How Investors decide What a Startup is Worth

Valuing an established business is difficult enough. Valuing a startup is something else entirely. The standard tools of business valuation, the EBITDA multiple, the discounted cash flow, the comparable transactions database, rest on a foundation of historical earnings, proven markets, and predictable cash flows. A startup, almost by definition, has none of these things in useful form. What it has instead is a team, an idea, perhaps a prototype, perhaps a handful of customers, and a pitch about what the future might hold.

And yet investors value startups every day, transacting at prices that both parties agree to with enough conviction to write and receive cheques. The number that results from these negotiations is not arbitrary. It is reached through methods that are well-established in the venture capital and angel investing world, methods that have been refined over decades of funding companies that go on to fail, to muddle along, and occasionally to become the most valuable businesses in the world.

Understanding those methods matters whether you are a founder trying to set expectations before a fundraise, an investor building a framework for evaluating opportunities, or a curious observer of how the business of new companies actually works. The maths is not complicated. The judgment embedded in it is everything.

Why Startups Are Different

The standard valuation problem is to estimate what a business is worth based on what it earns. The startup valuation problem is to estimate what a company is worth based on what it might earn, in a market that may or may not exist at scale, with a product that may or may not work as hoped, led by a team that may or may not be capable of executing at the speed the opportunity requires.

This means that startup valuation is less a financial exercise and more a structured negotiation between a founder’s belief in their company’s potential and an investor’s judgment about the realistic range of outcomes and the returns they need to justify the risk they are taking.

It also means that the methods used change as a startup matures. A company with no revenue requires different tools than one with a million dollars in annual recurring revenue. A company at Series B, with proven economics and a clear growth trajectory, can be valued using many of the same frameworks used for established private companies. The methods described in this article are roughly ordered by the stage at which they become most applicable.

The Berkus Method: Putting Numbers on Milestones

Dave Berkus is an angel investor and venture capitalist who, decades ago, grew frustrated with the impossibility of valuing pre-revenue companies using financial analysis. He developed a simple alternative: instead of trying to project future revenues, assign a dollar value to each meaningful milestone a startup has achieved. The presence or absence of each milestone adjusts the valuation up or down.

The original Berkus method identified five factors:

A sound and differentiated idea, which signals that the basic innovation is present and reduces execution risk. A working prototype or minimum viable product, which proves the technical concept can be built. A quality management team, which reduces the most significant risk in early-stage investing: the people risk. Strategic relationships, whether with early customers, distribution partners, or industry validators, which provide early evidence of market traction. And product rollout or early sales, which represents the most concrete form of de-risking: actual revenue, however small.

In the original formulation, each factor contributes up to half a million US dollars to the valuation, capping the maximum at 2.5 million dollars for a company that has achieved all five milestones fully. The figures have been updated over time to reflect higher market conditions, and contemporary applications of the method often apply larger caps in line with current seed and pre-seed valuations, but the underlying logic remains intact.

The Berkus method is frankly a blunt instrument. It produces a single range rather than a precise figure, and it says nothing about the size of the market the company is entering or the scale of the potential outcome. But that is also its virtue: it forces both founders and investors to think explicitly about what has actually been accomplished rather than what is being promised, and it avoids the trap of treating an optimistic revenue projection as evidence of value.

The Scorecard Method: Comparing to What the Market is Paying

The Scorecard method, also known as the Bill Payne method after the angel investor who developed it, takes a market-based approach to pre-revenue valuation. Rather than assigning absolute values to milestones, it asks: what are comparable startups in the same region and industry being valued at, and how does this company compare to that benchmark?

The process starts with identifying the median pre-money valuation for recent seed or angel investments in the same sector and geography. That median becomes the baseline. The startup is then assessed against a set of weighted factors, and the baseline is adjusted up or down depending on how the startup compares to the median company in the benchmark set.

The factors and their typical weightings are: the strength of the management team, which carries roughly 30 percent of the total weight; the size of the opportunity, which carries around 25 percent; the strength of the product or technology, weighted at about 15 percent; the competitive environment, at 10 percent; marketing, sales channels, and partnerships, at another 10 percent; and additional factors including the need for additional investment, the quality of board and advisers, and other considerations, sharing the remaining 10 percent.

If the benchmark median valuation is 3 million dollars and a startup scores above the median on team strength (its most important factor), in line on market size, and somewhat below on product maturity, the Scorecard method might produce a valuation of 3.2 to 3.5 million dollars. The discipline the method imposes is the discipline of comparison: it prevents founders from inflating projections and investors from undervaluing exceptional teams by grounding the conversation in what the market is actually paying for companies at this stage.

The Risk Factor Summation Method: Cataloguing What Could Go Wrong

Closely related to the Scorecard method, the Risk Factor Summation approach begins with the same market-baseline logic but adjusts the valuation by systematically examining each of the major risks a startup faces.

The method identifies twelve risk categories: management risk, stage of the business, legislation and political risk, manufacturing risk, sales and marketing risk, funding and capital raising risk, competition risk, technology risk, litigation risk, international risk, reputation risk, and potential lucrative exit risk (that is, the risk that the exit opportunity may not materialise). For each category, the analyst assesses whether the startup faces more or less risk than a typical early-stage company in its sector, and the valuation is adjusted up or down by a fixed amount, commonly 250,000 to 500,000 dollars, per factor.

A startup with an unusually strong management team and a proprietary technology position might receive upward adjustments on those risk dimensions. A startup in a highly regulated industry entering a large number of new markets might receive downward adjustments on legislative and international risk. The net result of all adjustments, applied to the baseline valuation, produces the final figure.

The Risk Factor Summation method is most useful as a checklist device. By forcing an explicit assessment of each risk dimension, it prevents the common error of valuing a company based on its strengths while ignoring the risks that could prevent those strengths from translating into a successful outcome.

The Venture Capital Method: Working Backward from Exit

The VC method, developed by Professor Bill Sahlman at Harvard Business School in the 1980s and still widely used in the venture capital industry, inverts the usual valuation logic. Rather than estimating what the company is worth today and projecting forward, it starts with what the company might be worth at exit and works backward to determine what an investor should pay today to achieve the return they require.

The mechanics are as follows. The investor estimates the terminal value of the company at exit, typically in five to ten years. This estimate is usually based on projected revenue or earnings at the exit date, multiplied by an appropriate multiple for that type of business. The investor then applies their required return, which for venture capital is typically very high, often 10 to 30 times the invested capital, because the vast majority of early-stage investments fail to return capital at all and the winners must more than compensate for the losers.

Dividing the terminal value by the required return multiple produces the post-money valuation, the value of the company immediately after the investment is made. Subtracting the investment amount from the post-money valuation gives the pre-money valuation, which is the value of the company the investor is willing to attribute before their money goes in.

A worked example: an investor believes a startup could be acquired for 100 million dollars in seven years. The investor requires a 20x return on their 2 million dollar investment. The post-money valuation is 100 million divided by 20, which is 5 million dollars. The pre-money valuation is 5 million minus 2 million, which is 3 million dollars. The investor will invest 2 million dollars for 40 percent of the company (2 million divided by 5 million).

The VC method is explicit about the logic of venture investing in a way that other methods are not. It makes the required return, the exit scenario, and the dilution all visible in a single framework. Its limitation is that all of the important numbers, the exit value, the timeline, the required multiple, are assumptions, and the sensitivity of the result to those assumptions is extreme. A 50 million dollar exit assumption produces a very different pre-money valuation than a 150 million dollar one.

Revenue Multiples: Once the Numbers Start

For startups that have moved beyond pre-revenue status and are generating meaningful recurring revenue, the conversation shifts from qualitative milestones to quantitative metrics. At this stage, the most common approach is to apply a revenue multiple: a multiplier applied to annual recurring revenue that reflects what comparable companies in the same sector are trading at.

A software-as-a-service company with 2 million dollars in annual recurring revenue and a market-implied multiple of 10 times revenue would be valued at 20 million dollars. The multiple itself reflects several factors: the growth rate of the revenue (higher growth commands higher multiples), the gross margin of the business (higher margins command higher multiples), the predictability of the revenue (more predictable revenue commands higher multiples), and the overall market conditions and investor appetite for the sector.

Revenue multiples vary widely. SaaS businesses with strong growth rates and high gross margins have historically commanded multiples between 8 and 15 times revenue, and sometimes higher during periods of intense investor enthusiasm. More cyclical businesses, or those with lower margins or less predictable revenue, attract lower multiples.

The shift to revenue-based valuation is significant because it introduces an anchor in observable reality. A startup with 2 million dollars in annual recurring revenue has demonstrated that customers exist and are willing to pay. The multiple applied to that revenue is still a judgment, but it is applied to a real number rather than a projection.

What the Market Is Actually Paying: 2026 Benchmarks

Independent of which method an investor uses, the outputs of those methods are constrained by what the market is actually paying for comparable companies at comparable stages. Benchmarks from current market data provide the calibration.

At the seed stage, Carta data from 2026 shows a median post-money valuation of approximately 24 million dollars, with a pre-money valuation around 16 million dollars and a median round size of roughly 3 million dollars. Founders at the seed stage typically surrender around 20 percent of their equity. These figures represent the median: half of seed-stage companies raise at higher valuations and half at lower.

At Series A for non-AI companies, median pre-money valuations in early 2026 sit at approximately 42 million dollars, with round sizes around 20 million dollars. The time between a seed round and Series A has stretched significantly, averaging more than 600 days, and Series A investors increasingly expect to see between 1 and 2 million dollars in annual recurring revenue before committing.

The single most consequential variable in the current market is whether a startup is building with artificial intelligence as a core component. AI companies at seed stage command valuations roughly 30 percent higher than non-AI peers. At Series A, the premium stretches to 84 percent. At Series B, AI companies are raising at multiples of what comparable non-AI companies achieve. This premium reflects both genuine excitement about the potential of AI-native businesses and significant competition among investors for the most promising opportunities in the space.

Geography creates a second tier of variation. Companies in established venture hubs, primarily the Bay Area, New York, and London, command valuations 30 to 40 percent higher than equivalent companies in other markets, reflecting the concentration of investor relationships, talent, and deal flow in those centres.

The Factors Investors Actually Weight

Behind every method and every benchmark are the factors that determine whether a specific company is valued at the top, the middle, or the bottom of the range. Understanding these factors is more useful than understanding the maths, because the factors are what founders can actually influence.

The team is the most important single factor at the earliest stages, and it remains important throughout a company’s life. Investors at the seed stage are often funding the people as much as the idea, because the idea will change many times before it succeeds or fails, and what determines whether those changes lead anywhere good is the quality of the people making them. Prior founder experience, domain expertise, technical depth, and complementary skills between co-founders all contribute to a team assessment. A founder who has successfully built and exited a company before commands a meaningful premium over a first-time founder, simply because the track record reduces uncertainty.

The size of the market matters because venture capital economics require large outcomes. A fund that deploys 100 million dollars and targets 3x returns needs to generate 300 million dollars from its portfolio. Given that most investments will fail, the winners must return very large multiples of capital. A startup addressing a 10 billion dollar market has the potential to deliver that kind of return; one addressing a 50 million dollar market does not, however well it executes. Investors assess total addressable market with appropriate scepticism, because founders have strong incentives to define markets broadly, but genuine large market opportunity is non-negotiable in a venture context.

Product and technology differentiation determine how defensible the startup’s position will be if the market develops as hoped. A product protected by patents, built on proprietary data, reinforced by network effects (where each new user makes the product more valuable to existing users), or characterised by high switching costs is more valuable than one that a well-funded competitor could replicate in eighteen months.

Traction, broadly defined as evidence that the market wants what the startup is building, is the most powerful de-risking factor available to early-stage companies. This can take many forms: revenue, active users, signed letters of intent, completion rates on free products, or qualitative evidence of strong customer demand. Each piece of traction converts a projection into evidence, and evidence is worth more than projection at every stage of fundraising.

Financial metrics become increasingly important as a company matures. Burn rate (how quickly the company is spending its cash), runway (how many months of cash remain at the current burn rate), customer acquisition cost, lifetime value of a customer, and gross margin all shape how investors assess the health and efficiency of the business model. A company growing at 15 percent month-over-month with high margins and efficient customer acquisition is valued very differently from one growing at the same rate but burning capital at an unsustainable pace to achieve it.

The Negotiation Behind the Number

All of the above describes how investors think about startup value. It does not describe how startup valuations are actually set, which is through negotiation, and the most important variable in that negotiation is often how much competition exists for the round.

A startup that is running a competitive fundraising process, with multiple investors interested and a credible timeline, has leverage that no valuation method can fully capture. Conversely, a startup that needs capital urgently and has limited investor interest is in a weak negotiating position regardless of what the Scorecard method suggests its fair value is.

Founders who understand this dynamic approach fundraising with the same strategic intentionality they would apply to a sales process: building relationships before capital is needed, creating genuine competition for the round, and setting realistic timelines that allow for the 616-day average journey from seed to Series A that current market data describes.

The valuation that results from a well-run fundraise is not simply the output of a model. It is the price at which a willing investor and a willing founder agreed to transact, shaped by methods and benchmarks but ultimately determined by conviction, competition, and the particular moment in which the deal is struck.

A Valuation Is a Contract, Not a Compliment

A point that many founders miss, particularly those who achieve high valuations on their first round, is that a high valuation is not purely good news. It is a commitment.

The right valuation is not the highest valuation a founder can extract from the market in a given moment. It is a valuation that reflects genuine progress made, sets realistic expectations for the next milestone, and leaves room for the company to grow into its price without requiring a heroic outcome to justify it.

That discipline, taking less money at a lower valuation in order to preserve future optionality, runs against the incentive to maximise the cheque. But the founders and investors who take the long view understand that a startup valuation is not a destination. It is the starting price for the next chapter of a story whose ending has not yet been written.