The creation myth is familiar: founders in a garage, a seed check and then (many years, and many rounds later) an IPO that serves as a liquidity event for insiders. In this story, the public markets are where the startup goes to retire.

But this is historically illiterate. went public in 1997, three years after founding, at a market cap of $438 million. It had $15.7 million in revenue. Nearly everything Amazon would become (the marketplace, , the logistics empire) was built after it became a public company.

The same is true of , and , the latter of which went public at under $1 billion and has since become the most valuable company on Earth.

In the 1990s, the median tech company went public when it was 4 to 7 years old. Public investors didn鈥檛 buy the tail-end of innovation 鈥� they funded the vast majority of it.

It remains true today. Just look at the 鈥淢agnificent 7.鈥�

Amazon vs. WeWork

When the dot-com bubble burst, Amazon鈥檚 stock collapsed to single digits. That crisis forced to restructure the cash conversion cycle, close distribution centers and lay off 15% of staff. Amazon posted its first profitable quarter in Q4 2001. The public market鈥檚 ruthlessness was the forge that hardened the business model.

Now compare this to what the 鈥淧rivate-for-Longer鈥� era has produced. By 2024, the median VC-backed company , a full decade later than many 1990s counterparts.

Shielded from quarterly accountability, short sellers and skeptical analysts, companies like accumulated $47 billion valuations while hiding behind metrics like 鈥淐ommunity Adjusted EBITDA.鈥� When WeWork finally tried to list, the market rejected it instantly. But by then, billions had been wasted. Private market opacity had delayed diagnosis until the rot was terminal.

Discipline creates strength

The data is damning across the board. The 2010鈥�2020 cohort of VC-backed IPOs generated a return relative to the S&P 500. In 2021, only 25% of IPOs were profitable.

went public at a 77% discount to its 2021 valuation. went bankrupt. The Renaissance IPO ETF fell over 50% from its peak.

Meanwhile, 鈥檚 research shows that of a company鈥檚 lifetime value creation now occurs in private markets, accessible only to institutional investors. The returns to innovation have been privatized while the risks have been socialized.

The central paradox is that more private funding has not produced more innovation, it has simply . Abundant capital allowed 鈥渂litzscaling,鈥� promoting growth over efficiency, far longer than the market would naturally tolerate. In the 1990s, a company could burn cash for three or four years before facing discipline. Today, it鈥檚 well over a decade and the result is companies that eventually go public with .

None of this means venture capital is unimportant. Early-stage risk absorption remains vital. But innovation is a lifecycle, and the lifecycle includes a public chapter that is not optional. What matters is the handoff, the transition from private incubation to public maturation, where ideas are tested, funded and held accountable by the broadest possible base of investors. The most consequential companies in technology history made that handoff early. The generation that delayed it has delivered the worst returns and the most spectacular failures.

If innovation is the goal, the handoff matters. And we have been fumbling it.

, a frequent guest author for 附近上门 News, is the research lead at , a platform that allows VCs and angel syndicates to raise and deploy capital globally.

Related reading:

• The Great 鈥楢I Bubble鈥� Debate
• Why Raising Too Much Funding Is Often Fatal
• Should VCs Invest In 鈥楯ockeys鈥� Or 鈥楬orses鈥�?

Illustration:

Bubbles emerge when a market becomes irrationally optimistic, causing the price of assets to diverge from their fundamental value. The critical moment is the formation of a recursive loop where price growth creates FOMO that pulls in more capital that further accelerates price growth. The end result is an increasingly unstable house of cards.

AI investment differs in two major ways from this historical pattern:

First, unlike previous bubbles (e.g. dotcom) there is significant revenue being generated by the underlying companies 鈥� implying real fundamental value. While questions have been raised about how much of this revenue is just , the growth is undeniable.

Second, these are private companies that aren鈥檛 easily accessed by investors, and the biggest names are selective about who gets on their cap table. Typically, bubbles play out in more liquid public markets, where sentiment can drive extreme swings in buy and sell activity.

Price vs. value

The final question: To what extent has price diverged from value?

For clarity, valuation is an opinion on the future, whereas pricing reflects the current fundraising market, so this is a mostly subjective issue. One investor might believe foundation models are the future of how we engage with technology, while another might just see them as an evolution of SaaS. Both are valid, and the speculative nature of VC means there should always be a range of perspectives.

However, venture capital is also influenced by herd behavior, with inflows being influenced by simple narratives about opportunity. that where investors concentrate on a particular sector, it tends to push up prices without implying better outcomes in future. That appears to align with venture capital activity in AI, as investors complain more frequently about the terms they鈥檙e faced with on competitive deals.

So, there is definitely some discomfort in the market. There is significant enthusiasm for AI, genuine optimism about the future, but also concern for how deals are playing out. Investors are gambling huge amounts of capital on the future of AI, too afraid to make an .

A risk bubble

The best way to consider might be in similar terms to 鈥檚 take on the , which is that it鈥檚 a risk bubble, rather than a valuation bubble:

鈥淎ll of this suggests that we are not in a valuation bubble, as the mainstream media seems to think. We are in a risk bubble. Companies are taking on huge burn rates to justify spending the capital they are raising in these enormous financings, putting their long-term viability in jeopardy. Late-stage investors, desperately afraid of missing out on acquiring shareholding positions in possible 鈥榰nicorn鈥� companies, have essentially abandoned their traditional risk analysis. Traditional early-stage investors, institutional public investors, and anyone with extra millions are rushing in to the high-stakes, late-stage game.鈥�

In conclusion, it doesn鈥檛 appear correct to characterise AI as a bubble in the traditional sense. Instead, venture capitalists have chosen huge systematic risk (undiversifiable reliance on AI), rather than the usual idiosyncratic risk (diversifiable across sectors) 鈥� which jeopardizes performance if the future doesn鈥檛 match up with today鈥檚 optimistic enthusiasm.

The bear case is probably not a total bust like 2000, but a mini-correction more similar to 2022. While portfolios won’t be wiped out, capital will end up locked away in overcapitalized private-market giants for longer than is comfortable for anyone involved, again.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation.

Related reading:

• Startups Are Art, Valuation Is Science, Price Is Both
• Are YC Valuations Really Too High?
• Venture Capital鈥檚 Second(ary) Chance
• How Do You Value Pre-Revenue Startups?
• Should VCs Invest In 鈥楯ockeys鈥� Or 鈥楬orses鈥�?

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On the surface, there鈥檚 status associated with the big platforms. Their deep pockets means they can pay over the odds, and bigger rounds at higher prices make for more impressive headlines. To founders, who are staring into a void of uncertainty, it might feel like winning the lottery.

Unfortunately, but unsurprisingly, it鈥檚 not quite that simple. Consider from veteran investor, :

“The fact is that the amount of money start-ups raise in their seed and Series A rounds is inversely correlated with success. Yes, I mean that. Less money raised leads to more success. That is the data I stare at all the time.”

Being on the boutique side of this argument, perhaps Wilson just doesn鈥檛 want to compete on price against the big platforms? As usual, data can point to the truth of this question, for those who care to look.

Premature scaling

In 2011, released a report on 鈥�.鈥� In its effort to understand the major drivers of startup failure, one factor kept coming up: Startups that raised large amounts of capital early would try to build their way to success without properly testing assumptions. By sinking money into untested product development or team expansion, they removed the optionality and flexibility that is vital to innovation.

This mirrors given to founders by co-founder :

鈥淭he more you raise, the more you spend, and spending a lot of money can be disastrous for an early stage startup. Spending a lot makes it harder to become profitable, and perhaps even worse, it makes you more rigid, because the main way to spend money is people, and the more people you have, the harder it is to change directions.鈥�

Today, as the bar for traction in subsequent rounds has risen so steeply, this is an even greater concern for founders. Coming to the conclusion that you need to pivot (which ) after you鈥檝e already burned millions of venture dollars may be too late, as the runway to hit the metrics for your next round grows shorter.

Re-risking

The more you spend, the more fragile your vision becomes. This is a process that venture capitalist has described as 鈥�.鈥�

鈥淎t each funding round, there is a significant re-risking of the startup, to the point that you are not moving meaningfully down the risk curve for a long long time. And even at a late stage, a mega funding round can bring you right back up to the point of maximum risk.鈥�

The lingering question is why the big platform firms would pursue a strategy that amplifies failure. The key is to understand their priority: , at almost any cost.

This strategy justifies putting options on as many startups as possible in the , and then winnowing that down to a handful of winners. If you crank the 鈥減ower law鈥� of venture capital up to 11 by injecting ever-larger sums of capital, owning these monstrous outcomes is worth hundreds of small failures. This strategy, while zero-sum in effect, is effective as long as enough of the remaining value accrues to these winners.

So, be careful about the motivation attached to any capital you may raise. Some investors are out there to support you through a journey of exploration, while others just want to pump you with rocket fuel and see what happens.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation.

Related reading:

• Are YC Valuations Really Too High?
• Venture Capital鈥檚 Second(ary) Chance
• How Do You Value Pre-Revenue Startups?
• Should VCs Invest In 鈥楯ockeys鈥� Or 鈥楬orses鈥�?

Illustration:

This debate goes back to the foundation of venture investing, where three distinct schools of thought emerged from the early pioneers.

of championed technology as the primary driver of success. of believed markets were paramount, and Arthur Rock of Davis & Rock put his faith in people above all else.

Today, the 鈥減eople-first鈥� approach has become overwhelmingly dominant, with rating team as their most important consideration, compared to just 10% for business model, and 6% for market opportunity.

This modern consensus around 鈥渇ounder-first鈥� investing might seem logical on the surface. Much of the success in venture capital is attributed to generational founders like or . Thus, the mantra 鈥渧enture is a game of jockeys, not horses鈥� has become gospel in Silicon Valley, with firms eagerly courting the most charismatic and well-pedigreed founders.

However, research suggests this convention is flawed. One study tracking startups from inception to IPO revealed that business quality is of success than founding team characteristics. Additionally, analysis of investment outcomes shows that VCs consistently make 鈥溾�� by over-indexing on founder attributes.

Superficial pattern-matching

It would appear that many VCs interpret founder first as pattern matching on superficial attributes, such as education, previous employment or demographic characteristics. They mistake correlation for causation, seeking founders who look like previous winners rather than optimizing their strategy to find outliers.

The best investors take a radically different approach. As has : 鈥淚 don’t separate the ideas and the business strategy and the technology that much from the people. It’s all some sort of a complicated package deal.鈥�

This explains why two seemingly contradictory statements can both be true: VCs make poor decisions by focusing too heavily on founder attributes, yet top-tier firms like and are right to prioritize founders above all else.

Expanding the aperture

Indeed, great investors evaluate founders through the lens of their business. As of : 鈥淵ou look at the product, and you try to learn about the humans behind the product by evaluating the product.鈥� This approach reveals the potential of an opportunity with greater depth than any pattern-matching exercise.

Y Combinator famously (and successfully) makes investment decisions with a 10-minute interview. Speaking at a summit in 2016, (then president of Y Combinator) 鈥渃larity of vision鈥� and 鈥渢he non-obvious brilliance of the idea鈥� as important elements in that process, in addition to personal characteristics like determination and communication skills.

This approach expands the aperture for early investors, creating more data to drive better decisions. Most importantly, it grounds investment decisions in observable reality rather than , reducing an investor’s susceptibility to the cognitive biases that plague venture capital and drive predictably bad decisions.

The venture industry’s founder-centricity isn’t wrong, founders do matter enormously. But the current understanding, with its emphasis on pattern-matching and demographic characteristics, misses the point entirely. Investors must recognize that great founders and great businesses are inseparable, two sides of the same coin.

In the end, the most successful VCs don’t look for jockeys or horses. They find centaurs.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation.

Related reading:

• Are YC Valuations Really Too High?
• Venture Capital鈥檚 Second(ary) Chance
• How Do You Value Pre-Revenue Startups?

Photo by聽听辞苍听

The assumption has been that investments are held until exit, with returns heavily weighted toward IPO or strategic acquisitions. For their patience, LPs would be rewarded with performance surpassing public-market equivalents.

In recent years, this model has been challenged by the sheer volume of private capital 鈥� with liquidity horizons stretching further into the distance. As companies absorbed investment at an unprecedented scale, the 鈥渆xit math鈥� became a challenge.

Acquisitions often leave early investors, founders and employees with little in the way of payoff, as preference stacks swell in growth rounds, giving later investors a guaranteed return on their capital.

IPOs often leave growth investors with little profit, as the conversion to common stock wipes out preference stacks on a listing that frequently has to be priced below the last round valuation.

Essentially, the extended life in private markets means a greater divergence of 鈥減rice鈥� and 鈥渧alue,鈥� due to limited price discovery and competing incentives of increasing ownership versus accelerating markups. Funding rounds are priced with spurious metrics in order to justify investment terms that look good on paper. All of this comes to a head when the company seeks an exit, which is why IPOs and M&A have been slow in recent years.

VCs鈥� existential exit problem

The lack of a healthy exit market is an existential problem for the venture capital industry. Fortunately, forward-thinking GPs have recognized secondary markets as a solution to a number of these problems, and the strategy is beginning to evolve.

First, with companies staying private for so much longer, it鈥檚 a challenge to the rate of return for early investors. As investor has described, .

A healthy secondary market means seed investors can distribute returns at a strategic moment, rather than white-knuckling until the ultimate outcome. Focusing more on Internal Rate of Return rather than total value to paid-in, or , capital drives outcomes in the right direction.

Second, as companies raise more capital, there is increasing pressure with dilution, maintaining pro-rata, board seats and the simple relevance that an early investor has to the management team. As portfolio companies mature, GPs end up holding investments they no longer manage in a meaningful sense, and it would be better to get them (at least partially) off their books. The alternative is leaving the fate of the fund to downstream investors.

These points are particularly pressing in a world with more multistage venture giants, who deploy capital aggressively into perceived winners 鈥� resulting in the 鈥溾�� of target companies. These monster rounds are highly dilutive, and often provide a good opportunity for everyone involved if an early investor chooses to take some liquidity.

Let鈥檚 flip the default strategy

Indeed, to challenge the traditional wisdom on 鈥渉olding to maturity,鈥� we can look at the TVPI trajectories for venture funds. According to from , the value growth in the value of a typical portfolio slows significantly in year eight of a fund鈥檚 life. This means, in theory, years five to seven of an investment (assuming a three-year deployment period) could be good timing to consider selling part of a portfolio stake 鈥� depending on how the risk:reward ratio of that company has evolved.

Crucially, and as always, venture capital is all about outliers. Each portfolio, and each investment, will require individual consideration. The main lesson here: Early-stage GPs should probably move from 鈥渉olding by default, with opportunistic secondaries鈥� to 鈥渟econdaries by default, with opportunistic holding鈥� in order to act as responsible fiduciaries and ensure healthier returns for their LPs.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation.

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It鈥檚 a funny question, because it assumes the premise that startups are generally valued based on past revenue. This couldn鈥檛 be further from the truth.

To quote the legendary , general partner at : 鈥淧eople act like [valuation is] an award for past behavior. It鈥檚 not. It鈥檚 a hurdle for future behavior.鈥�

All valuation is forward-looking. In the purest sense, it鈥檚 the discounted value of all future cash flows. In a more rough-and-ready venture capital sense, what you care about is exit potential. This is usually understood through revenue growth assumptions, or EBITDA projections in more rational markets.

The roadmap of future potential

So, how do you value a pre-revenue startup? The same way you value any startup, at any stage: by mapping the future.

This can be conceived as a three-step process:

• Ambition to strategy: Converting the ambition of the company into a rational strategy for growth and expansion, to help you understand the likely rate of growth and what investment will be required.
• Strategy to financials: Quantifying that strategy into revenue and the associated operational expenses (the cost of running the business) and cost of goods sold (the variable costs associated with each sale).
• Financials to ambition: Completing the cycle by comparing that projected financial future to the ambition you set out to achieve. Does it seem realistic, is it achievable, do you need to modify your pitch?

To be clear, what this delivers is coherence, not certainty. Startups are inherently risky, and any pitch is asking investors to suspend their disbelief and explore the realm of the possible with the founders.

To quote another brilliant investor, of , the question that early-stage VCs should ask themselves is: 鈥淲hat happens if everything goes right?鈥�

A series of assumptions

The journey of any startup is a series of , as it increments through key assumptions.

Among the first of those proof points is revenue, demonstrating the ability to attract paying customers. After that, you see how easily (and cheaply) that revenue can be scaled. Then, you might start looking at customer retention data, whether product expansion keeps creating value, how competition shapes margins. Step by step, the faith that you put in the founding team early on is displaced by hard data on performance.

While a pre-revenue startup may feel especially high-risk, it鈥檚 actually just one more assumption on a stack of others. Raising at this point usually means the product is particularly expensive to build 鈥� they鈥檙e not able to bootstrap the launch.

In this case, both the founders and prospective investors can look at other ways to address that assumption through speaking to potential customers, technical due diligence and market research.

This dynamic is what defines greatness. The best founders can build conviction among investors, allowing them to raise at a higher price.

On the other hand, the best investors aim to build conviction before the founders have proof, in order to invest at a lower price. While this is true at every stage of growth, pre-revenue investing addresses the largest pool of assumptions, and so a wider net must be cast on qualitative and quantitative inputs in the decision-making process.

In conclusion, like any investment, pre-revenue startups require that investors face the future with a , willing to embrace uncertainty. If there wasn鈥檛 any uncertainty there would be no venture capital. If you鈥檙e looking for the comfort of 鈥渒nowables鈥� like revenue, you might be in the wrong place.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation.

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This contradiction creates problems on both sides of the table.

Founders are sucked into cherry-picking market data to justify the terms they want to get, while investors manipulate comparables to suit the price they want to pay. It鈥檚 not a meaningful negotiation, and founders usually end up losing out.

On the other hand, investors end up building fragility into their portfolio by pricing to the market. It looks great when everything is 鈥渦p and to the right,鈥� but any downturn quickly becomes systemic and sweeps the whole industry.

We saw a consequence of the latter in 2022, when GPs were criticized by their own investors for not properly marking the value of their portfolios after the crash. With the market frozen and prices at rock bottom, the comparable approach was suddenly deeply undesirable.

Founders suffered from this as well. Anyone raising in the latter part of 2022 was faced with significantly more demanding terms from investors than companies that raised mere months prior. Cost of capital was the excuse, but truthfully, investors were in panic mode.

Older and wiser investors have been of this approach to pricing for a long time. Indeed, it seems you need to have seen at least one full market cycle to see how it plays out beyond the appealing short-term markups. The more cycles you study, the more vivid the case for greater financial literacy in venture capital becomes. Investors shouldn鈥檛 need to learn the hard way every time.

The answer(s) to that opening dilemma?

The best startups aren鈥檛 just priced, they are valued

Consider the extreme examples of , or . None of those companies were slapped with a market-based multiple. Each offered a novel concept, around which investors had to build independent conviction.

These companies had to be examined in a vacuum. There was no space startup industry to contextualize SpaceX. Taxis had existed for a long time, but there was nothing quite like Uber. AI was an existing field of study, but beat others to commercializing LLMs by several years.

In these cases, the only way for founders and investors to align on terms is to first align on a shared vision of the future; to understand the growth potential, the moats and margins, and the ambition at exit. Essentially, the drivers of value 鈥� as measured in a valuation.

The best investors do not rely on market consensus

If you鈥檙e in the venture business, which type of companies are you looking for? Do you want option calls on a dozen generic SaaS businesses in the hope that one of them works out? Or are you hunting for a masterpiece?

If you鈥檙e in the business of finding greatness rather than following the herd, you need to be able to see value where others cannot. If you rely on comparables to fuel a market-based pricing model, by definition you can only invest in consensus themes. Consensus themes, it turns out, have a habit of torching venture capital and .

It can be tempting to use market-based pricing as a crutch, especially in boom times when it would justify faster deals and larger markups.

Just remember, startups are illiquid for about a decade and the market can slam shut within a year. It鈥檚 easy to find yourself with a portfolio of companies whose price may be slashed to a fraction of what you paid.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation.

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Today, the divergence of fee-driven asset managers and performance-driven venture capitalists is clearer, but there are many lessons to unlearn.

Founders are also having to ditch a lot of the 鈥渃ommon wisdom鈥� from that period. It was common to hear greener GPs sneer at the utility of financial projections, or repeat trite soundbites like 鈥渙nly market passing prices matter.鈥�

These talking points were unfortunate symptoms of venture capital鈥檚 decline, not useful input for founders. Indeed, founders need projections to determine fundraising strategy, and 鈥渕arket passing鈥� prices only make sense in markets with real price discovery.

Where鈥檚 the value?

Valuation is an obvious problem to highlight as a consequence of this environment. We鈥檝e seen in recent years how an over reliance on market-based pricing helped create the bubble that burst in 2022 while also failing entrepreneurs with truly novel solutions. It is more important than ever for venture capitalists to develop an independent lens on value in order to recognize opportunities that offer more upside than consensus themes.

Beneath this is a simpler question of financial literacy. Specifically, the ability to coherently connect stories and numbers 鈥� the narrative exposition in a pitch, and the financials that translate economic energy. This is where 鈥渧alue鈥� lies, not in crude comparisons to roughly similar startups.

• Is there a sustainable competitive advantage, and how does that influence margins?
• How quickly can acquisition be scaled, and what is that likely to cost?
• What will drive customer loyalty, and what is the anticipated churn?

This basic probing of assumptions is useful to understand any pitch, and worth reflecting on from both sides of the table.

At a fundamental level, the value of any cash-generating asset is the discounted value of all future cash flow. This doesn鈥檛 require an MBA and you don鈥檛 need to build a complex model in Excel. It鈥檚 a framework to think about valuation 鈥� a perspective on the scenario that lets you peer into the future.

Is there inherent uncertainty and the potential for unforeseen problems? Of course, but you might as well rule out the foreseeable problems first.

鈥淭he value of an asset that produces cash is the present value of the cash flows it generates over its life. Few investors explicitly use a DCF model all the time, but it is useful to keep the drivers of the model in mind constantly.鈥� 鈥斅燤ichael J. Mauboussin and Dan Callahan in 鈥溾��

Today, there are hundreds of private unicorns that can鈥檛 land a good exit because they spent their lives being marked up with revenue multiples that offer no insight on financial health. It鈥檚 an issue that has been discussed for , but there was little incentive to correct that as long as the money kept rolling in.

Indeed, now we know what happens when the money stops: everyone suffers. You can鈥檛 fuel growth with negative unit economics, you can鈥檛 prop up otherwise unworkable businesses, and the buyers at the end of the chain (public markets and corporate acquirers) can always spend elsewhere.

For a while it seemed like the job of venture capital was to raise funds and accelerate fee income. Many investors needed reminding of their fiduciary duty: delivering maximal returns to LPs. That speaks to a need for professionalization, encouraging some basic standards in finance and economic theory across the industry.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation, and a venture partner at.

Related reading:

• Poor Valuation Practices Have Slowed Innovation
• How Venture Capital (Ab)Uses Revenue Multiples
• The 附近上门 附近上门

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As a consequence, it鈥檚 difficult to build a curriculum around these examples. Strategies that worked in one market, for one business model, may never again be repeated to the same effect. Even the most generic sound bites like 鈥渟hip fast and iterate鈥� or 鈥渄o things that don鈥檛 scale鈥� aren鈥檛 always applicable elsewhere.

This illustrates one of the factors that defines great founders: The confidence to carve their own path from first principles rather than going with the flow. They have the conviction to do their own thing and to see it through to the end 鈥� however, challenging it may be to swim against the current. The best advisers and mentors nurture this eccentricity rather than pressing founders into a mold.

This idiosyncratic nature creates a 鈥渓owest common denominator鈥� problem in the advice offered to founders: The most easily understood principles tend to be most valued 鈥� regardless of impact. This is also referred to as 鈥渟implicity bias,鈥� or the 鈥渋llusory truth effect.鈥� Recycling factoids allows the presenter to project authority without the risk of being 鈥渨rong,鈥� and it doesn鈥檛 hurt that fortune cookie wisdom is catnip on . Whether the outcome actually helps or harms founders is a significantly more complicated question.

An easy example is to look at how startup discourse leans toward averages as a source of false confidence. The average round size, average revenue for each stage, average dilution 鈥� all framed as optimal by advisers that do not understand that the 鈥渁verage鈥� startup fails. Exceptions rule. Market data should be carefully presented as a way to provide context on a proposition, not to shape it. Many of the most well-known startups were outliers from day one.

The problem is worse with complex topics. Founders are frequently directed to simple heuristics, like valuation as an outcome of target raise divided by typical dilution. This ends up punishing those who raise less, or pushing them to raise more. Or they鈥檙e told 鈥渞eal investors don鈥檛 care about financials,鈥� and are blindsided when real investors do indeed care about financials. Misunderstandings like this are a result of the fact that simple messages travel further.

Making an impression

The best way to make sure a pitch is ignored by investors is for it to be undifferentiated or incoherent; a generic template with the same tired jargon, a strategy slide showing unsurprising ambition, a standard raise at 鈥渕arket鈥� terms. Fundraising is not a box-checking exercise. The goal is not to meet expectations, but to cut through the noise and make an impression.

Why raise a typically sized seed round if the pitch implies huge capital expenditure? Why imply a normal range of dilution if the company only needs a small amount of capital today? Why target a typical range of valuation if you can make a clear and rational case for it to be higher? The most compelling pitches are designed with the goal of highlighting strengths, not hiding flaws.

A whole industry has formed to exploit the uncertainty that accompanies entrepreneurship. Whether it鈥檚 accelerators, advisers or consultants, many claim to offer a 鈥渞ecipe for success鈥� for founders who are desperate enough to pay for it. Nine times out of 10, founders would be better off spending that time talking to potential customers.

Where founders can get input from genuine experts that understand the above, whose experience is shared as 鈥渇ood for thought鈥� rather than a dietary restriction, can be a great benefit.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation, and a venture partner at.

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At one point in time, this understanding was the 鈥渕oat鈥� for venture capital firms. Those that could see value where others couldn鈥檛 were able to make decisions that resulted in outsized returns. Consider the investors who recognized the potential for , , or .

Over the past decade there has been such an incredible boom in software investment that it has completely distorted how venture capital operates and understands the world. Everything is now looked at through the lens of SaaS-style ARR multiples and growth rates. Many of today鈥檚 venture capitalists have not been around long enough to remember another environment.

This has recalibrated the whole asset class toward massive scalability and away from fundamentally innovative ideas like the semiconductor revolution that formed Silicon Valley.

The SaaS 鈥楤oom Loop鈥�

When your income as a VC relies primarily on management fees, is to raise and deploy capital as quickly as possible, and to maximize funds raised and fee income. In the past, this was throttled by businesses that could only put a certain amount of money to work in a practical manner. Acquisition took time and scaling took effort.

SaaS blew this ceiling out with a model where capital could be fed into growth through online advertising for platforms that were essentially infinitely scalable. The more money you put in, the faster the company grows, the better the investment looks, and the easier it is to raise another fund. It created a 鈥渧irtuous loop鈥� for VCs that enabled the growth of the mega funds we鈥檙e familiar with today.

The enthusiasm for this incredible growth, and the wealth it created, started to reshape practices. Perhaps the worst example of this is how crude ARR multiples became the default valuation method as SaaS solutions were effectively commoditized. The priority was closing deals quickly, not optimizing the price, so discipline fell by the wayside. This would fundamentally change how VCs understood value.

Unintended consequences

Not only did the public markets not care for this era of investing (post-IPO investors care more about EBITDA or cash flow than ARR), but it also made life much more difficult for deep tech startups. Consider these two points:

• The goal of a VC is to make investments that triple or quadruple in value in two to three years.
• VCs understand increases in value primarily through the lens of ARR multiples.

So how does a company that spends years just in R&D or building the product look in this environment? They can鈥檛 demonstrate progress in revenue, so they don鈥檛 obviously increase in value in the short-term. This makes them unattractive for venture investors, despite the fact they tend to have much stronger and sustainable competitive moats and marginality, leading to a smoother path to exit.

All of this is a clear reflection of how venture capital has gone from 鈥減atient capital鈥� creating lasting value, to a short-term trader mentality of riding volatility.

Fixing either one of these factors would help to put VC back on the right path, but changing the fundamental management fee incentive is going to take . Restoring financial discipline to VC, and taking a more sophisticated approach to valuation, may be a more accessible short-term solution.

, a frequent guest author for 附近上门 News, is the head of insights at , a platform for startup valuation, and a venture partner at.

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