Stripe

The Death of the Seat-Based Revenue Model For two decades, the software-as-a-service (SaaS) industry has lived and died by the per-seat license. It was a simple, predictable metric: more employees meant more revenue. But as Manny Medina, the founder of Outreach and now Paid.ai, warns, this model is hitting an existential wall. The rise of autonomous AI agents—software capable of completing complex tasks without human intervention—means the very link between headcount and productivity is dissolving. When a single AI Agent can perform the work of ten sales development representatives, a company's headcount shrinks while its output expands. Under a traditional pricing model, the software provider is effectively penalized for their own efficiency. They provide more value but capture less revenue because there are fewer "seats" to bill. Medina realized this shift while leading Outreach, noting that when a CEO asks how many fewer people they need to hit their numbers next year, the software provider is essentially building their own contraction event. To survive, the industry must pivot from taxing human presence to monetizing autonomous outcomes. Lessons from the $4 Billion Category Creator Building Outreach from a struggling pivot to a $4 billion powerhouse wasn't a matter of luck; it was a masterclass in aggressive category creation. Medina breaks down the journey into distinct revenue milestones, each requiring a total evolution of the founder's role. The leap from $0 to $2 million in ARR was a street fight, involving door-to-door sales and hiring a VP of Sales on a commission-only basis. The jump to $10 million was where the real strategic work began: defining a new category. Category creation is often misunderstood as a marketing exercise, but it is actually a battle for the customer's mental model. Medina chose to be different rather than just better. Instead of competing with established players like InsideSales.com on their own terms, he framed Outreach as an entirely new entity: a sales engagement platform. This distinction allowed the company to own 100% of its niche. However, he warns that this path is "hard miles" and not for every founder. If you can replace an existing, clunky tool—like Linear did for project management—it is often a faster route to scale than educating a market on a problem they don't yet know they have. Why Durable Growth Beats Fast Growth In the current venture capital climate, there is an obsession with "fast growth" at all costs. Medina argues this is a dangerous distraction. Fast growth is often a byproduct of a temporary market tailwind or an unsustainable customer acquisition strategy. True wealth and enterprise value are built on durable growth. This requires a maniacal focus on the quality of revenue rather than just the quantity. During Outreach's hyper-growth phase, Medina actually banned certain types of "easy" money. He targeted "create and close" revenue—deals that opened and shut within the same quarter—because they were often inefficient and prone to churn. He insisted on adding friction to the sales process to ensure the product solved an existential problem for the buyer. If a customer screams "take my money," a founder's first job isn't to grab the check, but to understand exactly why they are buying. Without that understanding, you have no control over your retention, and the moment the market shifts, your business collapses. Durable growth means becoming a system of record or an essential part of an operation that the business cannot function without. The Financial Stack for the Agentic Era With Paid.ai, Medina is building the infrastructure he wished he had at Outreach. The problem with current billing systems like Stripe or Salesforce Billing is that they are built for SKUs and human users. They struggle to handle the high variability of AI agent operations, where costs are tied to token consumption and value is tied to specific outcomes like booked meetings or closed tickets. Paid.ai aims to consolidate what is currently a fragmented mess of 20 different tools—CPQ, quote-to-cash, billing, margin management, and API tracking—into a single record. For agent builders, the pain point is usually a "pricing pretzel." They start with a basic subscription, but then a customer wants to pay by outcome, and another by task complexity. Paid.ai allows these builders to instrument their agents once and then monetize them in any way the customer demands. This flexibility is the difference between a profitable AI business and one that sells a dollar of compute for fifty cents of revenue. The Strategic Advantage of Small Teams Returning to the early stage after running a massive organization has forced Medina to unlearn his "scale-up" habits. One of his most provocative strategies is the deliberate use of physical constraints, such as keeping an office that only fits 17 people. This isn't about saving on rent; it’s about forcing prioritization. In a large company, you can place three or four bets simultaneously. In a small, constrained team, you have to pick one winner. This lean approach extends to product development and market entry. Medina recently discovered that the quickest sales cycle for Paid.ai isn't through technical teams, but through commercial leaders who are struggling to monetize their new AI products. By narrowing the focus to this specific persona, he can drive predictable sales cycles rather than chasing every possible lead. Founders often fear that saying "no" to opportunities will stunt growth, but in the early days, focus is the only thing that generates the efficiency needed to survive until the next funding round. Fundraising as a Staging of Risk Medina's advice to first-time founders on fundraising is blunt: stop trying to raise the bare minimum. While some advocate for extreme capital efficiency, he views venture capital as a tool to chip away at specific risks. Raising too little money is a death sentence because it doesn't give the founder enough runway to survive the inevitable "gestation period" of a new market. If your timing is slightly off—as it often is with category-defining tech—you need the balance sheet to wait for the tide to turn. When he pitched EQT Ventures and Sequoia Capital for Paid.ai, he didn't use a polished deck. He presented a "catalog of problems" he had validated by calling dozens of agentic companies. This approach shifts the conversation from speculative dreams to concrete solutions. For an investor, backing a seasoned founder who is obsessed with a problem is a different equation than backing a newcomer with a nice slide deck. The goal of the first round isn't just to stay alive; it's to eliminate technical and market-fit risks so that by the time you reach the next round, all that's left is distribution risk.

The Death of Artisanal Software and the Rise of the AI Native Founder We are witnessing a fundamental shift in how companies are built, transitioning from a world where humans wrote 80% of code to one where 80% is generated by models. This isn't just a technical evolution; it's an existential change for the startup ecosystem. As a former operator at Microsoft and Stripe, I’ve seen the transition from hand-crafted "artisanal" software to what is now becoming "mass-produced" software. For the first time since the 1960s, the capabilities we once only dreamed of in computer science are becoming reality through Large Language Models. The barrier to entry for prototyping has vanished. We are now in the era of "vibe coding," where a founder with a clear vision can iterate faster than a traditional engineering team ever could. This creates a new expectation in the venture capital world. If you show up to a pitch for a pre-seed or seed round without a working prototype, you are sending a signal that you haven't embraced the current paradigm. AI native founders are prioritizing building over deck-perfecting, and those who spend their nights vibe coding are the ones winning the market. The New Economics of Capital Efficiency and Distribution In the previous generation of startups, a seed round was essentially a hiring mandate. You raised a few million dollars to hire five engineers and sat in a basement for nine months to ship a product. Today, the AI native playbook is radically different. We are seeing founders hire a single engineer and then spend their remaining budget on "fleets of agents," tokens, and sophisticated workflows. The cost of building has collapsed, leading to a massive reallocation of capital toward distribution, brand, and marketing. This capital efficiency is creating a competitive environment where speed is the primary weapon. One of the most striking pitches I've seen recently featured a founding team comprised of an engineering manager and five "Devins" from Cognition AI. For roughly $2,500 a month, they were doing the work that would have previously cost hundreds of thousands in payroll. This shift forces us to rethink what a "company" actually looks like. If the cost of the "act of building" goes to near zero, then value must be found elsewhere. Defensibility in a World of Carbon-Copy Software If an agent can look at a competitor’s website and replicate a feature in an afternoon, where does defensibility come from? The answer lies in the "good old moats" of the 2010s: distribution, data, taste, and brand. To survive, founders must become subject matter experts who own the holistic workflow of a problem. A customer buys Linear not because they can't find another issue tracker, but because the team at Linear has the best "taste" and expertise in how project management should actually work. Owning the workflow is also the only way to build a data moat. By facilitating the full journey of solving a problem, you collect the specific reinforcement learning data needed to train agents that are better than generic models. A generic AI won't know the nuances of a specific accounting operation or how a venture capitalist reviews a deal. If you don't own the workflow, you can't collect the data, and if you can't collect the data, you can't build a specialized agentic system. This is where the next generation of giants will be built. Agent Experience is the New Developer Experience We are moving beyond Customer Experience (CX) and Developer Experience (DX) into the era of Agent Experience (AX). As startups increasingly use tools like Lovable, Cursor, and Replit to build their products, the underlying infrastructure must adapt. These "vibe coding" tools are not just toys; they are the new primary users of APIs. Take Resend as an example. When a user asks Lovable to build an email flow, the agent recommends Resend. This creates a massive growth loop where the GDP of a business is directly correlated to the GDP of vibe coding. Infrastructure providers now need to treat agents as a first-class client type. This means optimizing APIs for agent consumption, much like we once optimized web experiences for mobile phones. My former team at Stripe is already doing this with specialized servers that agents can talk to directly. If you aren't optimizing for agents, you are invisible to the most productive builders in the market. Bridging the Atlantic Gap in Tech Ambition Having spent decades in both Copenhagen and New York, the cultural divide between European and American tech ecosystems remains stark. In Denmark, there is often a "tall poppy" syndrome where success is defined by a stable middle-management role. While this has improved, the US still holds a significant lead in celebrating risk and taking "big swings." Europe has traditionally used American primitives to build vertical SaaS, but the next decade offers an opportunity for Europe to build its own sovereign infrastructure and cloud primitives in a new geopolitical reality. However, for a European founder to truly scale, they must adopt a global mindset early. Expanding from Denmark to Germany isn't a big swing; the real market is the US. New York City has emerged as the ideal landing spot for these founders. It is the second-largest tech ecosystem in the world and offers a time zone that allows for seamless collaboration with engineering teams back in Lisbon, Stockholm, or Copenhagen. If you want to build a foundational company, you need to be where your customers are, and for enterprise tech and AI, that is increasingly New York. Inside the AlleyCorp Incubation Machine At AlleyCorp, we don't just wait for the right founder to walk through the door; we build the companies we want to see. Our incubation process is born from operational conviction. If we see a tangible problem in healthcare, robotics, or AI that nobody is solving correctly, we put a team together and lead as the interim CEO. This allows us to lean into our experience as former operators to de-risk the earliest stages of company building. A prime example is Radical AI. We saw a massive opportunity at the intersection of material science and AI, incubated the team, and a year later they raised $60 million to build foundational models for new materials. This model works because we have an in-house engineering team that acts as an execution capacity for our portfolio. We aren't just writing checks; we are building the machine that builds the companies. In an agentic world, this ability to rapidly prototype and validate ideas is the ultimate competitive advantage.

The technical architecture of a billion dollar insight Innovation is rarely a lightning bolt from the blue; it is more often a calculated response to a visible architectural failure. For Paul Anthony, the co-founder of Primer, the path to a half-billion-dollar valuation began by identifying a missing layer in the global commerce stack. While serving at Braintree, a division of PayPal, Anthony spent his weeks flying across Europe and the United States to meet with enterprise-level merchants. These were not small-scale operators; these were giants processing billions in transaction volume, yet they were all struggling with the same fundamental problem: their payment architecture was a fragmented mess. Most payment providers focus on their own siloed value. They want you to use their specific gateway, their specific fraud tools, and their specific ledger. However, a modern global business needs to reason about payments in a unified way. The insight that launched Primer was the realization that merchants were being forced to build their own internal infrastructure just to connect various payment service providers. Anthony saw a technical vacuum where a unified orchestration layer should have been. By identifying this technical gap rather than a mere marketing opportunity, he set the stage for one of the most aggressive growth trajectories in the European fintech scene, raising over $70 million and achieving a massive valuation within only 16 months of founding. Hypergrowth is a state of calculated chaos Scaling a company from a three-person team to a 200-employee enterprise during a global pandemic is not for the faint of heart. When Primer launched in early 2020, the world was on the brink of a total shutdown. Yet, this upheaval accelerated the shift to digital commerce, bringing the necessity of a robust payment stack into sharp focus for merchants worldwide. Anthony reflects on this period as one of "hyper-growth" that skewed his perception of reality, partly due to his proximity to other high-fliers like Hoppin, which achieved a multi-billion dollar valuation in record time. Managing this growth required a rejection of the traditional "Lean Startup" methodology. When you are asking a multi-billion dollar merchant to rip out their Stripe or Adyen integration to replace it with your infrastructure, "minimum viable" doesn't cut it. You cannot compromise on robustness when you are the foundation of another company's revenue. This necessitated massive capital and rapid resource allocation. The pressure was intense, and the technical seams were often stretched to the breaking point. However, the conviction of tier-one VCs like Balderton, Accel, and Iconiq%20Capital provided the fuel to build a heavy-duty enterprise product while the company was still effectively in its infancy. Autonomy is a requirement rather than a benefit One of the most provocative elements of Anthony's leadership philosophy is his approach to human capital. He rejects the idea that autonomy is a perk or a benefit listed in a job description. Instead, he views autonomy as a hard requirement. In the chaotic environment of a high-growth startup, there is no room for hand-holding. If a team member cannot take the lead and drive their own sector of the business, the entire machine slows down. This philosophy dictated a grueling hiring process where Anthony personally interviewed 20 to 30 candidates for every single hire, seeking individuals who could thrive in an environment where the internal mantra was: "We are not a real business yet." This mentality serves as a defense against the complacency that often follows a successful funding round. In many US-centric startup cultures, raising money is celebrated as the finish line. For Anthony, raising money was simply proof that the team had to work harder to prove they weren't wrong. This "healthy paranoia" ensured that the product and engineering teams remained agile. He encouraged his engineers to "play jazz," emphasizing that until the company is turning a profit, they are in a state of constant experimentation. By giving employees massive leeway and responsibility, he created a trajectory where team members could grow their careers five times faster than they would at a legacy firm like Microsoft or PayPal. The feeling of the product outweighs the paper specs In the world of enterprise software, it is easy to get lost in feature lists and technical specifications. Anthony argues that the most important metric for a product is how it actually feels to the user. This is why he is a staunch advocate for technical spikes and Proof of Concepts (POCs) over lengthy theoretical planning sessions. Software is built for humans, and if a human cannot intuitively reason about an abstraction, the product has failed. At Primer, this meant constantly reassessing the models and abstractions they were building. If a merchant couldn't understand how to optimize their payment stack through the interface, the engineering was irrelevant. This focus on "feeling" and simplicity is now being carried over into his new venture, Colossal. By taking complex primitives—whether they are payment flows or AI-driven commerce journeys—and making them feel simple to a non-technical creator, Anthony is attempting to democratize the sophisticated tools that were previously reserved for massive corporations. Colossal and the prompt-based future of commerce Anthony's newest venture, Colossal, represents a dramatic shift from the enterprise-heavy world of payment orchestration to the burgeoning creator economy. Described by some as the "Lovable for commerce," Colossal aims to tap into a digital goods market projected to hit $400 billion by 2030. The core problem Anthony identified here is that while platforms like Shopify are powerful, they are often too broad or too complex for a solo entrepreneur who just wants to sell a course, a digital license, or access to a Discord community. Colossal leverages Large Language Models (LLMs) to create a prompt-based interface for building commerce journeys. Instead of navigating a complex dashboard with a hundred different KPIs, a user can simply tell the AI what they want to achieve—such as "I want to sell a micro-SaaS and give people a discount code for my Discord." The system then assembles the entire infrastructure, from the storefront to the back-end integrations with tools like Klaviyo or Intercom. This isn't just about building a page; it's about building a journey. Anthony views AI as an assistive library that allows users to think outside the box, offering them the flexibility of a developer without requiring them to write a single line of code. Redefining the merchant of record The traditional "Merchant of Record" model is often sold on the basis of compliance and tax handling. However, Anthony’s research indicates that for the modern creator, compliance is a secondary concern. The real value driver is the ease of billing and the aesthetic quality of the customer journey. Colossal is positioning itself as an open platform that prioritizes these high-value touchpoints. By using AI to ingest data from an Instagram profile or a Figma design, the platform can instantly replicate a brand's style and suggest the best payment methods for their specific demographic. This approach reduces the "time to value" to nearly zero. In an era where creators have shorter attention spans and higher expectations for their tools, the ability to generate a fully functioning commerce stack through a simple conversation is a significant disruption. It moves away from the static, one-size-fits-all storefront and toward a real-time, personalized commerce experience that evolves with the business. Future outlook for the commerce stack Looking ahead, the evolution of commerce will be defined by the further abstraction of complexity. Paul Anthony suggests that 20 years from now, we will look back at the current state of online shopping as a primitive beginning. The next generation of infrastructure providers will be those who can take the massive, daunting world of global payments, licensing, and community building and condense them into a few natural language prompts. Whether through Primer's orchestration for the enterprise or Colossal's AI-driven journeys for creators, the goal remains the same: enable people to reason about complex things so they can do more. By taking calculated risks and maintaining a culture of constant reassessment, Anthony is betting that the biggest winners in the next decade will be the companies that provide the most powerful building blocks for the rest of the world to build upon. The status quo is always vulnerable to a better abstraction.

The Quantitative Path to High-Stakes Venture Capital Success in venture capital rarely follows a linear trajectory, but for Andrei Brasoveanu, a partner at Accel, the journey began with the rigorous logic of mathematics. Growing up in Romania, Brasoveanu’s early life revolved around international math competitions, a foundation that eventually secured him a scholarship to the United States. This move marked his first experience with the "can-do" energy of American ambition, a trait he now looks for in the founders he backs across Europe and Israel. Before entering the venture world, Brasoveanu spent a decade on the East Coast, eventually working as a quantitative analyst in high-frequency trading. This period provided a window into systematic investing and the bleeding edge of technology applications. When he joined Accel eleven years ago, he brought that analytical rigor to the London office. Today, his strategy is defined by a mix of deep technical understanding and an unwavering focus on the human element of company building. He operates with the belief that while markets and technologies are in constant flux, the character and intensity of the founder remain the only reliable constants. Why Intensity and Brainpower Trump Industry Experience The search for the next unicorn often leads investors toward established hubs and pedigreed resumes, but Andrei Brasoveanu argues that the most promising "gems" are frequently hidden in unobvious locations. He cites Humio, a logging technology firm based in Aarhus, Denmark, as a prime example. The team was highly technical but operated outside the traditional VC orbit. By backing them early, Accel helped scale a solution that challenged legacy leaders like Splunk, eventually leading to a successful integration with CrowdStrike. When evaluating these early-stage opportunities, Brasoveanu prioritizes sheer intensity and drive, ideally paired with a cerebral, thoughtful approach. Interestingly, he does not over-index on previous experience. Many of his most successful investments, such as Celonis, were led by first-time founders who were "hungry" and capable of learning at a chaotic pace. In the case of Celonis, a Munich-based team of three founders in their twenties bootstrapped an academic project into a global leader in process mining. Their success wasn't born from a deep resume but from a willingness to experiment—evidenced by their early decision to test the market by charging €100,000 for a service they initially considered pricing at €5,000. Combatting Fake Traction with Founder Conviction One of the most significant challenges in modern venture capital is the rise of "fake traction." With easier access to distribution channels and the ability to sell to a network of fellow startups, many companies show early growth that fails to "cross the chasm" to broader enterprise adoption. Brasoveanu warns that the business model that gets a company to its first few million in revenue is rarely the one that leads to greatness. This reality is why Accel remains conviction-driven at the seed stage, often backing teams before they have a product or even a fully formed idea. By focusing on the founder as the anchor, Accel can weather the inevitable pivots that occur as markets evolve. Brasoveanu believes a VC's role is divided: 80% is what he calls "hygiene work"—helping founders avoid common mistakes in hiring, option plans, and M&A processes. The remaining 20% involves navigating "crucible moments," such as intense competitive threats or fundamental disagreements between co-founders. In these high-pressure scenarios, the relationship between the investor and the founder, built on transparency and mutual respect, becomes the deciding factor in the company’s survival. The Vibe Coding Revolution and the Future of Custom Software The landscape of software development is undergoing a seismic shift with the emergence of "vibe coding" and AI-native stacks. This trend, which allows for near-instant creation of back-ends and front-ends, is lowering the barrier for non-technical creators. However, Brasoveanu holds a somewhat controversial view: he believes this environment actually increases the value of truly technical founders. As technology becomes more accessible, the edge goes to those who understand core technical principles and can orchestrate complex systems most effectively. This shift is also paving the way for "personalized SaaS." Brasoveanu notes that large enterprises may eventually move away from bloated, one-size-fits-all solutions like Salesforce in favor of homegrown, tailor-made software built internally with AI assistance. To capitalize on this, Accel recently led the seed round for Polar, a Swedish payments infrastructure startup founded by Birk Jernström. Polar aims to become the payment standard for this new AI-native stack, offering a streamlined experience that legacy providers like Stripe can no longer deliver as they become increasingly "bloated." Strategic Orchestration of the Unicorn Network A critical component of Accel's strategy is the intentional orchestration of its network. When backing a new company like Polar, Brasoveanu doesn't just provide capital; he brings in a cadre of strategic angels from the Accel family, such as the founders of Vercel, Supabase, and Framer. This isn't a mere PR tactic. By surrounding early-stage founders with seasoned operators who have achieved scale, Accel creates a feedback loop of mentorship and potential partnerships. This collaborative approach extends internally across Accel’s global offices. The firm operates as one cohesive unit, sharing insights between early-stage and growth-fund teams. This cross-pollination allows them to identify "holes in the stack"—identifying a need for specialized payments through Polar while simultaneously backing the infrastructure backbone via Supabase. By maintaining a boutique, personal feel despite their institutional scale, Accel continues to position itself as a "kingmaker" in the global tech ecosystem, betting on the individual's ability to disrupt the status quo.

The Premise of the Great Collapse Recent industry whispers and social media trends suggest that Software as a Service (SaaS) is facing an existential crisis. The argument, often echoed by leaders like Satya Nadella, posits that most business applications are merely CRUD databases wrapped in business logic. With the rise of AI agents and "vibe coding," many believe these platforms will collapse into a single, fluid agent era where bespoke internal tools replace expensive subscriptions. While the technical barrier to entry for building software is plummeting, the reality of running a global service remains stubbornly complex. The Barrier of Invisible Infrastructure Software development is often the simplest part of a successful SaaS product. High-utility platforms like Stripe or Squarespace do not just offer code; they provide a gateway to massive, regulated ecosystems. Consider Stripe. A developer might "vibe code" a functional payment button in an afternoon, but they cannot code the legal agreements with global banks, compliance with international tax laws, or the trust required to handle millions in transactions. The value lies in the hard-won partnerships and infrastructure that an AI agent cannot simply prompt into existence. Regulation and the Compliance Moat Regulatory requirements act as a natural defense for established platforms. An accounting SaaS must adhere to GDPR, ISO security standards, and local tax laws that vary by country. In the Netherlands, for instance, independent accountants often only support specific, validated platforms. You cannot replace a legally compliant audit trail with a custom-coded agent if the bank refuses to grant that agent API access or if the government doesn't recognize the output. These administrative and legal hurdles form a "moat" that protects the SaaS model from being completely disrupted by decentralized AI tools. The Future of Integrated Intelligence Instead of dying, SaaS is evolving to absorb the very tools meant to replace it. Platforms are already implementing Model Context Protocol (MCP) to allow AI agents to interact with their data seamlessly. We are moving toward a hybrid world where graphical user interfaces and chat interfaces coexist. The goal remains efficiency. It is still cheaper and more reliable to pay for a specialized service like Spotify than to build a custom player, negotiate music label licenses, and manage cloud streaming personally. SaaS isn't dead; it's simply getting smarter.

Overview of MCP Model Context Protocol (MCP) serves as a universal interface between Large Language Models and external data. While models like ChatGPT often live in isolated environments without network access, MCP acts as a standard connector. It allows an AI to understand how to call tools, format parameters, and interpret responses from your custom systems. Prerequisites To build an MCP server, you should possess a solid foundation in Python, specifically regarding asynchronous programming. Familiarity with JSON configuration files and basic REST API concepts is essential for implementing robust integrations. Key Libraries & Tools - **Fast MCP**: A high-level Python framework designed to streamline the creation of MCP servers. - **HTTPX**: A next-generation HTTP client for Python, used for making asynchronous API calls. - **FastAPI**: A modern web framework for building RESTful APIs that can be wrapped by MCP. - **YouTube Search**: A Python utility for querying video metadata. Code Walkthrough You can initialize a server using the `FastMCP` class. This server defines "tools" that the LLM can invoke. Below is a foundational implementation that exposes a search function. ```python from mcp.server.fastmcp import FastMCP Initialize the MCP server mcp = FastMCP("VideoSearch") @mcp.tool() def search_videos(query: str): """Search for videos based on keywords.""" # Logic to fetch data goes here return f"Results for {query}" ``` The `@mcp.tool()` decorator is vital; it generates the schema that tells the LLM exactly how to use this function. In a more advanced architecture, your MCP server should act as a thin client for an existing REST API to avoid logic duplication. ```python import httpx @mcp.tool() async def get_api_videos(query: str): async with httpx.AsyncClient() as client: response = await client.get(f"https://api.example.com/search?q={query}") return response.json() ``` Syntax Notes - **Docstrings**: MCP uses Python docstrings to explain tool functionality to the AI. Clear descriptions are mandatory. - **Type Hints**: Explicit typing (e.g., `query: str`) helps the MCP server generate the correct JSON schema for the LLM. Practical Examples Beyond searching for videos, MCP enables AI to interact with GitHub repositories, manage Stripe subscriptions, or query internal company databases directly through an interface like Claude Desktop. Tips & Gotchas Avoid direct function calls if you already have a REST API. Treating the MCP server as a separate "user" of your API ensures that bug fixes in the core logic propagate to your AI tools automatically. Always check your `config.json` pathing, as incorrect directory references are the primary cause of connection failures.

The chemistry of co-founder alignment Romain Sestier and Guillaume Lebedel did not start StackOne because of a white paper or a market analysis. They started it because they wanted to work together again. This reversal of the traditional startup narrative—where the "visionary idea" usually takes center stage—is the fundamental bedrock of their recent $20 million Series A success. Having known each other for a decade and weathered the storms of three different companies, including the acquisition of Yieldify by Publicis, they entered the venture arena with a base layer of trust that most founders spend years trying to manufacture. Finding a co-founder is not about an interview process; it is about shared history and battle-tested reliability. Sestier argues that the co-founder relationship is more important than the product or the space. If you do not have a decade of history to lean on, you must artificially create it through high-intensity side projects or consulting gigs. The goal is to see how your partner reacts under pressure before you have millions of dollars and dozens of employees on the line. At StackOne, this trust allowed them to move with a speed that GV and Workday Ventures found irresistible. Shifting the mindset for venture scale Building a venture-scale company requires a psychological pivot that many talented entrepreneurs never fully complete. Sestier credits a former mentor at Google for a piece of feedback that changed his trajectory: "You're dreaming too small." This is a common trap for founders who focus on building a "good" business instead of a market-dominating infrastructure. Venture capital is not just fuel; it is a commitment to a specific level of aggression and magnitude. Once you take the money, the chips are down, and you have to play for the billion-dollar outcome because everyone involved has already agreed to that vision. This mindset shift trickles down into every operational decision. It means hiring for "talent density" rather than just filling seats to meet headcount goals. Guillaume Lebedel emphasizes that in the early days, you are not just hiring for skill; you are hiring for a lack of ego. You need "doers" who can transition from writing code to talking to clients without feeling that certain tasks are beneath them. The moment the talent density drops, the founders lose control of the culture, and the venture-scale dream begins to dilute into a series of middle-management compromises. The discipline of founder-led sales Sales at the seed and Series A stage is not a clean, automated process. It is "dirty work." StackOne achieved its initial traction by doing things that do not scale—cold calling, leveraging personal networks, and obsessively following up with prospects. Romain Sestier views sales through a simple framework of packaging and process. The packaging is about identifying why a customer should change their status quo right now. The process is about making it physically and legally easy for them to buy. One of the most dangerous traps for early-stage SaaS companies is "revenue at all costs." Sestier warns against selling to the wrong customers, even if they are willing to pay. If a customer drags your product in a direction that deviates from your long-term vision, you must be disciplined enough to cut them loose. You want the right type of revenue—the kind that validates your core thesis and helps you build a repeatable motion. At StackOne, this meant focusing on the CTO as the primary buyer and ensuring the product offered immediate, high-trust infrastructure value. Engineering the internal champion In enterprise sales, your biggest obstacle is not your competitor; it is your customer's internal bureaucracy. Founders must learn to treat their internal champions as partners in a joint venture. This means doing the work for them. If a champion needs to present a business case to a CFO, the founder should be the one writing the memo and preparing the data. You are essentially saving their time and reducing their reputational risk. Guillaume Lebedel notes that being a developer-centric product adds another layer of complexity. You are selling an SDK or an API that other engineers will have to live with every day. This requires a level of transparency and documentation that goes beyond typical sales collateral. By reducing the friction for the end-user (the developer) while simultaneously arming the champion with the financial justification for the CFO, StackOne created a dual-track sales motion that accelerated their growth past the "friends and family" network. Maintaining agility in the Series A era With $20 million in the bank, the temptation is to build a massive, rigid hierarchy. StackOne is fighting to stay nimble. This requires a culture where everyone stays close to the customer, regardless of their title. Guillaume Lebedel insists that even his engineers attend events and speak with users. This keeps the feedback loop short and prevents the product from becoming a bloated collection of features that nobody asked for. Ultimately, the Series A is just the beginning of the next chapter. The capital provides the runway to take bigger shots and move faster, but the core principles remain the same: high talent density, low ego, and a relentless focus on solving a massive problem. As StackOne continues to build the universal integration layer for B2B SaaS, Sestier and Lebedel are proof that the strongest startups are built on the foundation of shared history and a refusal to dream small.