automating resolution, summarization, and agent workflows at unprecedented speed.

But industries built on structured data have not been as quick to adopt genAI. In financial services, insurance, and industrials, AI teams still stitch together thousands of task-specific machine learning models — each with its own data pipeline, feature engineering, monitoring, retraining schedule, and failure modes. These industries require a general-purpose primitive for structured data, an LLM-equivalent for rows and tables instead of sentences and paragraphs.

We believe that primitive is now emerging: tabular foundation models. And they represent a major opportunity for industries sitting on massive databases of structured, siloed, and confidential data.

How LLMs Devoured Unstructured Data (And Why They’re So Good At It)

LLMs use attention mechanisms to understand relationships between words, and simultaneously capture context, nuance, and meaning across sentences and entire documents. As these models scaled, an unprecedented supply of freely available text across the internet provided trillions of tokens that taught them how language works across domains, styles, and use cases. Models that could read, write, summarize, and reason over text suddenly became everyday business tools — drafting emails, answering tickets, and redlining contracts in seconds.

Entrepreneurs quickly recognized the pattern: plug into a foundation model’s API, wrap it in a vertical interface, solve a painful workflow, and sell seats to high-value knowledge workers. Thousands of AI-native startups followed, forming a virtuous cycle: application companies drove demand, foundation model providers reinvested in better capabilities, and improved models enabled even more powerful applications. Domain by domain, LLMs devoured unstructured data wherever it lived.

Where Current LLMs Hit A Wall: Understanding Structured Data

But LLMs were trained on text, not tables. When asked to work with structured data, they flatten spreadsheets into token sequences and strip away the meaning encoded in schemas, column relationships, data types, and numerical semantics.

The typical workaround is indirect. The model generates SQL or Python, hands it off to an external system for execution, and hopes the result is correct. This works for simple queries, but breaks down quickly. A single ambiguous column name — “revenue” versus “revenue_id” — can derail an entire analysis or forecast.

This problem compounds in large enterprises. Years of tech debt, acquisitions, and mergers leave behind dozens of siloed and brittle systems. Current LLMs and agents have greatly improved, but they still can’t confidently understand and manipulate an organization’s data which lives across different ERPs, CRMs, data warehouses, and spreadsheets. A single query can force an agent to join tables that were never designed to fit together, built by teams that no longer operate.

As a result, high-stakes sectors like financial services and healthcare remain anchored to their trusted (and sprawling) stacks of traditional ML models. Startups have built agents that write Excel formulas or execute Python notebooks via natural language, but when it comes to actuarial-level accuracy, large-scale forecasting, or multi-table reasoning that drives million-dollar decisions, the heavy lifting still falls to libraries like XGBoost and LightGBM.

LLMs can interact with structured data, but they are not the right engine to model it.

Unlocking The $600 Billion Opportunity With Tabular Foundation Models

Structured datasets require a foundation model built natively for structured data. It must understand schemas, column relationships, and numerical semantics from the ground up, rather than treating tables as flattened text.

The market opportunity here is staggering. The global data analytics market is projected to exceed $600 billion by 2030, but the industries most reliant on structured data — financial services, insurance, and healthcare — represent trillions in market cap that have yet to fully leverage generative AI.

Tabular foundation models may be the key required to unlock that TAM for startups. TFMs are trained to reason over rows and columns the way LLMs reason over sentences and pages. They deliver state-of-the-art predictions across classification, regression, and time-series tasks in seconds rather than hours.

Unlike traditional machine learning, TFMs can work with messy, heterogeneous data out of the box. They can deal with missing values, inconsistent formats, and ambiguous column names with no feature engineering, no model selection, and no hyperparameter tuning required.

A new generation of companies is building in this space, including Rowspace, Prior Labs, Fundamental, Intelligible AI, Kumo AI, Neuralk AI, Avra AI, Wood Wide AI, each exploring different architectural approaches to representing tabular and relational data, learning cross-column dependencies, and generalizing across tasks.

The operational implications of TFM are profound. Rather than maintaining a fragmented portfolio of brittle, task-specific models, enterprises can consolidate around a single foundation that generalizes across use cases. This would dramatically reduce the cost and complexity of building, monitoring, and retraining models.

But there are also real risks for startups building in this space. As LLMs get better at coding, some argue that generating analysis scripts on the fly could eliminate the need for specialized tabular models altogether. Open-source pressure may also compress technical differentiation, as happened with now-commoditized image models.

This makes distribution and business models critical. Technical advantage alone will not be durable. TFMs must be embedded into enterprise workflows, sold with clear ROI, and priced in ways that reflect the value of reliability and reduced operational overhead — before the shelf life of the technology advantage expires.

Catalyzing A New Set of Startups

For industries where AI adoption has lagged, TFMs offer a reset. Use cases that once required months of data science work — custom pipelines, bespoke features, continuous retraining — can now be tackled with a single, general-purpose model that delivers reliable results out of the box.

In healthcare, that means patient risk stratification and diagnostic prediction.

In financial services, credit decisioning and fraud detection.

In insurance, claims triage and pricing optimization.

In manufacturing, predictive maintenance and demand forecasting.

These problems have been addressed with traditional ML for years — but never with the speed, flexibility, or scalability that a foundation model enables.

For founders, this is a greenfield opportunity. Just as LLMs unlocked a wave of AI-native companies built on text, TFMs open the door to startups tackling structured-data problems that were previously too slow, too expensive, or too complex to solve at scale. As investors with a long history of investing in infrastructure and applications that power financial services, healthcare, and regulated industries, we believe tabular foundation models represent the next major opportunity to unlock AI adoption in these industries. If you’re working on tabular foundation models, building applications on top of them, or tackling structured-data problems in those industries, we’d love to hear from you.

Originally published on Forbes.

How much labor spend will AI capture? A lot, but not as much as the headlines suggest.

A core tenet has emerged that the AI opportunity is much larger than SaaS because it is going after labor spend which is 10-30x larger. At the headline level, this is undeniably true in almost every industry.

However, over the last three years we have started to see how much labor spend AI can actually capture. TLDR: It’s a lot less than the headlines, but still a large expansion from SaaS. I anticipate software spend will increase 2-3x with the addition of agentic workflows.

The answer will vary a lot by industry, but I am using this framework for sizing the AI market opportunity. I will illustrate it with customer support data, one of the earliest adopters of AI in the enterprise.

There are three main drivers.

#1 Fixed vs. variable costs. Call centers will continue to have management teams that hire and manage employees, procure technology, analyze data, and make decisions. Of a total customer support budget, it is typical to see 40% fixed costs, leaving 60% variable human costs doing the actual work of customer support.

% of jobs that AI can handle. This number will steadily rise as AI gets better and enterprises customize agentic workflows to their specific needs; however, it’s not going to be 100% of all customer support interactions for many reasons – one-off or highly complex support needs, enterprise unwillingness to integrate AI agents with high-risk systems like payments or prescription ordering, etc. However, out of the gate, we have seen AI handle 50% of chats and emails (less of voice calls), encouraging enterprises to target 75% deflection of support from live humans. It’s impossible to know where this settles, but 75% is possible, if optimistic. Over a long enough horizon, I will bet on AI’s inexorable improvement.

AI cost vs. humans. It is fascinating to see AI vendors pricing AI agents at 10-20% of their comparable unit of labor replacement. For example, it costs many companies $5-10 per customer support interaction (variable only), but AI vendors like Sierra, Decagon, and Maven often charge ~$1. That is 80-90% variable spend reduction for enterprises…and reduced market size for AI vendors. To be sure, as companies grow, their customer support interactions gr ow, and so will the AI market opportunity, but all things equal, aggressive AI pricing deflates the market size.

In summary, there might be 10-30x more labor spend than SaaS today, but it is probable that only 10-20% of that is accessible to AI. That is better news for people worried about losing jobs to AI, but worse news for investors hoping for a larger market opportunity. In the end, there are many ways AI could capture more labor spend, and even take spend from SaaS, so this framework will evolve. We will all learn together.

Behind the Breakthrough: Q&A with Kai Eberhardt, CEO and Co-founder of Oviva

Kai Eberhardt transformed a personal cancer diagnosis in his twenties into a lifelong commitment to improving patient empowerment and healthcare accessibility.

Diagnosed with cancer in his early twenties, Kai Eberhardt quickly learned how disheartening it can feel to navigate the healthcare system without information or agency. That experience became a transformational force, first pushing him toward deeper medical knowledge, then through a PhD in medical physics, and ultimately into the business of healthcare.

He co-founded Oviva in 2014 with engineer Manuel Baumann to confront one of the most widespread, but underserved, health challenges in society: chronic weight-related conditions (such as obesity and type 2 diabetes). Despite the abundance of clinical evidence showing that behavior change and lifestyle interventions can be highly effective, few systems were designed to deliver them at scale, and even fewer offered sustained, patient-centric care accessible to everyday lives.

Eberhardt and his team saw an opportunity to reimagine care delivery, starting with something simple: a secure, compliant chat app connecting patients and their care teams. Over time, that communication layer evolved into Oviva’s full-stack digital care platform, now used by more than one million patients across the UK and Europe.

On the heels of Oviva’s expansion into cardio-metabolic conditions, and after nearly a decade of building credibility and capability in systems like the National Health Service (NHS), Eberhardt shares what it takes to turn frustration into innovation, how the company is scaling with purpose, and why technology is only one part of the solution.

What gap in the healthcare system were you aiming to address in founding Oviva?

The idea for Oviva emerged from a common challenge in obesity treatment—most patients don’t continue treatment after one or two visits. It just isn’t practical for patients to regularly attend sessions in-person despite a demand for care.

What stood out was that these same patients were always on their phones, and unlike other areas of care, weight management doesn’t require physical exams, lab work, or imaging. It largely includes education, coaching, and real-time support. So, we asked: what if we digitized the same care that we provided in-person and delivered it on their phones, anytime, anywhere? That would make it dramatically more accessible, and likely more effective, too.

Can you talk more about how this model helps address affordability and equity?

People managing chronic conditions often juggle jobs, childcare, daily stress – and weight-related health is important, but not always urgent. That makes it easy to de-prioritize care, especially when it requires a visit to a doctor’s office on a random Wednesday afternoon.

Making care available on your phone, on your own schedule, changes everything. For example, look at the NHS Diabetes Prevention Programme – about 20% of people completed the in-person model, but closer to 70% completed Oviva’s digital version. That’s a massive difference.

Virtual care also opens the door to serving culturally and linguistically diverse communities. With digital delivery, you can tailor the content, language, and nutrition guidance for many different patients. Curating care is almost impossible to do well in a one-size-fits-all, in-person group setting.

You integrate clinical, nutritional, and psychological care. What makes that approach so essential?

Obesity is multifactorial – you just can’t treat it through one lens. Some people need help with nutrition education, some have complex psychological patterns or trauma, and now we also have powerful medications that should be managed by doctors. No one discipline can cover it all.

Not every patient needs every service, but having a full stack available is essential to delivering effective care. We learned this from the best in-person programs –where coordination across teams made all the difference, though it was resource-intensive and hard to sustain. By operating digitally, we can bring those same multidisciplinary perspectives together without the limits of geography or scheduling.

What makes Oviva truly different from other players in your space?

We’re with our patients every day. That’s the biggest difference. Face-to-face models might give you 30 minutes with a clinician once a month. We’re a daily companion – logging meals, giving feedback, coaching, and support throughout the day. That consistency leads to better outcomes.

We’ve published more than 90 papers showing that we outperform in-person care, and because we’re digital, we can do it at lower cost and with broader reach. We’re essentially industrializing something that used to be artisanal – making personalized, behavior-change therapy highly scalable.

Regarding Oviva’s role within the NHS – what does it take to build innovation and credibility in a system as rigorous and complex as that one?

Evidence, first and foremost. I’ve always believed in backing up what we do with strong data, while publishing results publicly to build trust and demonstrate transparency.

After that, it’s about communication – having the skills and patience to speak to very different stakeholders across the NHS. And finally, it’s about partnership. We don’t try to replace services; we instead think about how we can add value to the system through better access and efficiency. This mindset helps us prove we’re here for the long haul.

You have talked about being driven by your own personal experiences in the healthcare space. Can you share how that energy helped shape your journey as a founder?

I’ve always been a pretty intense and action-oriented person. Frustration, for me, serves as a powerful motivator because it offers clarity and urgency. I don’t sit still when I see something broken. I’m not afraid to make decisions or move fast. I think that drive helped me do something many would consider irrational – starting a health tech company from the ground up in a pretty complex space.

Obesity is a field that often carries judgment or stigma. How do you lead with compassion and evidence in that environment?

Honestly, that’s one of the most fulfilling parts of what we do. Many of our patients haven’t received good care before – they’ve been judged or dismissed by the system. When we help them see real progress, it’s incredibly rewarding.

It’s not just for the patient’s benefit either. We’ve shown, with data, that our program reduces patient sick days by about a third within six months. That translates to added productivity in the workplace, tax revenue, and long-term cost savings – things that help the entire system. So, when people ask if this population is “worth investing in,” our results make the answer abundantly clear.

What advice would you give to other founders trying to build something in or alongside a public health system?

You need grit. It takes a long time to get through validation, adoption, and scaling inside a system like the NHS. The process can be very frustrating, especially when you know your solution could help people immediately, but adoption takes time.

Some delays are for good reasons, like needing strong evidence. Other delays are due to competing interests or systemic inertia. You must keep showing up and pushing forward. The reward is that once you’re in, and your model works, it’s incredibly sticky and impactful.

What excites you the most about what’s coming next?

We’re about to launch our hypertension solution, pending final regulatory approvals. It’s been in the works for two years and is a huge opportunity to build something that serves both patients and doctors more effectively – especially in how we manage data, daily insights, and ongoing support between visits.

The role of AI in all of this is just getting started. Our AI-first care model has the potential to transform patient support, making delivery more efficient and effective. We can provide even better continuity of care between doctor visits and better inform doctors for those visits. Since the ChatGPT moment, we’ve been embedding more AI features into our product, making care more scalable and improving outcomes. AI technology and Oviva are evolving rapidly – and I can’t wait to see how far we can go.

Robotics on the Rise: The State of Robotics Investment in 2025

Updating our annual report.

We had the opportunity to provide a mid-year update on our State of Robotics report at RoboBusiness 2025. The buzz at the conference was palpable, as this year is proving to be an incredible year for robotics. The market is hitting an inflection point with investment on pace to hit record highs, public and private market valuations growing rapidly, exits accelerating, and innovation continuing to offer transformative opportunity. The future of robotics is more exciting than ever!

We invite you to download the report here, and reach out to authors Sanjay Aggarwal and Betsy Mulé.

F-Prime’s Summer Internship and Fellowship Program: Meet Our 2025 Interns and Fellows

A big thank you to our interns and fellows for their valuable contributions this summer!

This summer, F-Prime was excited to welcome a talented group of interns and fellows to our Cambridge and London offices. They played key roles in competitive landscape analysis, sourcing, founder calls, and more. Read on to discover what it’s like to be part of our internship and fellowship programs.

“This experience has deepened that interest, especially seeing how these tools might fit into real business contexts like VC. Listening to discussions where those kinds of possibilities are explored has also been hugely motivating. ”

“The work is creative. I expected rigorous diligence, but I didn’t anticipate how much of the job involves pattern recognition, storytelling, and forming contrarian but grounded views on where a field is heading. You’re constantly toggling between scientific depth and high-level strategic vision.”

“I am most surprised by how fast-paced and rapidly evolving the job is. The team has many new calls every day, while also having to study new technologies, keep up with the news, and manage the portfolio companies. I am learning a great deal about how to manage all these aspects of being a venture capitalist.”

“I have come to further appreciate how the venture framework is about asking the right questions rather than having all the answers. The best investors seem to pair scientific curiosity with disciplined judgment, which has given me a deeper appreciation for how to approach underwriting risk.”

“One thing that stood out is how hands-on and multidimensional the team is at every level. I expected sharp and high-level strategic thinking from partners, but it was refreshing to see just how engaged they are in the details; in every meeting, building models, debating sourcing strategies, refining TAMs.“

“I was most surprised by the rapid pace of innovation and how quickly the team collaborates to evaluate and act on exciting new opportunities. I also learned how important building relationships are in the VC world. it’s not only about finding good investments but also about fostering long-term relationships with founders and industry leaders.”

“I learned about F-Prime through a family friend. I decided to join as an intern because F-Prime gets to work with amazing biotech startups and help them grow as a business. Additionally, the culture at F-Prime is extremely friendly and everyone at the firm wants to help you be the best version of yourself.”

Applications for our 2026 program are not open yet, but if you are interested in learning more, please send an email to careers@fprimecapital.com.

Kanastra: Private Credit Infrastructure Gets A Full-Stack Overhaul

At F-Prime, we have long tracked the rise of alternative assets as they become a core piece of the modern investment portfolio, and the subsequent rise of infrastructure players enabling their expansion. Within “alts”, private credit has been one of the fastest-growing and most overlooked segments. With some estimates for the asset class standing at $1.8T (largely driven by direct lending in the US), others have sized the more complex asset-based finance market in emerging economies closer to $20T. In countries like Brazil, regulators are actively encouraging investment in private credit while simultaneously spurring the creation of new non-bank and fintech loan originators. Thanks to macroeconomic and regulatory tailwinds, the market has grown 230% over the last five years, driven largely by private markets behemoths such as Ares and Patria expanding their footprint into private credit. The sector’s AUM has now outpaced the technology that supports it, with both funds and originators relying on manual, headcount-heavy processes and technologies.

As co-founders of a $500M asset management firm in Brazil, Gustavo Mapeli and Manuel Netto were well-acquainted with the pain of managing a burgeoning asset class with outdated technology. The pair built and then spun out a software product that would solve those pain points, and the result is Kanastra: a back-office platform to manage private credit funds, enabling funds and originators to more efficiently structure, manage, and monitor private credit facilities.

Currently, there is very little infrastructure to support private credit markets in emerging markets, and Kanastra has emerged as an all-in-one tech platform for funds and originators alike. In a market with too many service providers to interact with on a manual basis — fund managers, fund administrators, custodians, controllers, securitization companies, BaaS platforms, loan-as-a-service providers, and monitoring agents — the company provides a tech-forward fund admin solution with end-to-end platform features. The team’s product roadmap is smart and ambitious, with plans to automate onboarding, day-to-day management, risk management, monitoring, analytics, and business intelligence.

We were thoroughly impressed by Gustavo, Manuel, and the Kanastra team when we first met in 2022, and it has been exciting to watch their growth in the years since. Kanastra is well on its way to becoming the leading fund admin provider in Brazil, serving some of the country’s largest banks (Itaú), investment management companies (XP Investments), private credit funds (Patria Investments, Vinci Partners), and originators (Solfácil, Creditas). Earlier this year, Kanastra secured a strategic investment from Itaú alongside a commercial agreement.

At F-Prime, we are proud to have backed foundational companies in the capital markets arena like Kensho, FutureAdvisor, and Canoe Intelligence. Today, we are thrilled to announce that we are leading Kanastra’s $30M Series B. Congratulations to Gustavo, Manuel, and the whole team on the milestone, and we look forward to the years of partnership ahead.

Originally published on Forbes.

From Shortages to Scale: Specialty Care in the AI Era

AmplifyMD’s $20M Series B fuels platform for scalable virtual specialty care.

We spend $1 of every $5 in the U.S. economy on healthcare. That’s exorbitant compared to most developed countries, where people live longer than we do at half the cost. Perverse incentives continue to drive unsustainable cost growth, with employer plans expected to grow by more than 9% in 2026. Our current model reduces employee take-home pay and saddles future generations with added debts to pay for today’s inefficient, fragmented system.

We need startups to clean up this unmanageable mess. To disrupt the current system, entrepreneurs must challenge health oligopolies (e.g., insurance carriers, PBMs, health systems) and chip away at the economic rent extracted by overpaid intermediaries (e.g., brokers, provider contractors, revenue cycle vendors). They also must build new platforms to enable efficient care delivery, powered by AI.

Provider shortages abound because the medical profession has long operated under an oligopoly and guild mentality, limiting the number of new physicians trained each year. Fortunately, technology now allows vastly more efficient distribution of provider time and talent, which could ultimately reverse the expected scarcity. The pandemic proved that care can be delivered effectively in virtual settings, despite primitive tools. As data has also become more portable, we may finally see the end of an era where provider systems hoard data to keep patients in high-cost settings and preserve unfair pricing power. Advances in software now allow “systems of engagement” to interface seamlessly with legacy “systems of record.” This opens the door to disrupting EHR monoliths and creating “new moats,” with AI poised to inject powerful new capabilities into outdated infrastructure.

These shifts create fertile ground for new platforms built for a digital-first, data-rich era, representing a new digital care architecture. AmplifyMD is one such platform – and today we’re thrilled to announce its $20M Series B financing. As an AmplifyMD director, I’ve seen firsthand how its EHR-integrated, AI-enabled virtual care platform helps health systems extend scarce physician capacity and drive material operational efficiencies. AmplifyMD allows specialists to practice anywhere, virtually treating patients in acute care settings and beyond, via its state-of-the-art platform. What began as a solution to expand specialist access in underserved settings has become a systemwide coverage solution trusted by some of the nation’s largest health systems—enabling physicians to extend their expertise without geographic limits.

The age of AI will further transform AmplifyMD’s product into an essential aspect of efficient and effective care delivery. The advent of superintelligent AI in medicine presents a golden opportunity for “creative destruction” to take root, but its potential requires modern platforms like AmplifyMD, which shift workflows from the in-person setting to always-on digital infrastructure. AI can enhance productivity and quality through clinical decision support today, and over time, may enable increasingly autonomous care delivery. This will give patients a greater ability to participate in decisions while receiving tailored treatment plans.

In the modern architecture of care delivery, AI agents will likely evolve to do the heavy lifting for all of us, freeing providers to focus on the highest leverage moments. With innovations like AmplifyMD’s platform, powered by this new financing, the industry can move toward greater access to high-quality care – an important step toward a system that respects our limits today while unlocking our innovative potential for tomorrow.

AI Can Turn the Robotics Industry’s Golden Opportunity Into a Golden Age

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.

Read our 2025 State of Robotics report here.