The lifecycle of a startup is often described as a linear sequence of funding rounds, from pre-seed to maturity. In reality, progression is not driven by capital alone, but by the ability to resolve successive and fundamentally different forms of uncertainty. Each stage of a startup’s development corresponds to a specific question—about the problem, the solution, the market, or scalability—and only companies that answer these questions convincingly are able to move forward.

Preseed:

The pre-seed phase is not concerned with execution, growth, or scale, but with a more fundamental question: whether a company should exist at all. At this stage, a startup is neither a product nor a business, but a set of unresolved uncertainties concerning the customer, the nature of the problem, and the solution. The purpose of pre-seed is therefore not to validate an idea in the abstract, but to reduce uncertainty in a structured manner, transforming intuition into grounded understanding before meaningful commitment takes place.

Problem discovery

Every startup idea originates from an intuition; intuitions, however, are not actionable unless they are translated into explicit, testable assumptions. These assumptions consistently concern the same dimensions: who the customer is, what problem they experience, when it occurs, and why it is sufficiently painful to influence behavior. An assumption becomes meaningful only when it is stated with enough precision to be falsified, allowing it to be confronted with reality rather than sustained through narrative.

The confrontation with reality occurs through direct interaction with customers, typically via one-on-one conversations focused on observed behavior rather than stated opinion. In the pre-seed phase, asking whether someone would use a product yields limited insight; what matters instead is understanding how people currently act, which tools they rely on, and how they respond when the problem actually arises. Individual conversations are rarely informative in isolation. Insight emerges when similar behaviors, constraints, and workarounds recur independently across multiple interviews, allowing signal to emerge from noise.

As these patterns stabilize, the problem can be structured along three dimensions: frequency, severity, and existing alternatives. If the customer already uses an imperfect solution, it doesn’t mean that the problem is solved; rather, its existence is a positive signal, as it indicates that the problem is real, recurring, and that customers are actively seeking a way to solve it.

Once the problem has been clearly structured does it become meaningful to introduce a solution hypothesis, in the form of a minimal demo such as a landing page, a short deck, or a clickable prototype. At this stage, the relevant signal is not general interest, but urgency. Expressions such as “this is interesting” carry little weight, whereas questions like “when can I use it?” or “when will it be available?” signal that the proposed solution addresses a top-three customer problem.”

MVP

The next step is to assess whether the proposed solution is the right one. This is the role of the MVP. An MVP is not a simplified version of the final product, but the minimum artifact required to test the startup’s core hypotheses, which typically concern sustained usage, effective problem resolution, and willingness to pay. At this stage, speed outweighs quality and learning outweighs scalability; building something that does not scale, relies on manual intervention, or lacks polish is often the most efficient way to achieve a rapid feedback cycle

In this sense, the MVP functions less as a product and more as an instrument for learning, designed to inform subsequent decisions rather than to impress external observers.

Cofounders

Alongside product exploration, founders must determine whether to bring in one or two co-founders. This decision should be driven by necessity rather than convention. From a skills perspective, a co-founder becomes essential when critical capabilities are missing and cannot be outsourced without slowing learning or execution. From an emotional perspective, pre-seed is a cognitively demanding phase, characterized by frequent feedback, shifting direction, and persistent uncertainty. In this context, a co-founder provides not only complementary skills, but also stability, helping to distribute cognitive load, challenge assumptions, and maintain perspective as the startup evolves.

Funding

Finally, pre-seed often coincides with the startup’s first interaction with capital. At this stage, companies are typically pre-revenue and highly uncertain, making fundraising difficult despite the relatively limited capital required. Funding may come from family and friends (often the preseed round is called “family and friends”), angel investors, crowdfunding initiatives, incubators, accelerators, competitions, or grants, while some founders choose to bootstrap in order to preserve flexibility. Regardless of the source, raising pre-seed funding requires demonstrating a clearly defined customer segment, a coherent MVP, and early signals of traction or engagement. Runways are usually short, often under one year, and investment decisions are rarely driven by metrics alone; they primarily reflect confidence in the founders’ ability to reason clearly, learn efficiently, and make disciplined decisions under uncertainty.

Some datas about preseed funding:

Sources: P101 State of Italian VC 2024 (IT, EU); Carta 2025 (US); KPMG Venture Pulse Q4 2025 (US)

– median values

Italian and European funding rounds tend to be smaller because the overall investment market is less capitalized than in the U.S., and there are fewer large venture funds able to support startups in later rounds at high valuations.

Risk appetite also plays a role. U.S. funds are generally more willing to invest heavily in small teams based primarily on vision and high potential, whereas European and Italian investors typically require clearer KPIs, traction, and more established metrics before committing significant capital.

This pattern extends to later stages as well: greater availability of capital—if deployed effectively—enables faster growth, stronger market positioning, and ultimately higher valuations.

Seed:

If pre-seed is about understanding whether a company should exist, the seed phase is about proving that it can exist as a real business. At this stage, the startup is no longer a collection of hypotheses, but it is not yet a scalable company. More specifically, the purpose of the seed phase is to transform an MVP into a stable, focused product, make a first credible entry into the market, and generate concrete evidence of Product–Market Fit, in the form of traction or early revenues.

From MVP to a Stable Product

An MVP is designed to learn; a seed-stage product is designed to deliver value reliably. This transition does not mean adding features. During the seed phase, a startup should concentrate on one, at most two essential functions—those that directly solve the core customer problem identified during discovery and that guarantee a real competitive advantage. Everything else is postponed. A seed-stage product wins not by being complete, but by being decisively better at one specific job.

The Goal: Product–Market Fit

The central objective of the seed phase is to reach Product–Market Fit (PMF). PMF occurs when a clearly defined group of customers consistently uses—and is reluctant to give up—a product because it solves a top-priority problem for them. Importantly, PMF is not proven by vision, excitement, or press, but by key metrics that summarize customer behavior.

Activation captures whether new users reach their first moment of value.

• Activation Rate = Activated users ÷ total sign-ups — Share of users completing the key event that represents initial value.
• CAC (Customer Acquisition Cost) = Total acquisition spend ÷ activated users — Cost required to acquire a user who actually experiences value.

If users do not activate, nothing downstream matters.

Retention is the strongest signal of PMF.

• CRR (Customer Retention Rate) = (Customers at end of period − new customers) ÷ customers at start — Ability to retain users over time.
• Stickiness = DAU ÷ MAU or WAU ÷ MAU — Frequency and regularity of usage.
• WAU / MAU — Degree to which the product becomes part of a weekly routine.

Retention indicates that the product solves a recurring problem and that value is delivered repeatedly, not accidentally.

Revenue metrics provide economic proof of value.

• MRR / ARR = Sum of recurring subscription revenue — Predictability of revenues.
• CCV (Customer Contract Value) = Total contract value ÷ number of customers — Economic weight of a customer.
• Repeat Purchase Rate = Customers with repeat purchases ÷ total customers — Consistency of demand.

Even small revenues demonstrate willingness to pay and reduce ambiguity for future investors.

Hiring first Employees

During pre-seed, founders ask whether they need cofounders; in seed, the question becomes whether they need employees. This shift happens when founders spend a significant portion of time on tasks that do not require strategic decision-making, repeat daily, and can be executed better by someone specialized. Over time, these activities create bottlenecks that slow execution and learning, making hiring necessary.

Early employees are not founders, but they are not ordinary hires either. They often receive equity compensation, reflecting both risk and long-term alignment, and their contribution directly shapes the company’s trajectory. Typical first hires include development roles to stabilize and extend the product, or sales roles to systematize early revenue, although the exact profile depends on the nature of the startup. A deeptech company, for example, may prioritize researchers, while a marketplace may prioritize operations.

The Seed Round

The purpose of a seed round is not to scale the company, but to fund the transition from validation to credibility. Seed capital buys time—typically 12 to 18 months—to reach milestones that make a future Series A plausible. Funding sources remain similar to pre-seed, but at this stage a new player enters the stage: venture capital funds.

Venture returns follow a power law: a small number of investments generate the majority of returns. As a result, VCs actively seek outliers—companies with the potential to produce extremely large outcomes. For a VC, a startup that stagnates is equivalent to a failure; moderate success does not return the fund. This dynamic explains both a preference for high-risk, high-return opportunities and the emergence of the seed squeeze, where large amounts of capital are deployed into fewer companies. VCs therefore look for a large Total Addressable Market, a clear and focused roadmap, and a credible explanation of how seed capital will be used to reach Series A–ready milestones.

A Note on Valuation:

A high valuation at seed may appear attractive, but it can be counterproductive. By raising the entry price, it raises expectations and makes the milestones required to justify a Series A more demanding, increasing execution risk. At seed, valuation should support progress rather than constrain it: the objective is not to maximize price, but to preserve optionality.

Sources: P101 State of Italian VC 2024 (IT, EU); Carta 2025 (US); KPMG Venture Pulse Q4 2025 (US)

– median values

Series A:

If pre-seed is concerned with whether a company should exist, and seed with whether it can exist as a business, the Series A round addresses a more structural and demanding question: whether the company is capable of sustaining growth over the long term while progressively converging toward profitability

From a risk perspective, Series A represents a qualitative shift. In earlier stages, risk is predominantly epistemic: the startup may fail because the founders misunderstand the customer, misjudge the severity of the problem, or propose an inadequate solution. By Series A, many of these uncertainties have been at least partially resolved. The dominant risks now become executional and structural. Failure is less often the result of being wrong, and more often the consequence of being unable to scale what already works. As a result, the tolerance for error decreases sharply.

Production grade product

During pre-seed and seed, the product primarily functions as an instrument for learning: an artifact designed to test hypotheses, elicit feedback, and reduce uncertainty. In Series A, the product becomes the company’s primary economic engine and the main driver of growth.

This transition usually coincides with the passage from a prototype or early product to a production-grade offering. The defining requirement at this stage is scalability. A Series A product must be able to support a customer base that is an orders of magnitude larger than at seed, often ten or one hundred times greater, without a proportional increase in operational complexity, costs, or failure rates. This requires deliberate investments in system architecture, infrastructure, security, and monitoring, as well as robust debugging and error-handling mechanisms.

At the same time, user experience becomes a strategic concern. Early-stage users are often willing to tolerate friction, instability, and incomplete workflows. As the company scales, and the customer base grows, this tolerance disappears. Small usability issues compound into large losses in activation and retention. UX improvements are therefore no longer cosmetic; they directly affect growth efficiency and unit economics.

Channels: from Exploration to Optimization

A similar evolution occurs on the go-to-market side. During seed, distribution channels are explored opportunistically, with the goal of discovering whether acquisition is possible at all. In Series A, the emphasis shifts from exploration to optimization and repeatability. The company is expected to have identified one or two primary acquisition channels and to demonstrate that these channels can be scaled in a controlled and economically sustainable way.

This involves systematic optimization of the entire funnel, from acquisition to activation, retention, and monetization. Metrics such as customer acquisition cost, payback period, conversion rates, and churn are no longer observed retrospectively, but actively managed.

Team Evolution: From Bottleneck Removal to Organizational Leverage

Team composition also changes in nature. In the seed phase, hiring is primarily aimed at bypassing execution bottlenecks: founders bring in strong individual contributors to maintain speed and avoid distraction. In Series A, the company begins to make its first senior hires, typically in technology, sales, and operations.

Senior hires are expected to operate at a different level of abstraction. A senior technical leader makes architectural decisions that affect scalability and maintainability over year, for example a senior sales leader designs repeatable sales motions rather than closing individual deals.

At this stage, companies prioritize top-tier talent. Hiring from prestigious universities, top consulting firms, Big Tech, or elite startups is not merely a signaling strategy. Series A companies operate in environments where leverage comes from judgment, learning speed, and ownership. Mediocrity does not fail immediately, but compounds negatively as the organization grows.

As headcount increases, the first managerial roles emerge. Typical examples include an Engineering Manager coordinating multiple developers, a Head of Sales managing early account executives, or an Operations Manager formalizing internal workflows. Alongside these roles, minimal but explicit processes are introduced, such as structured hiring pipelines or clear role definitions.

Governance and board

Governance undergoes a parallel process of formalization. While a board may already exist at seed, it is often informal and advisory in nature. At Series A, the board becomes a formal governing body. The founder is no longer the sole authority; major decisions must be presented to, justified before, and approved by the board.

This transition profoundly changes how decisions are made. Founder intuition, while still valuable, is no longer sufficient. Strategic choices must be supported by data, trends, and explicit trade-offs. As a result, companies introduce regular reporting cycles—typically monthly or quarterly—based on a stable set of metrics such as growth rates, retention, burn rate, runway, unit economics, and cash position. The organization shifts from narrative-driven to metric-driven alignment.

The board typically has authority over key decisions, including senior hires, fundraising, acquisitions, and exit opportunities. It also plays a central role in risk management, ensuring that growth does not compromise long-term viability.

A standard Series A board consists of four or five members, commonly two founders and two venture capital representatives, additional participants may attend as observers. Observers have no voting rights and bear no legal responsibility, but can exert significant influence through experience and perspective. They often include non-lead co-investors, particularly involved VCs, or seed investors seeking continued engagement.

When the board has five members, it is common to appoint an independent director, formally unaffiliated with either founders or investors. This individual acts as a balancing force and is often a former successful founder, CEO, or senior executive, such as a CTO in a technical company.

Series A Preferred Stock

Series A investments are typically made through preferred shares, which differ from common stock in several key respects. Preferred shares usually include a liquidation preference, granting investors priority in the distribution of proceeds in the event of an exit, often ensuring at least the return of invested capital before common shareholders receive anything.

They also commonly include anti-dilution protections, designed to partially protect investors in the event of a down round. Under weighted-average anti-dilution, for example, the investor’s conversion price is adjusted downward, allowing partial recovery of value without fully neutralizing dilution for founders.

Finally, Series A preferred shares typically grant board rights, formalizing investor involvement in governance.

What Investors Look for in a Series A:

Series A investors focus on a limited number of signals. The first is compound growth, typically in the range of 10% to 30% month-over-month. Compounding at these rates leads to dramatic annual outcomes.

Second, investors seek confirmation of Product–Market Fit, through the stabilization and improvement of the same metrics introduced at seed, particularly retention and unit economics.

Third, they look for a clear path to growth: a visible demand pipeline that is just out of reach, but easily accessible with additional resources. Growth should feel constrained by capacity, not by demand.

Fourth, team quality remains central. Top-tier talent not only improves execution, but also increases credibility with future hires, customers, and investors.

Finally, investors assess defensibility, particularly through network effects or structural advantages that allow the company to become a market leader over time. Startups that benefit from strong network effects—such as marketplaces or communication platforms—become increasingly difficult to displace as they scale, making early execution especially valuable.

Sources: P101 State of Italian VC 2024 (IT, EU); KPMG Venture Pulse Q4 2025 (US)

– median values

After the early stage

If Series A is about proving that a startup can work, Series B and Series C are about scaling it and maintaining that position. By the time a company reaches Series B, the fundamental uncertainties around the problem, the product, and the customer have largely been resolved. Demand exists, value is delivered, and revenues are real. The dominant risk is no longer epistemic, but executional.

Series B capital is deployed to scale a model that already functions. The focus shifts from learning to expansion: strengthening go-to-market efforts, entering new geographies, and building structured teams and internal processes. Growth remains the primary objective, but it must occur within clear economic boundaries. Metrics such as retention, unit economics, and customer acquisition efficiency are no longer exploratory signals, but constraints that actively shape strategic decisions.

Series C represents a further shift in maturity. The company operates at meaningful scale, with established revenues and a defined market position. Capital is used to consolidate leadership, improve operational efficiency, and expand strategic options, including international expansion, acquisitions, or a credible path toward profitability. Across these stages, uncertainty progressively recedes and is replaced by a focus on disciplined execution and efficient capital allocation.

When a startup stops being a startup

Alex Wilhelm introduced the 50–100–500 rule, a commonly cited heuristic according to which a company can no longer be considered a startup once it exceeds $50 million in annual revenue, employs 100 or more people, and reaches a valuation of at least $500 million. While useful as a rough benchmark—particularly in the context of the U.S. market—the 50–100–500 rule captures scale rather than the underlying nature of the company’s risk.

What ultimately distinguishes a startup from a non-startup is not size, revenue, or headcount, but the type of uncertainty the organization is designed to handle. In the early stages, uncertainty is structural: the company is still discovering who the customer is, which problem truly matters, and why the solution creates value. In this context, error plays a productive role. Mistakes generate information, guide iteration, and reduce ambiguity.

As a company progresses through Series B and Series C, core assumptions around product–market fit and customer demand are largely validated, but uncertainty does not disappear. The dominant challenge becomes the difficulty of sustaining elevated expectations. In this phase, companies are no longer testing whether growth is possible, but whether it can be reproduced reliably.

As companies move beyond Series B and Series C, growth tends to become more predictable, supported by repeatable and scalable processes, while the company’s competitive position—often approaching market leadership—becomes increasingly well defined.

This shift in the nature of uncertainty directly affects how error is treated. Error becomes expensive and avoidable. What was once a mechanism for discovery turns into a liability that destroys value if left unmanaged.

This evolution is reflected in the company’s concrete actions. As the company matures, the focus shifts decisively toward optimization. Processes are standardized, strategic choices are narrowed, predictability is prioritized, and capital is allocated to initiatives with known and measurable returns. Experimentation does not disappear, but it becomes incremental, bounded, and tightly controlled.

The organizational structure evolves accordingly. In many cases, founders are gradually complemented or partially replaced by experienced managers, particularly in functions such as operations, finance, and sales.

In practice, this transition typically occurs several years after founding, this can take from three years up to ten years, depending on sector and market conditions, but most commonly coincides with late growth rounds, such as series D, pre-IPO rounds or directly with the IPO.

The organization has shifted from learning under uncertainty to executing at scale—and that transition, more than any numerical threshold, marks the moment a startup stops being a startup.

The moment in which an organization shifts from learning under uncertainty to executing at scale—more than any numerical threshold—marks the point at which a startup stops being a startup.

Conclusion

What has been described so far represents an idealized lifecycle of a startup: a clean progression from pre-seed to maturity, where uncertainty is progressively reduced and replaced by scalable execution. In reality, very few companies complete this journey. Most startups drop out long before reaching its later stages.

Empirical data highlights just how selective this path is. Only around 45–50 % of companies that raise a pre-seed round manage to reach a Seed round. Of those, only about 20–40 % go on to raise a Series A.

 Each transition acts as a filter, progressively eliminating companies that fail to resolve the specific form of uncertainty required at that stage.

For this reason, the startup lifecycle should not be read as a checklist or a guaranteed roadmap, but as a conceptual framework. It clarifies what kind of problems must be solved at each stage and why success becomes increasingly rare as companies advance.

Reaching the end of this path is exceptional not because startups are poorly run, but because building an organization that can repeatedly transform uncertainty into scalable execution is, by nature, extraordinarily difficult.

Author: Marco Carabelli