The post Tracing the Limits of Today’s AI Approaches appeared first on Frontline.
]]>In the last few weeks, two things happened that at first seemed incompatible:
1. Anthropic published a labour market report showing AI’s disruptive potential across knowledge work, with entry-level hiring slowing and entire occupational categories exposed. It’s easy to read that and think “AI is on the home stretch”.
2. Sequoia led a $1 billion seed round into Ineffable Intelligence, a pre-product, pre-revenue company founded by David Silver, the architect of AlphaGo and AlphaZero. His thesis? LLMs will never reach the next frontier because they only learn from what humans have already written down. In other words, they can only do things that humans can understand.
Personally, I’ve been using Claude to build things I don’t understand. Agents, data pipelines, workflows. I know enough to describe what I want and I can test the output. But I’m not an engineer by training, and so I’m trusting something I can’t fully explain.
How is it that while I can use LLMs to do things I can’t understand, Silver claims LLMs can only do what humans understand? And per the Anthropic report, is AI close to eating the world or have we barely started yet?
What does “understanding” actually mean?
2,400 years ago, Plato described prisoners in a cave watching shadows on the wall. They built entire theories about how shadows behave. The predictions worked. But they weren’t seeing the source itself, they were seeing a projection into the cave, dependent on multiple other variables (light, distance, the original objects). These shadows were caused by a world that existed behind them that they couldn’t see, a world beyond their own cave.
This is a useful analogy for human knowledge and understanding: we don’t truly “understand” the universe, we have models that we’ve tested and trust, but these models are sometimes built on “shadows” and dependent on truths we don’t yet understand. We can only trust models until they break.
Take Mercury. For centuries, Newton’s laws “explained” planetary motion to a high degree of accuracy. Then astronomers noticed Mercury’s orbit drifting by c.40 arc-seconds per century. To explain the aberration, they invented a planet called Vulcan, spent over 50 years looking for it, tweaked Newton’s gravity exponent, and proposed invisible dust clouds. But in essence all these attempted adjustments were trying to wedge a 3D object into a 2D shadow.
Then Einstein published general relativity and predicted the drift exactly, without any adjustments. Newton’s laws weren’t slightly wrong. They were a shadow of a deeper reality.
The pattern repeats everywhere from Ptolemy to Copernicus to Newton to Einstein. Each model was “right” until an edge case broke it. Each successor revealed the previous as a shadow of something deeper. Understanding has never meant seeing absolute reality, it means having a model that’s good enough for now.
What do LLMs “understand”?
Simplistically, LLMs are trained on language, maths, and code, i.e. the entire written output of human knowledge. And they’re extraordinary at recalling, synthesising, and generating across all of it. The genius of LLMs is their ability to generalise across knowledge work for the purpose of analysis, code generation, and tool execution etc.
But language, maths, and code are the “shadow” of human learning as opposed to learning itself. They are our “map” of the world as opposed to the “territory” (see the “Map is not the Territory”, Korzybski). This distinction matters because it defines where LLMs dominate and where they might hit a wall:
- Where the “shadow” IS the skill: Content, translation, code generation, mathematical computation. LLMs will be better than humans at these because they draw on all human knowledge simultaneously. There is no gap between the map and the territory. We see this show up in the Anthropic chart below, with LLM dominance in areas like legal, computer & math, finance, etc.
- Where the “shadow” may fall short: Physics, biology, chemistry. We’ve codified Newtonian mechanics, genomics, protein structures etc., but each codification is a shadow of something deeper we as humans don’t fully grasp. An LLM trained on physics textbooks can be brilliant at physics as we’ve codified it. But can it see beyond our “shadows” to a world that’s more fundamental than we even grasp, to point out where our frameworks are wrong, or generate new theories? While LLMs trained on the output of human knowledge are great at predicting what a human might do, this is very different from an AI that can perceive the world and learn from experience to infer things de novo.
The next-frontier: “Non-shadow models”
But the reason a $1b seed round still makes sense (let’s park the discussion on price for now) while Anthropic continues its march across these occupational categories, is that these are not the final frontier.
We should expect a new wave of opportunities in the future which are AI systems operating where human codification has been weakest: Physics, Biology, Chemistry, Materials science. In other words, the domains in which seeing beyond the shadows has the highest value.
The companies building here look different to LLMs. They’re training in simulations and physical environments. Their data is experimental results and sensor readings, not documents. They’re using Reinforcement Learning (‘RL’) and self-play. And critically, they’re in domains where being wrong about the “map” has massive consequences, and therefore massive upside for getting it right.
AlphaFold didn’t read biology papers and get better at answering questions (e.g. through next token prediction). It solved protein folding (a problem scientists worked on for 50 years) by finding patterns in structural data that no human had codified. To frame the impact of these approaches, DeepMind spun out Isomorphic Labs specifically to commercialise AlphaFold’s approach to drug discovery.
Within three years, they’d signed billions in milestone deals with leading pharma companies, raised $600m+ in funding, and Hassabis and Jumper had won the Nobel Prize. While we’re still in the early innings, this gives a sense of the commercial shape of “seeing beyond the shadows”, and unlocking problems previously unsolvable to humans.
At Frontline we’re excited by these new frontiers. Our portfolio company Stanhope AI has developed a proprietary ‘free energy minimizing’ world model for decision-making in scenarios with imperfect data. Their model attempts to mimic the human brain as its reasons in real-time dynamic environments, as opposed to predicting the next token based on the output of that reasoning.
Containing what we’ve discovered is less powerful than the ability to discover
Richard Sutton’s Bitter Lesson nails it: “We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.”
Chess engines with hand-coded opening books lost to AlphaZero, which learned from scratch. Go programs with human heuristics lost to AlphaGo, which discovered Move 37, a move that broke 3,000 years of accumulated human wisdom.
LLMs are the most sophisticated library ever built. They recombine everything humanity has written down in ways that are genuinely transformative. But the physicist in me is excited for future models that don’t work off the output of human knowledge as its starting point. Models that can build, test, and learn beyond even the dimensions and fidelity of our own human senses, experience, and knowledge. To uncover the world that’s casting the shadows we perceive.
To return to my opening question on what it means to “understand”: the future is not just us using models to build things we personally don’t understand. It’s models doing things no human has ever understood.
That’s why the Anthropic report and Sequoia investing in Silver aren’t incompatible, they’re two chapters of the same story. One is about how far AI goes with the map we’ve already drawn. The other is about what happens when it starts exploring the territory.
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]]>The post Why Application Companies Still Win When Code Is Free appeared first on Frontline.
]]>I’m seeing a narrative building in the post OpenClaw / Claude Co-Work world: the future of software is just massive databases (Snowflake, Databricks and friends) with swarms of agents running on top, spinning up custom workflows and UIs on demand.
Why build application companies when you can just conjure bespoke solutions for any workflow and any user?
I don’t buy it. I’ve found it helpful to think about this from the perspective of the buyer (CT/IO of an Enterprise).
The 8 Constants of Enterprise Products
When companies buy application software, they’re not just buying lines of code. They’re buying the below “8 Constants of enterprise products”:
- Reliability from battle-testing
- Repeatability across teams and use cases
- Security that’s been audited
- Brand and someone to blame when things go wrong (“you don’t get fired for buying IBM”)
- Service & maintenance to fix issues as they arise
- Business case that delivers economic value at a cost-profile that makes sense and can be budgeted
- Product roadmap i.e. the product keeping pace with the rate of innovation in the market
- Opinions on how a workflow should actually run, distilled from hundreds of customer deployments
I refer to them as “Constants” because, like physical constants, I don’t believe they change regardless of what the underlying technology does. The last one matters more than people think. It’s my view that while many today are experimenting and creating their own workflow automations with agents, the long tail of enterprise employees don’t want to figure out what they could automate, they’d rather buy something to do a job. What’s more, the way to solve a problem and pressure test the solution may not be obvious. That’s why we buy applications from those who have done the work.
Occam's razor: when code is free, simplicity wins
People assume that if AI drops the cost of code to zero, we’ll drown in it. Infinite code, everywhere, for everything.
But that’s a CTO’s nightmare. More code equals less reliability, harder debugging, systems humans didn’t write and barely understand. Occam’s razor suggests the opposite: the simplest solution wins. When code gets cheap, the power of our systems will increase (the shift to agentic systems of action etc.) but value over time then shifts to efficiency i.e. how much value you can deliver per unit of code.
Balancing inference & code
As a physics student I was taught to think in local or global maxima and minima. Right now we’re at a local maximum, with relatively stable code per unit of value. AI will initially spike us up as every company experimenting with agents and custom workflows generates code like crazy. People who have never written code before are getting in on the action with Claude Code.
That initial spike will be an unstable maximum. Over time, patterns will emerge and opportunities for repeatability will crystallise. The chaos of a thousand custom agents will collapse into refined, simple applications and agentic systems that do the same job, much better, in a cost efficient manner…over and over again.
The nuance for CTOs and CIOs will be striking the balance between code (compute at build time) and inference (compute at runtime). Pure inference (agents generating everything on the fly) is powerful but expensive and unpredictable. Pure code (writing everything upfront) is inflexible but cheap and reliable. The equilibrium lives between them.
For example, there are some processes like Payroll, where you don’t want an AI to “infer” the way to do it each time. There is a “right” way to do it, and you don’t want it to be unpredictable, or at a random cost-basis. Therefore, it’s better to codify.
That’s not to say all parts of the process should be deterministic. Inference may thrive in certain parts of the workflow.
Where does value accrue in enterprise solutions?
Enterprise systems like ERPs, CRMs have historically been usability-poor and people only use them via an interface if they really have to. Yet, they command multi-million dollar contracts. The value ascribed to these contracts is not really about usage, or the code itself, therefore. It’s much more about the way they deliver on the 8 Constants above (reliability, repeatability, service … etc). Again, a software business is much more than just code.
What’s exciting is that AI can let you win on both sides at once. Where legacy systems were reliable but painful to use, AI-native applications can be powerful and actually enjoyable.
We’re already seeing companies deliver on this, like Frontline portfolio company Donna, which has built a multi-modal AI assistant for field sales teams. The chart below shows how organisations running Donna alongside SAP CRM are seeing significant usage increases. Deterministic code gives them the reliability they need; AI inference gives the user experience they actually want.
Will there be more code?
Yes. Software will keep eating the world and AI will accelerate this. The TAM of software keeps expanding. But code efficiency will improve dramatically too. The complexity isn’t more lines of software, it’s deciding where to use code versus where to use inference.
To continue the physics analogy: at the limit, systems settle into the lowest energy configuration that still delivers full value, known as their “ground state”. In other words, future systems will optimise for maximum capability, but minimal waste. The application layer will find that ground state: powerful enough to handle complex workflows agentically, simple enough to be stable and cost efficient.
The chart above demonstrates this transition: an initial spike due to experimentation, followed by a stabilisation as repeatable patterns are distilled and optimised, before settling in a ground state.
Should Enterprises build or buy?
This clicked for me last week talking to the CEO of a non-tech enterprise who said to me: “I’m paying too much for my systems of record and nobody likes using them. I’d love to replace them. Should I build my own or buy? What would you advise?”
In that moment the answer became clearer to me: Buy. 100%.
Sure, he could spin up a team to replicate a CRM’s code in a couple days with Claude et al. But then you have to maintain it, debug it when things go wrong. And more importantly, you can’t keep pace with a vendor whose entire business is that product and to innovate with that product.
If it’s not your main focus you’ll fall behind fast if you try to do this in house today, and will be stuck maintaining your out of date stack instead of growing your core business. I don’t doubt that some tech-forward companies will experiment with DIY systems. But for most enterprises, particularly in non-tech sectors, it won’t be the optimal approach.
So does the future of software look like the past?
Not quite. Actually, not at all. To get the most value of the transition from deterministic applications and systems of records to agentic systems of action, AI workers, and all the future capabilities of AI-native systems, I do think the enterprise tech stack will need to evolve dramatically.
It’s unclear what that looks like exactly, but it feels like some kind of unified data plus business logic layer (to maintain integrity of agentic apps) will be the foundation. On top there will be an application layer, some of which will be custom, sure. But in my view, third-party apps aren’t going anywhere. CTOs/CIOs will want to purchase from AI vendors whose core business is to solve the job they are hiring for in the first place. This will be the solution that solves for cost-adjusted-value (outsource to someone who knows what they’re doing), ensures reliability and accountability (someone to blame), and provides the optionality to swap vendors in and out if needed.
The application layer strengthens, not weakens
In a world where everyone can generate code, the winners will be companies selling “we figured this out already, and our solution is more powerful, but cost optimised and reliable than what you could build yourself”.
For founders this means the opportunity is in finding those natural, energy-efficient, repeatable states. Workflows that collapse into elegant simplicity. Patterns every company needs but shouldn’t rebuild. Application companies that nail these equilibrium points where code and inference balance perfectly, and can wrap it in the “8 Constants” that Enterprises actually buy, will capture outsized value.
Of course, there are no right answers here, so I’m always happy to debate this topic with others via Linkedin or email.
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]]>The post Voice AI and Financial Services: Introducing Avallon appeared first on Frontline.
]]>Voice is the most natural human interface. It removes friction, builds trust, and enables real-time, nuanced interaction, which is critical in complex and highly regulated industries. Here at Frontline, we believe voice AI marks a paradigm shift in how customers will interact with businesses. We’ve already backed Telnyx, Regal, and Tucuvi in this space, and are now excited to welcome Avallon to the Frontline portfolio, too.
The Evolution of Voice
For most of the 2010s, “voice” in finance meant rigid IVR trees, high transfer rates, and siloed call-center tooling. Word error rates were too high for complex, regulated conversations, assistants were FAQ-bound, and anything beyond simple queries fell back to humans. Voice was a cost center — useful for scale, limited for intelligence.
Over the last decade, that has fundamentally changed. Continuous advances in automatic speech recognition (ASR) and natural language understanding (NLU) reduced word-error rates from double digits to near human parity (~5% in benchmark tasks). Today’s real-time LLM-powered systems go much further than transcribing: they infer intent, detect sentiment, and respond with contextual nuance within milliseconds. Voice interactions have evolved from “press 1 for balance” to natural, dynamic conversations that can execute secure, end-to-end tasks.
Wave 1: Reliable self-service: Improved ASR/NLU moved voice from basic triage to reliable service and enabled banks to deploy assistants that handled complete customer requests (PIN resets, card replacement, statements, etc.), cutting handle times and improving first-call resolution. Voice became the front door for routine service.
Wave 2: Voice as workflow: Modern voice agents capture structured data, validate policy details, trigger back-end workflows, and automatically write accurate CRM or Third-Party Administrator (TPA) notes. It’s not just the conversation, but the entire workflow that’s supported.
The convergence of accuracy, latency, and reasoning marks a new era in voice capabilities, where financial institutions like banks and insurers can move beyond call centers to embed voice across the entire stack, from automating servicing, claims intake, compliance review, and relationship management:
- Claims and servicing: Voice AI can collect structured data from spoken narratives, auto-fill claim forms, and trigger triage steps in minutes instead of hours, cutting “First Notification of Loss” (FNOL) times by up to 60%.
- Wealth management: Advisors use voice assistants to summarize client meetings, extract action items, and automatically update CRMs.
- Banking and credit: Banks like NatWest and Bank of America are using voice-enabled virtual assistants for personalized product discovery, not just support.
- Compliance and audit: Voice systems can flag disclosure gaps or risk phrases during customer conversations, turning a traditionally manual QA process into an automated one.
Beyond improving efficiency for the company, voice AI is also more accessible for customers, who may be visually or physically impaired, have low literacy levels, or speak a different language from the vendor.
In an industry where trust and complexity matter most, we’re seeing financial institutions reimagine how they speak with their customers, literally. Early adopters are already reporting 20-40% reductions in average handle time and 25-30% improvements in first-call resolution. That impact is translating into spend too, with the market for conversational AI projected to reach ~$41B by 2030 at ~24% CAGR. The data is clear: customers prefer to talk, and the institutions that choose to listen intelligently will win.
As generative AI moves into regulated environments, winners will combine conversational design with governance, explainability, and domain expertise. The right platform architecture includes:
- Role‑based permissions for actions that change money, entitlements, or customer data
- 100% interaction scoring for accuracy, completeness, and tone, with automated QA and audit trails
- Locally trained, domain‑specific speech models that handle accents, jargon, and policy terms
- Redaction, retention, and supervision policies consistent with regulator expectations
- Grounded reasoning on current policy and product states to avoid drift
It’s this next wave of opportunity in voice that we are most excited about at Frontline. Specifically, we’ve been deep-diving on the insurance industry, which is now primed for Voice AI because of three converging trends:
- Model maturity: Speech-to-text, intent recognition, and LLM reasoning have crossed critical accuracy thresholds (financial services-grade assistants handling billions of interactions with >98% answer success) making automation viable even in high-stakes domains.
- Regulatory openness: Supervisors are increasingly comfortable with explainable AI systems that enhance transparency and auditability.
- Customer expectation: Consumers now expect instant, human-like support, without the frustration of rigid IVR trees (reflected in rising digital engagement and assistant usage across banks).
As we’ve heard repeatedly in diligence calls, real-world adoption depends not on flashy demos, but on error rates and explainability. For example, a single wrong digit in a car insurance claim or an incorrect name in a KYC process can derail entire workflows.
That’s why domain-specific, locally trained speech models, especially in highly complex financial processes such as claims processing, are likely to outcompete generalist systems over the next few years.
We expect the global speech AI market to bifurcate:
- Horizontal infrastructure providers powering global developer ecosystems, and
- Vertical voice intelligence platforms built for regulated domains such as finance, healthcare, and government.
Introducing Avallon
Founded by Cornelius Schramm and Bryan Guin , Avallon is a company that is well-positioned to build a generational company within the Voice AI market for financial services — starting with insurance and claims operations, where friction and manual processing are still the norm. Today, we’re delighted to share that we’ve led the team’s $4.6m Seed round.
Avallon’s platform offers a more scalable and efficient claims handling process for insurance carriers and TPAs, most of which depend on legacy IVR systems and call centers, without sacrificing human warmth or regulatory rigor. Its AI voice agents address operational inefficiencies and automate operational tasks across the claims lifecycle from intake to resolution, including:
- managing intake, status and billing questions by phone, email or file upload
- tracking case status, contacting employers, providers, repair shops and injured workers
- summarizing and extracting information from critical insurance documents such as PDFs, invoices and medical reports
- analyzing policy terms and flagging exposures
In early deployments, Avallon has already shown measurable improvements in:
- Customer satisfaction through shorter wait times and higher first-call resolution
- Agent productivity via automated documentation and post-call summaries
- Operational efficiency by turning unstructured voice data into actionable insights
As one of their early customers put it: “It’s the first time we’ve seen an AI that actually understands what our customers are saying, not just what they’re typing.”
Avallon’s product strategy leverages Voice AI to gain a deeper understanding of claim-specific context, positioning the company as a contender to become the future system of record. We believe this is a rare and disruptive opportunity: by owning both the interface and the data model over time, they have the potential to reshape how critical insurance interactions are captured, processed, and acted upon, creating significant long-term strategic value.
Philipp and I first met with founders Cornelius and Bryan in their hacker house in San Francisco, and were quickly impressed by their rare mix of regulated-industry experience and deep knowledge on speech technology.
The pair met while studying computer science and machine learning at Cornell University. Since then, Cornelius scaled the US expansion efforts for FINN as an automation engineer, where he built the fleet operations platform and experienced the manual, paperwork-driven pain points of the insurance industry firsthand. Bryan, meanwhile, worked as software engineer at Agentive where he built AI systems for auditors and led product-consulting teams at EY advising Fortune 500 clients on AI.
They’re joined by Moritz (ex-Taktile) and Leander (ex-FINN) to build Avallon out of the heart of financial services – New York City. Avallon is hiring software and systems engineers. Learn more about open positions here.
For us, Avallon represents a bet on the next operating layer for financial services, and we’re proud to partner with them on their mission to make financial institutions speak and listen more intelligently.
If you’re building a voice AI product with global ambitions, or know a team that is, please get in touch.
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]]>The post Congratulations Navan: Frontline’s First IPO appeared first on Frontline.
]]>Today is a big day for Ariel Cohen and Ilan Twig, who have taken Navan — the travel and expense platform — public on the Nasdaq. It’s also a big day for Frontline: Navan was the first investment from our US Growth fund and it becomes the first IPO in Frontline’s wider portfolio.
I met Ariel in early 2018 when the company, then known as TripActions, had revenues in the single digit millions. Ariel wanted to take on the enormous, outdated world of corporate travel and was raising a Series B to expand internationally. I got an immediate taste of his all-action style when he rerouted a European trip to meet me in Dublin. We spent hours at the whiteboard in the Frontline office, talking about his business and sketching out its European future.
When I look back at my notes from that meeting, I’m struck by how accurately Ariel’s vision has played out:
Feb 2018. Ariel wants to create a beautiful end-to-end experience of corporate travel for the traveler, not only the finance team…this will require deep travel infrastructure, not just a pretty app, and he wants to use modern technologies such as machine learning…He wants to provide rewards for the traveler to get incentives right…and maybe one day offer an expense solution…He wants to support US customers globally and sell to enterprise customers anywhere…global expansion is a top priority.
How Navan Shaped Frontline Growth
Ariel became our design partner as we shaped the nascent Frontline Growth model. To select the best city as European HQ, the two of us hopped on a plane and toured Amsterdam, London, and Dublin. To hire the right senior leader, I built a list of candidates and wrote the company’s first EMEA GM job spec. When Ariel wanted to meet European customers and partners, that became part of our playbook too.
He had recently hired a promising exec from Uber, Nina Herold, as VP International and asked for my help with her onboarding. I met Nina regularly and acted as a sounding board for a time. She didn’t need me for long (a quick study!) and went on to great things as CPO and COO of the company.
Flight Status: Indefinitely Delayed
By late 2019, Navan’s international go-to-market engine was firing and revenue was approaching $100m. But one thing Ariel couldn’t plan for was the first global pandemic in a century. As the world locked down, Navan’s revenue went to zero.
Amid the chaos, Ariel and Ilan never lost faith that people would travel again. They stabilized the company, doubled down on product development, and kept selling. In an internal note from the darkest days of 2020, I wrote to my colleagues:
Ariel and the TripActions team are so impressive — ambitious and relentless.
It took nearly three years for global travel to normalize. Under weaker leadership, Navan would not have survived. Ariel and Ilan’s resilience during that time was extraordinary.
Destination Everywhere, via Navan
Since 2020, Navan has expanded far beyond travel, now offering corporate cards, expense management, and AI-powered spend insights. It has become an end-to-end platform for managing business travel and spend.
In 2022, having outgrown TripActions, the company rebranded and became Navan. The palindrome has a silky mouthfeel that evokes global navigation and adventure. But as an Irishman, it would be remiss not to mention that Navan is also a small town near Dublin, a gateway to nowhere in particular, where cows outnumber tourists. The name change caused much merriment in Frontline’s Dublin office and gave us yet another reason to root for the company.
Ariel’s early vision has now become reality: LTM revenue of $612M (up 32%), with 41% coming from outside the US. Customers love Navan because it’s a great product. Frontline loves Navan because it helped us define our product as a venture capital firm.
Onwards
Thank you, Ariel and Ilan, for letting us be part of the journey. Congratulations on today’s milestone and good luck on the next leg.
– Stephen and the Frontline team
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]]>The post Brightflag Announces €425m Acquisition appeared first on Frontline.
]]>Today marks a huge milestone for the team at Brightflag, the AI-powered legal operations platform, which has been acquired by Wolters Kluwer for €425m.
Congratulations to the entire team at Brightflag, and especially to founders Ian Nolan and Alex Kelly, who have shown exceptional focus and drive throughout this company-building journey.
We first met Ian and Alex in 2016. They both came from legaltech backgrounds and so intimately understood their customer needs. Alongside the strong founder-market-fit, we were incredibly excited by their vision for the product. Building a proprietary set of training data for an AI model is par for the course in 2025, but Brightflag had the foresight to do so back in 2016.
But what really attracted us to this team was their global ambition. Whilst the average time for software companies to expand from Europe to the US is five years, Brightflag established a presence in New York within the first year of operations—a strategic choice that was bold and highly unusual ten years ago. This early expansion meant they were building a product fit for both US and European customers from the very outset, and enabled them to bring on Kevin Cohn as CRO, and Michael Scarpelli as CFO, who greatly strengthened their US operations.
We made our first investment in Brightflag at Seed stage in 2017, alongside Tribal VC. We worked closely with the team in the years that followed—through their Series A (led by Sands Capital) and Series B (led by OnePeak) rounds—with increasing conviction that they were building something great.
Today, Brightflag has established itself as a true category-defining company. As of April 2025, the company is generating €27 million of ARR. Its revenues are approximately 95% recurring in nature and approximately 60% from US customers.
On behalf of Frontline, a huge thank you to all 155 team members at Brightflag and all who have contributed to the success of the company over the past decade. We’re excited to see the product in the hands of many more people in the future — go Team Brightflag!
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]]>The post 5 Essential Questions for Application-Layer Investors appeared first on Frontline.
]]>As AI startups gain traction at an unprecedented pace, investors face a new set of challenges in distinguishing what characteristics are indicative of future category leaders, versus mere hype.
Unlike traditional SaaS, we expect AI-first applications to thrive with different business models, growth levers, and defensibility factors.
In this second part of our series on application-layer AI, I lay out five essential questions that investors must ask to successfully navigate this evolving landscape. See part one for our five essential questions for application layer founders.
1. Is traction becoming a distraction?
AI companies can generate revenue much quicker than traditional software companies. They can essentially bypass the product-market-fit stage (PMF) stage of building a company if they are simply automating existing workflows or services work with immediate value-add. Additionally, as many enterprises are keen to explore the potential of AI, they have dedicated experimental and innovation budgets to try out these solutions. Getting €50k ACV from these customers for experimentation is very different from an at-scale, recurring contract with the product embedded in core operating work-flows. As a result, AI startups are gaining traction faster than ever. For example, Cursor went from $1M ARR in 2023 to $65M ARR by Nov 2024.
Many of these AI companies operate like mini “agencies” rather than software companies. We could end up with hundreds of these agencies at €1-10m ARR, as they each have an ability to win work quickly in local markets and for specific use cases (more on that later). As a result, getting to €500k-1m in ARR at seed stage doesn’t really tell you much as an investor. It provides some early validation of ability to sell, but in the journey of becoming a €1B+ valuation company it is a rounding error. For these “agency”-like agentic companies, the revenue is very undifferentiated and un-sticky, making it quick to acquire and quick to lose.
Implications for Investors:
Investors must see through revenue metrics and assess the business on the other facets discussed in part one e.g. how opinionated is the product, access to differentiated data, re-imagining existing workflows etc. These facets are going to define who becomes the category winner more than early revenue, particularly if that revenue is experimental or proof-of-concept based.
2. What type of AI and what level of automation does the product unlock?
Not all AI is created equal, and it’s important to distinguish the category of AI being developed, as well as the depth of innovation. Broadly, there are three types of AI product:
- Co-pilots
- Agents
- AI embedded in services
However, it’s important to go one step deeper. Even for “agentic” solutions that are autonomously executing on tasks, there are still nuances in the level of automation to which they operate. Langchain provides a useful five level framework for agentic automation, distinguishing between whether code (written by a human) or an LLM are executing on identifying the steps to take, choosing which step to take, and generating the output. Different levels of automation may make sense for different product and differentiation strategies.
For example, Github copilot is trying to own level 2 automation by suggesting code but giving developers power to make final decisions; UIPath thrives at level 4, with agents handling repetitive tasks but escalating to human in the loop for any issues; Wayve technologies is pushing for full level 6 autonomy through its end-to-end deep learning for AVs.
Implications for Investors:
Take the time to truly understand the type of AI at work, and the level of automation at play for every company assessed. On the surface level they may look the same, but each step towards level 5 automation is a step-change in technical sophistication and product positioning.
3. What role do professional services firms have to play?
As large enterprises work out how to extract value from AI and deploy it in a responsible and secure way, they are increasingly turning to consultants for guidance and implementation support. Professional services firms will therefore play a greater role earlier on in an AI startup’s lifecycle than in a traditional SaaS lifecycle, supporting with custom AI integration, build tailored use cases, and at-scale deployments. Accenture, for example, secured $3B in generative AI bookings in 2024, illustrating the immediate revenue opportunity for consultants.
This may seem counterintuitive as AI has the potential to erode the long-term revenue base of professional services firms. We expect to see a short term boost to services revenue, but over time we expect there to be two types of category winner emerge:
- Tech-enabled services firms, with business models optimized for AI that will disrupt service incumbents through offering outcomes that are better, faster, and cheaper.
- AI software platform companies that fully productise the repeatable use cases that are emerging from bespoke services projects – just as ERPs, CRMs, and HRIMs emerged in the past
Over time, enterprises will shift from bespoke AI solutions and instead outsource to either software platforms or tech-enabled services, as a way to reduce operating complexity and drive competitive advantage.
Implications for Investors:
We expect to see professional services play a large and early role as channel and integration partners for AI startups. Investors need to distinguish between those that may remain as tech-enabled managed service providers, and those that can evolve into standalone platforms that disrupt the services industry.
4. Is this company embracing new business models?
New platform shifts lead to a refresh of optimal business models. For example, legacy on-premise software lent itself to perpetual licences, while the shift to cloud computing drove the adoption of per-seat pricing. Established companies often struggle to adapt their business models to new paradigms, as doing so can erode their profitability structure. This offers new entrants, optimised for new business models, an advantage over incumbents.
For SaaS products, per-seat pricing makes sense because users interact directly with the software. In this model, revenue scales with the number of users—an early form of usage-based pricing. AI-driven applications, however, can deliver value that is completely decoupled from the number of seats (people) interacting with it. Agentic AI systems perform tasks autonomously, often without a human in the loop, and their costs scale with usage—every time they need to ping an LLM API for the customer request. If a company continues charging per seat while its costs are driven by usage, it risks a margin squeeze.
At the same time, customers increasingly expect to pay based on actual value received rather than flat fees. Together, these trends point to a shift towards outcome- or value-based pricing models. We also anticipate a growing ecosystem of tech companies that enable this shift by powering dynamic, usage-based pricing (e.g. Metronome). Here is a list of some of these business models we are seeing in market:
Implications for Investors:
Founders must be deliberate in designing business models that align with AI-driven cost structures and evolving customer expectations. Investors should look for early signals of pricing validation, such as evidence that customers are willing to adopt a usage- or value-based model at scale.
5. Does this company look like a VC or PE opportunity?
Because many vertical AI companies will look more like mini-agencies than traditional software companies (see point 1), we can expect some of them to behave more like PE—rather than VC—opportunities.
Given the level of consultative support required, and the limited differentiation across these AI solutions, many companies may struggle to scale beyond the 5-10m ARR mark. So while they may generate revenue sooner, they are unlikely to achieve the J-curve growth trajectory of traditional venture-backed software businesses. We may see many lookalike vertical AI applications spread across different geographies.
As a result, exits could happen sooner than in typical venture-backed scenarios, with PE-style roll-ups emerging to drive synergies across similar AI companies. This could resemble the consolidation of creative agencies under firms like WPP or Publicis Group.
However, some companies will break free from this low-differentiation agency model. The winners will be those that execute rapidly and expand their product vision with common horizontal use cases across multiple vertical applications.
A good example of this is Rippling, which identified that HR is built on top of many vertical applications, with duplication of functionality as each has their own security, permissions, integrations etc. Rippling unified these by using employee data as a central layer, and allowing vertical apps to plug into it. We expect similar winners to emerge in AI applications by “platformising” common operational use cases across the enterprise, unlocking network effects or proprietary data advantages. These businesses will look a lot more like venture outcomes.
Implications for Investors:
Early consolidation and in-organic growth levers are likely to have a bigger role to play for AI start-ups. Investors must have a high bar for product differentiation when backing category winners, or be comfortable with a potentially more capital intensive roll-up play for vertical specific AI applications.
While the early traction of AI companies is enticing, investors must be aware of the nuanced characteristics of these companies versus their SaaS equivalents. Understanding the technical depth of products as well as the appropriate business model, the role of services firms, and the appropriate growth strategies will be critical for AI investors.
At Frontline, we’re actively seeking visionary founders building enduring AI-first companies. If you’re tackling these challenges head-on, we’d love to hear from you. Reach out at [email protected].
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]]>The post From Seed to Series B: Cloudsmith’s $23M Milestone appeared first on Frontline.
]]>When Steve Collins, former Frontline Partner and current CTO of Unity, gets excited about an infrastructure technology company, you know to pay attention—as a three times CTO he has first-hand experience of the issues that technology teams have deploying code securely.
Now a Venture Partner at Frontline, Steve first met Cloudsmith co-founders, Alan Carson and Lee Skillen, in 2019 and was immediately impressed with their technical vision for a cloud-native artifact management platform. Due diligence confirmed that there was a real market need for the product they were building, and with strong early traction, Frontline led Cloudsmith’s Seed round.
Today, as the company announces its $23M Series B investment, it’s clear that Cloudsmith is poised to become a major player in the software supply chain.
Over the past six years, Cloudsmith’s product vision has continued to expand as the company deals with larger and more complicated use cases. Engineers at customers like PagerDuty, Shopify, Kandji, Ford and American Airlines now rely on Cloudsmith for their artifact management.
In 2023 Cloudsmith attracted the attention of Glenn Weinstein, then the Chief Customer Officer at Twilio, who joined as Cloudsmith’s CEO later that year. Glenn has led the company as it expands its commercial presence in the US (where he is based) whilst maintaining the heart of the company in Belfast, where they are one of the fastest-growing software companies.
Fast growing companies in big markets attract a lot of investor attention and so Cloudsmith was able to choose which investors to work with, and chose wisely by picking TCV as lead investor, with participation from Insight Partners, for its Series B. TCV has an incredible track record in infrastructure software, with early investments in Gitlab, Rapid7 and Twilio. Cloudsmith is lucky to have Morgan Gerlak from TCV join their board.
I have no doubt that in a few years much of the world’s code will pass through Cloudsmith’s infrastructure. Which has me left wondering, as NASDAQ tickers CLOU and SMTH are already taken, what will Cloudsmith’s ticker be?
If you are an engineer wondering what the future of software supply chain looks like, go check out cloudsmith.com
Go Team Cloudsmith!
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]]>The post 5 Essential Questions for Application-Layer Startup Founders appeared first on Frontline.
]]>One of the areas of innovation we are most excited about at Frontline is the rise of application layer AI, and particularly those companies that intersect with the services industry, with categories of spend that were previously inaccessible by technology now open to disruption. The opportunity for value creation here is huge.
To put it into perspective, global GDP is currently about $115 trillion. Over 60% of this is generated by services, with technology contributing 5-15% of GDP across sectors. If, say, just 5-10% of services work shifts to addressable tech spend over the next decade, the total addressable market (TAM) for tech will expand dramatically.
We expect the new combined services-plus-software market to be far greater than the sum of its old parts, representing one of the next great market opportunities. We’ve seen this before, with the global switch to cloud computing from on-prem facilities expanding the total software market from $350B to $650B.
Whilst there’s broad agreement that application layer AI will redefine the tech market, the only consensus on what successful AI-first companies will look like is that they’ll be fundamentally different from the previous generation of SaaS companies.
This article—the first in a two part series—explores the five critical questions that founders must ask themselves when building application layer AI companies, in order to become global category leaders.
1. How are you handling non-determinism?
One of the defining factors of traditional software is its determinism i.e. it does exactly what you tell it to do, and it retains a traceable and auditable record of activity. AI, by contrast, is by nature non-deterministic and probabilistic, which means buyers are cautious and skeptical about adopting AI/LLM-first solutions into critical and production processes.
It will be the companies that can verifiably stand over their results with appropriate benchmarking, validation, and mitigation of bias, all in a secure way, that will be the ones to successfully navigate from experimental to production budgets.
For example, Writer—which helps businesses deploy generative AI into their processes—trains its own models, so it can disclose the training dataset and assure customers that their data won’t be used for training.
Implications for Founders:
- Demonstrate accuracy, reliability, and lack of bias—this is table-stakes, not a nice-to-have. Explainability will be particularly important in regulated industries like healthcare and finance.
- Consider approaches like confidence levels, easy-to-access citations (as seen with Perplexity), or incorporating a human-in-the-loop.
- Invest in the latest infrastructure, LLMOps, and observability tooling (e.g. Braintrust.dev, Langfuse, or others for tracing, monitoring logs, managing prompts and evaluations etc.).
2. Are you looking beyond just automation of existing workflows?
As we adopt new technology, the tendency is to take what happened in the old world and adapt it for the new platform. This is comparable to taking the on prem solution, and porting it to cloud. However, we believe the big winners won’t just optimise and automate current workflows, but will re-define how companies can achieve their objectives with new technology.
For example, rather than automating existing procurement workflows like contract generation, review, and payment, perhaps AI applications could parallelize conversations with dozens of vendors, automatically assess them, provide recommendations of the best fit vendor, before agentically completing the procurement process?
As AI or agentic products replace manual tasks, founders will have an opportunity to unbundle existing workflows and re-imagine how organisations achieve their objectives, by focussing on the customer “jobs-to-be-done”.
Take Frontline portfolio company Vanta as an example. Vanta solves the job-to-be-done of achieving and maintaining security and regulatory compliance. Rather than creating an “AI employee for compliance”, Vanta re-imagined traditional manual compliance workflows as an automated software-driven process, allowing customers to simply buy “being compliant” as an outcome.
As companies adopt agents and adapt existing workflows, new systems of records (SORs) will be needed to maintain business logic and data integrity. There is no reason to believe that the current breed of cloud-first SORs (e.g. CRMs, ERPs etc.) will survive this transition and this is where opportunity for the next generational companies lies.
Implications for Founders:
- Define the specific “jobs-to-be-done” your product solves, and focus on the “outcome” the customer can achieve rather than being tech, or workflow led.
- Set a high bar for product ambition and sophistication beyond just automation and optimisation, as category leaders are likely to become the new SORs by expanding in product surface area over time.
- Ask yourself: What can your product enable that was previously impossible? What business outcome does the customer want to buy?
3. How are you creating value above and beyond the underlying foundation models?
At the beginning of any platform shift, value is often derived from showcasing the power of the underlying technology, with the application layer acting as a light “wrapper”. Think back to when the first iPhone was released—many of the first popular apps were very light digitisations that showed off innovative features, like the one that made it look like you were drinking beer when you tilted the screen.
Over time, people figured out how to build truly valuable apps leveraging the power of both the underlying tech and the implications of its mass adoption. Uber would not have been possible unless you assume everyone has a device in their pocket with location tracking, payment integrations, portable interface etc. And the value that Uber creates lies in its extensive network of drivers, consumers and supporting distribution and business operations, which was bolstered by the increasing adoption and functionality of smartphones.
Similarly, the big winners in AI will be accelerated by advances in the underlying models, while having a robust and defensible value-creating layer on top. Take Cursor, the AI code editor, for example. It has a feedback loop (i.e. the code works, or doesn’t) that improves its models with usage, and the better it gets, the more developers share it with their friends to use, creating a reinforcing flywheel of adoption and quality.
Implications for Founders:
- Build applications that improve with each foundation model release and create defensible value through distribution, data, brand, and network effects. Light wrappers built on top of foundation models will not survive over time.
- Ask yourself: Does the next version of Claude or ChatGPT kill or accelerate your product?
4. Are you leveraging data, complex integrations, or regulation to create unfair advantages?
One of the obvious differentiating factors between AI-first companies will be data, particularly in regulated industries. Winning companies will likely have access to proprietary datasets, or will be combining known data sets in novel ways, or adding value in other ways on top (e.g. labelling). They will also have ways to continuously access this data either through robust integrations into Systems of Record like ERPs or CRMs, or having an “unfair advantage” by developing a clever mechanism to capture data at the source. Even better if the processing of proprietary data results in outputs in regulated spaces.
Frontline portfolio company Tucuvi, which offers conversational voice AI for healthcare organisations, is a great example of this. Its models, trained on millions of hours of clinical conversations, are both proprietary and clinically validated, giving it a significant competitive edge.
Implications for Founders:
- Push for access to proprietary, hard-to-replicate, or unusually large training sets.
- Explore integrations and mechanisms to collect and surface data in useful formats (LLM readable), or with genuine IP in your data model.
- Leverage regulation to your advantage if possible as a source of moat.
5. Are you considering the appropriate interface and modality?
As technology evolves through incremental platform shifts, so do the ways we interact with it. This is not a new story. The graphical user interface (GUI) was a step change in interactivity with technology, democratising access for people to control applications via simple points, clicks, drags and drops rather than requiring knowledge of command lines. The pace of change of natural language understanding and response in AI will similarly alter how, where and when we interact with software. The use of voice, and natural language prompts are likely to play an increasingly important role.
However, founders also have a new dimension to consider for their products which is how much they want to push to “agents” vs for what they will keep human interaction in the loop. This is a new vector of competitive differentiation. For example, frameworks like Llamaindex or Langchain approach is to give developers full control via code in the building of AI apps and agents. Whereas other companies are opting for no-code or low-code interfaces for building agents (e.g. n8n, or Langflow). Great founders will meet their ideal customer profile (ICP) where they are, and effectively distinguish which tasks make sense to run in the background agentically, and which require human interface and control.
Implications for Founders:
- Offer optionality of interface or have a strong rationale for focusing on a specific modality.
- Old style GUIs won’t be dead, but you should stay aware of how shifting market dynamics influence user expectations, and be intentional about your choice of interface and modality.
- Be opinionated on which tasks require human interaction points, vs which make sense to run agentically with less human control.
So, are you a killer team creating an application-layer product with solid answers to each of these questions? At Frontline, we are always looking to partner with the most ambitious founders as early as possible.
Feel free to get in touch at [email protected], and check out the next article in this series, here.
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]]>The post 4 lessons from working in VC I wish I’d known as a founder appeared first on Frontline.
]]>Hello there 
I’m Dylan, part of the Seed investment team at Frontline Ventures.
I’ve spent my career in and around innovation, from founding my own sports tech startup (MatchDay) and academic journal (Trinity Student Scientific Review) to directing global innovation budgets for a large multinational (Accenture).
I bear the scars of failure from many of these endeavours, and know first-hand the difficulty in setting up a business.
As such, one of my goals is to help demystify venture capital and provide transparency and support for budding fundraisers to give them a better chance of success. This piece is my first attempt at that.
Here are four lessons I have learned as early indicators of success in companies, as I have transitioned from startups to venture capital.
Lesson 1: Team Trumps All
Investing at Seed stage is exciting because it is the perfect mix of science and art. Science in the sense that you can write on a piece of paper the things we look for in a business (and I have written some of those things below in lessons 2-4); and art in the sense that, at early stages, you don’t really have any data points to assess that reliably, and the decision almost entirely comes back to an assessment of the founder.
From Frontline’s experience, an exceptional founding team is the absolute number one early signal of success. This is backed up by data we’ve tracked over time (See our analysis of past deal data to shorten VC learning cycles). Products change. Markets change. How founders like to operate typically doesn’t.
It isn’t possible for a business to have every answer and data point to give you confidence in an investment decision at Seed stage, but it is possible to have a founder with the competency, resilience, and ambition to figure it out along the way. With the right team, the rest often comes out in the wash.
Ultimately, if a startup ticks every other box but doesn’t have the right team, we won’t invest. If a startup ticks the team box but hasn’t quite figured out the rest, we may still invest, on the basis that good founders will figure it out.
The qualities this VC looks for in founders
If you are to take one thing from this piece about how Seed investors assess opportunities it is this point. And what are those qualities we look for in a founder? It’s a combination of competency and traits. If starting a technical company it is critical to have a competent team of engineering talent, for example.
And the traits we look for at Frontline are typically:
- High level of ambition and strong motivation for starting a company
- Clarity of vision — not just what you will do but what you won’t do
- Deep thought on answers i.e. you have novel insight and a high level of rigour when challenged
- Fast rate of learning — we refer to these founders as “learn-it-alls” in Frontline i.e. those that can demonstrate an ability to absorb learnings from customer interactions, and clearly describe them
- Ability to pivot and persevere i.e. while you have strong opinions, you hold them loosely and are willing to ride with the punches of the market to achieve product-market-fit
As you read through the next three lessons, read them through a “founder” lens. It’s not about the business having all of the below answers, but more so: Does the founder have the ambition and credibility to scale a business of size relevant to the fund? Can the Founder build a product that delivers step change value to a customer? Can the founder implement a business model that repeatedly wins customers in a differentiated way?
Lesson 2: Articulate a vision that is precise and big
After the team, this is the second most important thing we consider at Frontline. Total addressable market (TAM) defines how big an opportunity can be, and the team defines how big an opportunity will be. The more clarity with which you can demonstrate that the market you are competing for can sustain venture-relevant returns, the easier it is for us to say “yes”.
So “what are venture-relevant returns?”, you might ask. This is the formula for how VCs will assess whether your business can become big enough:
- Step 0: Check the investment thesis of the fund you are pitching. In Frontline Seed’s case its European B2B Software business with US ambitions. This is our first sniff test, and we fundamentally believe that to be a category leading software company, you need to be a great US software company.
- Step 1: What is the fund size? In our case we are currently investing out of a $100m seed fund in Europe. (Clarification: this doesn’t refer to a fund’s total assets under management (“AUM”), rather the size of the current fund they are investing out of.)
- Step 2: What is the VC fund underwriting to? Most VCs make an investment under the assumption that it has the potential to return the fund at least once (sometimes more).
- Step 3: What will the ownership percentage be at exit? Many early-stage VCs will aim to own 15-20% at Seed stage. After some standard dilution through Series B, that would imply about 10% ownership at exit.
- Step 4: What is the required exit size? Assuming the VC will own 10% at exit, and each individual investment needs to return the size of the fund, that implies the exit size needs to be at least $1B for a $100m fund.
- Step 5: To become a $1B business, what conditions do I need to meet? Again, this is sector dependent but let’s keep the maths simple: assume a 10X multiple on ARR for exit value i.e. I need to reach $100m in ARR.
Congratulations, you now have the answer of the exact number VCs are assessing your business against when reviewing your company. The challenge is describing the path to get there with clarity and credibility.
How to better articulate your vision
To help tell your story, consider this point: the fewer things a VC needs to believe are true to reach that point, the better. For example, if you have a multi-dependency business to reach that scale (i.e. crack this market, then build a whole new product to crack a different market) your chances have just diminished as the low probability of success of each new pivot compounds. It often helps to list these “things that need to be true” assumptions and figure out how to systematically de-risk and validate each one as part of your business plan.
E.g. To believe you can reach $100M ARR a VC needs to believe:
- You can build a product that delivers the value you say it will
- You can win clients at the “ACV”s (annual contract value) that you say in the business plan (lets say $500k per year per client);
- There are enough of those customers in the market to sustain a $100m ARR business (e.g. If ACV is $500K I need to believe you can win 200 customers, you probably need 5-10 times that amount in the pipeline).
The clarity and precision of this articulation of vision is often how we make our decision. The best founders tell a story of how they reach 100m ARR that is logical, and outlines both what they will do (and in what sequence), but also what they will never do.
Lesson 3: It’s not enough to be solving a customer problem
Most people can repeat the sage wisdom that starting a business should usually begin with identifying a customer pain point to solve. This is true, but I would add two addendums to that:
- It has to be a top priority pain point; and
- You have to be able to solve it in a way that is at least 10X+ better than the status quo.
Otherwise it’s too easy for customers to stay with the status quo or give an answer of “this is really interesting—come back to us in 6 months”. The customers probably aren’t lying—they likely do think it looks interesting. But if you have really hit product-market-fit, there should be such a pull from the market that they will be doing everything they can to sign up ASAP.
The first point was my number one learning from founding MatchDay (a fan engagement tech company). Towards the end of MatchDay we had pivoted the business several times, and ended up pitching Over-The-Top (OTT) sports broadcasters about ways to make their live streams more interactive, sticky, and drive revenue opportunities. We knew this was the way the industry was heading (look at the success of companies like DAZN since).
When we spoke to those customers they agreed that our proposition made sense. But it was only when we probed further and asked them to list their top three priority pain points they invariably said:
- Negotiate licensing agreements from rights-holders (a big challenge)
- Improve latency of streams to make the viewing experience palatable (highly complex)
- Acquire viewers (extremely difficult in a highly competitive space with fickle users who just want to see the game anywhere they can, ideally for free)
In summary, though we were solving a real problem, it wasn’t going to be a priority problem for 5-10 years. If we had focussed our customer discovery research on “just solving any problem” we would have missed this critical nuance.
Additionally, the reason something should be a step change improvement is to overcome the momentum of the status quo (using excel etc.) You may well be able to do something 10% better but as an old colleague of mine once said “I can get 10% improved performance by shouting louder”.
Every successful business has a unique view on the world
VCs like competitive markets. It mitigates some of what we like to call “market risk” if there is already a large cohort of your potential buyers spending money on alternative solutions. There is obviously a real problem to solve if customers spend money on it, and it’s often easier to fight for budgets that are already allocated to a solution.
This often leads to an area of questioning you’ve probably encountered if you’ve ever pitched to an investor: “what is your differentiation or moat?” i.e. how does your solution uniquely satisfy customer needs and why is that hard enough to do for you to maintain that advantage over time?
Founders (myself included) sometimes get bogged down on these points and think to have sufficient differentiation or defensibility they will need protection in the form of patents. However, Frontline’s experience is that patents typically aren’t necessary, or enough, to protect from alternatives (competitors will find a way to do what you are doing in a way sufficiently different that will avoid breaking the patent).
Instead, what we are looking for is a unique view on the world, that is both compelling and logical, and that will afford your company certain advantages in gaining market share. These novel approaches typically are across the dimensions of product, business model, or distribution. For example:
- Pointy (acquired by Google)—A Frontline portfolio company that was addressing a problem we had seen countless startups try to address (how to make local stock visible online for small retailers). The company’s unique product insight of a hardware device connected to a barcode scanner to get the data, and software to predict stock levels, allowed them to effectively collect the data and surface insights, which was the difference in them succeeding in this space.
- Klarna—The “buy now pay later” giant’s unique business model charges retailers fees and commission for the uptake in sales of goods bought through the service. This avoids consumers having to pay costly interest fees unlike credit cards, and supercharged their user adoption.
- Noloco—A no code app builder that has a unique distribution strategy through a prominent partner program for youtubers influential within developer communities. This incentivises viral growth of the product, as well as serving as effective customer success and education through third party product videos.
Large incumbents typically find it difficult and time consuming to pivot across these dimensions, so your unique insight really is a competitive weapon!
So, are you a killer team, solving a top priority pain point, in a unique way, with a well described path to your north star vision? At Frontline, we are always looking to partner with the most ambitious founders as early as possible.
Feel free to get in touch at [email protected] (just make sure to use the above as a cheat code to your story first).
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]]>The post Expanding the Team: Welcoming 3 New Faces to Frontline appeared first on Frontline.
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Allison Graf joins as our first Investor Relations Manager, helping to strengthen our relationships with our fund investors. Allison joined us in May and has been busy getting to know all of the investors in our funds across Europe, and building her network of brilliant IR professionals across VC and PE in London—reach out to Allison here if you’d like to swap notes.
Prior to joining Frontline, Allison spent seven years at Cornell Tech, Cornell University’s New York campus focused on educating the next generation of tech entrepreneurs. As a core member of the development team there, she raised funds to advance the strategic vision of the campus, and worked on projects to promote a more inclusive and ethical tech ecosystem.
Dylan Scully joins the Frontline Seed investment team. Based in our Dublin office, Dylan will have a particular focus on meeting the best and brightest founders that the Irish ecosystem has to offer.
He’s already hit the ground running, and is spending one day a week with the team at Dogpatch labs to mentor the next cohort of startups coming through.
The most recent of our new hires is Philipp Werner on the Seed investment team, based in our London office.
Philipp joins us from a Berlin-based VC, where he spent five years investing in B2B AI SaaS, specialising in fintech. He’ll remain an active participant in the DACH and CEE tech ecosystems whilst at Frontline.
Prior to his venture career, Philipp played a key role in building the commercial strategy for a B2B data analytics platform. He is an active member of Sigma Squared, a non-profit organisation supporting young founders, and regularly advises entrepreneurs across Europe on their go-to-market and fundraising strategies. He’s looking to meet “thoughtfully wild” founders developing innovative products in security, climate, fintech, or dev tools—you can reach out to Philipp here if that’s you.
We’ve loved welcoming Allison, Dylan and Philipp on board over the past few months, and can’t wait to see how they progress and grow with Frontline in the years to come.
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]]>The post Landing the Plane: 5 Tips for Founders Navigating M&A appeared first on Frontline.
]]>Here we are at the end of a decade of low interest rates, in a much tougher fundraising environment than the heady highs of 2020-21, and a disjointed and uncertain economy. VC-backed CEOs like you are now coming to an inevitable realisation: if you don’t hit revenue growth targets, you’re unlikely to attract new investors. To make matters worse, existing investors may decide to allocate their finite capital to a different company.
While some of you will reach profitability and have plenty of options of where to go next, others will need to face reality as early as possible and consider selling the business.
Much like the average bloke thinking they could land a plane in an emergency situation, many founders harbour the unrealistic expectation that they can ‘just sell’ their company if the original funding plan doesn’t work out. The reality is that getting acquired is really hard and really unlikely. The data tells us only a tiny fraction of companies actually get acquired.
And not to be completely doom and gloom, but even if you do get into a process with someone there is so much that can go wrong between signing the letter of intent (LOI) and closing the transaction.
Over the years, I’ve found myself acting as an independent counsellor to some incredible founders, and have now seen the M&A process play out a few times, noticing some common mistakes and frustrations at each stage of this process.
These mostly arise from a lack of communication between parties, and a failure to understand the different incentives at play. So I want to open source my learnings with the objective that more founders and senior leaders can be better prepared for this path, understand the behaviours of their stakeholders and hopefully maximise the outcome for everyone.
1) know which customers/partners are potential acquirers
No matter how well a business seems to be doing, it’s always best practice to know which of your customers or partners are potential acquirers. Then if the need arises, they are ‘warm leads’ and so much better than cold calling done by a corporate advisor.
For a customer, there are various qualifying criteria for when they might be an acquirer:
- Big spend and/or rapid growth indicating strategic importance
- Your product being something they have either tried and failed to build or talked about building
- You’ve already got relationships there with people who have revenue and growth responsibility etc.
There are corresponding ‘tells’ of partners to whom you may be strategically important e.g. you jointly win many/valuable deals together. If you never need to sell the business, then great, none of this ever arises—but it’s important to have in the back pocket. Another CEO may well end up saying to you ‘don’t sell this company without calling me first.’ Great result.
2) Find out early on if your investors are ‘buyers’ of your stock
It’s a very common tale; you’ve invested for growth—cash burn is high, runway is shrinking, and a few contracts are taking longer to close than you’d hoped. Do you know where you stand with your inside investors in a hypothetical future fundraise, or even in a bridge scenario? Have you openly talked about “what if all the great things we plan to happen, don’t”? If not, don’t assume you know the answer.
Once you’re operating at fewer than 18 months to cash out, you should address the following questions with your board/investor representatives. Realistically, at that point you are likely to get some vague responses but at 13 months runway, you really do have to seek out definitive answers:
- Do you think we can raise our next round as we stand today and on our current trajectory?
- Would you be supporting an additional investment into our business with your internal IC? (Are you a sell, buy or hold?) What are you ‘reporting back’ to the rest of your team when you talk about us?
- Do you have reserves set aside for us at this point?
- At what point would you expect us to be searching for an acquirer/engaging a banker?
The best outcome you can hope for in this scenario is alignment and clear communication (at a shareholder level) about the path forward for the company. So if the answers are fuzzy, push for clarity.
Being in limbo for any period of time is unpleasant, so even if the end result of this conversation is an agreement to try and sell the business, you can be comforted by having a singular goal to drive towards.
If investors are not planning to make funds available to you, you should understand that their incentive is to now facilitate a sale to — at the bare minimum — get back the money that they invested into your business.
3) You need a plan to get funded through the sales process itself
So, now you’re in sell mode. You must consider the question of whether you have enough cash to get to the end of a sales process.
Here are some things to bear in mind:
- 18 months runway is very useful, 12 months is doable—once you are inside 6 months, it’s almost impossible to get through a sales process. A potential acquirer who doesn’t put a lot of value on the team itself may smell blood and wait until you are really struggling to buy you out at a lower price. This all depends on what the acquirer values most about your business.
- Growth is likely to be knocked, given that certain key senior leaders will be concentrating on a M&A process (which can become all consuming). It’s one of the toughest dichotomies—you need the business to be growing in order to win over an acquirer (who won’t typically want to buy a flat-lining company) but revenue growth may suffer during this period, unless you run the M&A side of things extremely tightly. And, of course, if you do start growing like a rocketship again, your VCs (or other VCs) may once more be interested in funding the company. It’s a fine needle to thread.
- Often a company will want some more runway to get through a sales process.
Your existing investors: VCs won’t want to fund any further unless there is some kind of tentative offer or Letter of Intent (LOI) on the table. If your existing VC backers do decide to put forward funding, it is likely to come in the form of a convertible note. They do this instead of an equity deal because they don’t want to put a price on your business and set a new valuation anchor — the ideal outcome is that an acquirer decides your valuation in the coming months.
Similarly, VCs want to make an absolute minimum of a 3x return for every $ that goes out of their fund. They are unlikely to make that return by providing your company with funding at this stage, so instead they often attach a 2x redemption preference to the funding amount. This means that, if and when the business gets sold, this is the first money to be repaid and they will get 2x their money back (minimum).
Your existing debt providers: If you already have some venture debt in the business then it may make more sense to use this to fund the business through the sale process. Do the maths and never forget that, ultimately, most venture debt providers are like banks—they will do everything to be repaid.
The acquirer: Another option that is not uncommon but that may not be obvious is to have the potential acquirer bridge you to the final step of the process. Of course, this only becomes an option once you reach negotiation/LOI stage. It may feel like it weakens your position, but any potential acquirer will have seen your financials already as part of their due diligence, and it may be a simpler form of capital to raise than wrangling dollars from a fractious cap table via a punitive note. If the acquirer does lend capital, then it will wash out in the closing adjustments. Don’t forget, the acquirer of an early-stage startup is likely to be placing a lot of value on the team, so allowing payroll to be missed and key people to leave is a death spiral they don’t want to take place.
Again, you must have clarity here and alignment with your VCs on messaging to the market. Let’s say you have 8 months of runway and the prospective acquirer speaks to your lead VC. It’s the lead VCs job here to not say anything that would put off the acquirer. If the acquirer knows the business is running on fumes and that no one is interested in funding it, they may well just wait or pull out. I’m not encouraging any sort of misrepresentation here, but it’s this type of wrong move that can kill deals very quickly.
4) Figure out who gets what out of this sale
- Most recent investors: Big chunk of equity, top of waterfall, first equity holder to be repaid. Will want their capital back asap to deploy into other startups.
- Earliest investors: a good chunk of equity held, near to the bottom of the waterfall—either the same place or just ahead of the founders. Likely to have run out of funds to keep investing in the company and also most likely to end up ‘under water’ with nothing—the new owner doesn’t mind if early investors get zero, but they certainly don’t want the founders getting zero. Tough spot here—your earliest backers who took a chance on you can often get the most screwed. In scenarios 1-5 in the graph above, it would be likely that the acquirer would ‘re-cut’ the waterfall to ensure founders are receiving ‘meaningful’ proceeds (and not just the investors). So even though the legal documents may entitle the investors to get all of their money back, this may not end up happening and investors may agree to give up some of their proceeds to get the deal over the line.
- Employees: Smallest equity stake, bottom of the waterfall. Employees may lose their job (salary and benefits) and may or may not benefit from accelerated vesting of their options in a sale situation but, in all likelihood, they’ll transfer to the acquirer. The employees you need to be mindful of are those that are very valuable to the business—your CFO/COO/Chief of Staff is likely to become invaluable during a sales process and the acquirer will usually want the leadership team intact.
Founders: most equity at stake, bottom of the waterfall. Founders are typically vital to the success of the company and therefore the transaction itself. If there’s a buyer who likes what you’ve built, you are going to benefit. Most likely from the transaction proceeds and from growing the company in its new home. It makes no sense for founders to get zero/no incentivisation at all.
There’ll also be plenty of other claims being made on your “day one” distributable proceeds—corporate finance advisers, lawyers, accountants and then potential funds tied up in escrow for a certain period after the transaction has played out. Make sure you plug numbers into your waterfall early on to have an economic picture of where everyone stands — it will help give you a lens as to why you are getting the advice you are getting, or the questions being raised or why your pre-seed/seed stage investor feels a bit miffed.
If you’re using an equity management service like Carta then your waterfall should be readily available. If you’ve built your own model then have it double and triple checked—both by a lawyer and a VC (typically a legendary associate can help do this), independently. Home-grown models are usually wrong (sometimes very wrong) in their first version.
5) Put yourself in an acquirer’s shoes and listen to valuation advice
When it comes to valuing your company, know that your acquirer is not looking at your business through the same lens that you do.
There are basically three buckets for acquisition prices:
- Invested capital plus a bit (for an acquihire, distressed sale). The team’s outcome then is mostly on the back end through earn-outs and time at the acquirer.
- Multiple of this or next year’s revenue (for a company which isn’t a forced seller, has a good team/IP and/or where the situation is competitive)
- crazy AI prices (self explanatory!)
Going into a sale process with a lofty opinion of how your company should be valued may backfire—rejecting an early offer that feels too low may prolong the process to the point that you’re out of cash and the original offer is off the table. Get each of your large shareholders (founders, VCs) to tell you what they consider a ‘fair’ valuation of your business to be. They’ve been valuing the business for internal purposes once a quarter so they should have this information readily available.
Think critically about why a buyer/key sponsor within the buyer would put their neck on the line to buy this company. How does it make sense as a business case for what she (and her company) are trying to achieve?
Taking a punt on a seed-stage company is rarely the answer, and most companies can’t sensibly buy pre-series A startups (unless they are simply buying the team or IP). They want revenues, a customer base, brand and product already built out so they can bring their own sales and marketing to bear.
Don’t forget, the buying company will be taking on a big working capital burden to get a newly acquired business to wash its own face (and not be a drag on their balance sheet)—No buyer finds a high-burn, high head-count company an attractive proposition, even with the best technology/product in the world.
And one last point I’d make is that the reality is that most big companies screw up M&A integrations. It’s likely that in a year the acquired team will mostly leave, that ambitious revenue goals won’t have been hit (and you will feel it’s because you didn’t get the attention/resources/sales bandwidth etc you were expecting), and the product plan and product integration milestones will be missed. So, where you have discretion, don’t tie too much of your transaction to earn-outs based on ambitious and optimistic goals.
Happy to write more/chat more on this topic – let me know what interests you. There’s tons of rabbit holes I could go down!
The post Landing the Plane: 5 Tips for Founders Navigating M&A appeared first on Frontline.
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]]>1) Know your stakeholders and learn the right jargon
The entirely private US healthcare system can be difficult to navigate, especially when transitioning from Europe’s predominantly free and publicly funded systems. To simplify, about 92% of US patients have some form of healthcare insurance. They access medical services from providers — clinics or hospitals — aligned with specific insurance companies (think Aetna and Cigna) in a multi-payer system. Individuals get coverage through their employers or government programs like Medicare for seniors and Medicaid for low income individuals. When seeking medical care or prescription drug coverage, ‘free’ is not the norm. Even if employers deduct health insurance premiums from an employee’s paycheck, it doesn’t mean that all medical expenses are fully covered. Most people still need to pay out-of-pocket for certain aspects of their care, like copays, and deductibles which can vary considerably.
Providers utilise diagnosis and procedure-specific reimbursement codes in a fee-for-service model, billing patients individually after each visit. Those with insurance have their bills submitted to insurance providers, while uninsured individuals receive direct invoices. Efforts to enhance healthcare access, such as the Affordable Care Act (ACA and commonly known as Obamacare) have generated ongoing debate. The ACA was enacted as a means to provide affordable healthcare coverage however it continues to face significant criticism as it has increased taxes and deductibles for Americans. As a result, significant disparities across the nation continue to persist in healthcare delivery, access and outcomes.
Caroline Mitterdorfer, Co-founder and CEO of LEVY Health, learned quickly that though there were only two possible reimbursement models in Germany, in the USA there were so many more. Mitterdorfer comments that LEVY Health needed to be more “creative” with their American approach, and to tailor their sales pitches to the specific stakeholders that genuinely cared about the success of the company’s product. It is important to understand that your traditional European allies might be different and American stakeholders span from patients, healthcare providers, insurers, pharmaceutical and biotech firms to government agencies. US stakeholders also highly value companies that speak their language, so invest time in mastering the jargon and terminology that resonates with them (Healthcare.gov’s glossary of terms is a useful start).
2) Understand your market needs – the US is a different ballgame
Huma, a UK-founded remote patient monitoring platform, had already established a strong presence in Europe before making the leap into America. Kaushik Gune, Head of US at Huma, knew that despite some commonalities, entering the US market was akin to starting from scratch. Understanding the complexity of the American healthcare landscape, where there are multiple insurance carriers with thousands of payment plans and a fragmented provider space, Gune and his team realised that a one-size-fits-all approach would never work. Everything from patient needs, provider challenges and reimbursement constraints, was different and simply sending in European sales teams to pitch Huma’s existing product just would not work. The team spent months overhauling and re-engineering their product, business model and commercial approach to meet the needs of this new market.
LEVY Health also recognised the importance of both understanding the US market and planning a relevant go-to-market strategy several months in advance of moving. This research involved accelerator participation to learn the ins and outs of the American healthcare market to: assess product-market-fit, validate assumptions and allow agility in product development, refinement and overall sales approach. Remember, payers are not only interested in patient outcomes but are also keen on cost savings. Proving that your product boosts ROI by reducing hospitalizations or streamlining clinician workflows, for instance, is absolutely essential. LEVY Health nailed this strategy by demonstrating how their prescreening fertility software accelerates diagnosis and treatment initiation for female patients.
Milliman is an independent firm that specialises in helping companies develop an ROI strategy. This framework can then be shared with payers and other prospective stakeholders to assess your company’s alignment with potential solutions. Nevertheless, seeking guidance from firms like Milliman can present a challenge: you will need to demonstrate the impact of your product or service on health outcomes, and data from at least 1000 patients can be required. The issue lies in accessing these patients. Good news though, lessons five and six in this article offer insights that could facilitate your efforts in engaging and securing patients.
3) FDA Approval might be easier than you think
Maria Teresa Perez Zaballos, Co-founder and CEO of France-based Endogene.Bio emphasises that obtaining FDA approval is more than just a regulatory stamp; it serves as a powerful signal. Just as CE (European Conformity) marking ensures product standards in Europe, the FDA oversees medical devices, pharmaceuticals, food and cosmetics in the US. While the FDA approval process is rigorous, time-consuming and costly, it serves as a declaration to the global community that the highest standards of the most stringent regulatory body in the world have been met.
Ailene Thiel, Senior Healthcare Partner with The Virtuous Cycle Collaboratory, offers guidance to European healthtech startups looking to enter the US. She characterises the FDA as “supportive and nurturing” especially to companies that proactively engage with the agency early. And contrary to common belief, “the FDA is approachable,” she says. She advises healthtech startups to take advantage of the FDA’s Pre-Submission Program, where you can request meetings with FDA advisors on a number of topics such as: the appropriate regulatory path for a novel device, product risk assignation, and medical device classification. Even seemingly simple items like contact lenses and electric toothbrushes fall under the FDA’s device category, making early engagement with the FDA essential to understanding and fulfilling regulatory obligations.
While the FDA necessitates a fresh application, a significant portion of the groundwork is similar and efforts can be shared across the European CE mark application. Gune with Huma, emphasises that it was a strategic move to pursue both certifications consecutively.
Alex Gough, Co-founder and CEO of Sequins, is focused on developing technology that improves assay development for large diagnostic labs. In light of the evolving US regulatory environment, they are actively collaborating with these laboratory partners to help navigate the FDA approval process, through laboratory developed tests (LDT) or in vitro diagnostic (IVD) pathways, allowing Sequins to stay nimble in the process.
Another company that was interviewed is also navigating the regulatory maze; although their product required a CE marking in Europe, it was classified as a non-device in the US. So instead of recreating the same European playbook, the team sought expert guidance to decode the labyrinth of US regulations first. Healthcare-specific advisors are your allies. Gough’s advice is crystal clear though, “look beyond the on-the-ground US experts” to find founders, investors and professionals that have lived and breathed this very challenge. The key is to look for individuals deeply familiar with the ins and outs of the regulated US diagnostics market, from your very same starting point.
Mark Swanson, Partner at QRx Partners, a US-based quality and regulatory advisory firm, advises companies to remain vigilant. While your product may not be classified as a medical device now, Swanson highlights the potential for change. Indeed, many sub-sectors within healthtech like AI and diagnostic aids are still being navigated by the FDA, so it’s important to stay ahead of evolving regulations. According to Swanson, it’s possible the FDA could change rules, or (and more commonly) he adds, clinicians and other users might ask for added capabilities that could lead to the reclassification of your product as an FDA-regulated item. Swanson outlined that the key factor is the “intended use.” For example, “buying a hammer for home repair is one thing, but once it’s used for bone chipping it doesn’t matter that it may look like the same hammer, it is now a medical device that is regulated.”
4) Prioritise HIPAA with a dedicated team
To succeed in the United States, startups must also prioritise HIPAA (Health Insurance Portability and Accountability Act) compliance. This means adhering to the regulations set forth in the act to safeguard patient’s protected health information (PHI). HIPAA compliance is important for any company handling PHI, but can also extend to manufacturers in pharmaceuticals and medical devices (particularly class 3). Vanta, a compliance and security platform and one of Frontline’s portfolio businesses, offers a very helpful HIPAA compliance checklist for companies.
Since the initial act was brought forward in 1996, there have been ongoing amendments and additions given the evolving nature of healthtech. And companies must ensure they are also in compliance with any new regulations. HIPAA also requires that one team or individual be in charge of compliance so it’s important to assign your designated privacy and security officials early and with this in mind. While HIPAA is healthcare specific, it does share some common principles with Europe’s GDPR (General Data Protection Regulation). A solid foundation in GDPR compliance can be beneficial as these frameworks offer some overlap for European companies making the leap across the pond.
Mitterdorfer and team hired a single seasoned employee with experience in global quality affairs that kept up to date with HIPAA compliance and ensuring associated team training was up to code. The decision to hire a single employee focusing on compliance ultimately proved fruitful when landing initial customers; LEVY Health’s sales cycles were shorter and more streamlined due to the thorough work that had been completed long before boots were ever on the ground in the USA.
5) Key Opinion Leaders (KOLS) are important champions when moving to the USA
Due to the rigorous regulations in healthcare settings, progress can sometimes be slow. In the US, where your healthtech company may be relatively unknown, it’s imperative to garner support from individuals who can champion your cause. KOLs play a pivotal role in the success of your healthtech venture, as they not only validate your offerings but also cultivate public trust due to their influential expert status — both of which are absolutely crucial. Moreover, KOLs facilitate market access through their extensive networks and are able to provide valuable insights into the intricacies of the US healthcare system, which can in turn refine your product development strategy. KOLs can also enhance your company’s board, serving as a significant credibility marker to potential customers and investors.
Huma acknowledges the significance of aligning with individuals who deeply comprehend the company’s vision and products. Its KOL network spans across a spectrum of healthcare professionals including clinicians, physicians, pharmaceutical experts and medtech companies, which significantly bolsters the business’s credibility in a new market. Dr. Ansgar Lange, COO at Nostos Genomics, adopted a similar strategy, enlisting his European KOLs and investors to forge essential connections within the US market. He emphasises the importance of “starting with warm introductions where possible” and trying to have these KOLs in place before moving to the US — “you’ll want to hit the ground running.”
Dr. Thomas Heathman, CCO at Ori Biotech, also advises to “identify KOLs at an early stage and use their expertise to test industry assumptions.” Ori Biotech strategically aligned itself with influential figures and industry giants in genomics, such as Bruce Levine, referred to by Heathman as the “Madonna of cell therapy,” which provided invaluable third-party validation and referencing. With the support of prominent genomics leader Levine, Ori Biotech successfully launched into the US market with a pre-established and internationally recognised level of credibility.
6) Partnerships validate and open doors
Teaming up with major academic centres might be your golden ticket to kickstart the expansion journey. It’s not just validation; it’s a heavyweight endorsement of your company’s credibility. But brace yourself — you might be up against pharmaceutical giants and deep-pocketed competitors for those studies. To stand out, you need to be creative and break free from the mould.
Nostos Genomics has done just this. Realising that he was competing with companies with immense budgets for marketing, sales, and business development, Lange has doubled-down where large corporations cannot: strong customer service, agility and relationship building. Nostos Genomics also provides help with producing scientific research papers, which are particularly relevant and enticing for academics. And these efforts have paid off — Nostos Genomics is currently partnered with multiple major hospitals in New York. So, remember, a single ‘no’ doesn’t shut all doors. There are plenty of other folks within the same establishment who might be able to help and perhaps are more willing, too.
Despite being a prominent UK brand, Huma encountered questions like ‘but how would this work for American patients?’ when engaging with US customers. Their success in the UK and with the NHS certainly opened doors, but Americans were keen to learn how they could use Huma’s technology within their own workflows and clinical decision making. These inquiries were not limited to national boundaries, given state specific insurance plans and regulations. So, even if you’re a slam dunk success in New York, customers could still raise the ‘which insurance plans in Alabama will support this?’ question. While the compelling evidence they amassed in the UK paved the way initially to meetings, Huma also made the strategic move to extend their existing partnership with AstraZeneca to the US market.
Ori Biotech pursued a unique partnership strategy; rather than relying on partnerships as the primary gateway into the US market, the team focused on refining their product with partners that were understanding and forgiving if the product had early imperfections. Inspired by Mike Tyson’s wisdom: “Everyone has a plan until you get punched in the face” the philosophy highlights the importance of rigorous product validation and testing to ensure any major kinks are well ironed out before launching with customers. However, the commitment to continuous improvement did not deter the team from aiming high. Heathman characterises Ori Biotech’s approach as extremely ambitious. “We started by identifying our ideal partners and would then go and land them.” Their bold approach to partnerships is working, with heavyweights like MD Anderson — a leading US cancer care and research institute, ultimately endorsing Ori Biotech.
Acknowledgements
Thank you to: Ailene Thiel, Alex Gough, Ansgar Lange, Caroline Mitterdorfer, Christina Pecoraro, Courtney DeSisto Obecny, Dan Glazer, Dannie Hanna, David Rose, Kaushik Gune, Maria Teresa Perez Zaballos, Mark Swanson, Michael Macdonnell, Nick Treuil, Thomas Heathman.
And to Hannah Skingle, William McQuillan, Zoe Chambers, and the entire team at Frontline Ventures.
The post Conquering the US: 6 lessons for healthtechs appeared first on Frontline.
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