The Library

Gut Decisions and Missing Spreadsheets

Gompers, Gornall, Kaplan, and Strebulaev, “How Do Venture Capitalists Make Decisions?” Journal of Financial Economics, 2020, vol. 135, no. 1, pp. 169-190.

Founders are told constantly to come back when they have metrics. It is worth knowing how the people saying it actually decide.

When researchers surveyed close to nine hundred venture capitalists about their own process, the first surprise was where the work happens. Of the three things a venture firm does, seeing deals, choosing among them, and helping afterward, deal selection was rated the most important by 49 percent of respondents. The choosing is the job.

The second surprise is what the choosing runs on. Nine percent of the VCs reported using no financial metrics at all when evaluating an investment. Among early stage investors that figure rises to 17 percent. Nearly half acknowledged that they often make gut investment decisions, a tendency concentrated among early stage, IT, and smaller firms.

Roughly one in six early stage investors uses no financial metrics whatsoever.

If you are pre-revenue and have been made to feel unserious for it, the evidence says otherwise. A large share of early stage investors are not evaluating you on a spreadsheet, because at your stage there is nothing in the spreadsheet worth reading. They are evaluating judgment, market, and team.

That is not permission to be vague. It means your effort belongs somewhere other than fabricating five year projections that everyone in the room knows are fiction. It belongs in being unmistakably clear about who you are, what you have seen that others have not, and why this market is large enough to matter.

It also explains why the fit between your company and the specific firm you approach carries so much weight. When selection is judgment driven, whose judgment you are in front of determines the outcome.

Valuation Is a Term, Not a Number

Gornall and Strebulaev, “Squaring Venture Capital Valuations with Reality,” Journal of Financial Economics, 2020, vol. 135, no. 1, pp. 120-143.

A headline valuation is the most quoted number in venture capital and among the least meaningful, because it is not a price. It is the output of a contract.

Two researchers rebuilt the capital structures of 135 US unicorns from their certificates of incorporation and priced each class of shares properly. Post money valuations overstated fair value in every single one of the 135 companies, ranging from 5 percent to roughly 250 percent.

Square is the clean illustration. Its Series E implied a valuation of about $6 billion. Modeled honestly, the fair value was about $2.2 billion. The headline number overstated the company by 171 percent.

The gap is created by terms riding along with the price: liquidation preferences, seniority stacking, participation, and IPO ratchets. In 66 of the 135 companies, new investors sat senior to some existing preferred holders. In 43, they sat senior to all of them.

Every one of the 135 companies studied was worth less than its headline number said.

When you receive two offers, the higher valuation is not automatically the better deal, and sometimes it is the worse one. A generous number attached to a 2x participating preference can leave you with less in the outcomes that are most likely to happen.

The practical move is to stop negotiating the number in isolation. Ask what preference multiple applies, whether it participates, where the new money sits in seniority, and whether any ratchet attaches at IPO. Then compare offers on what you would actually receive across a range of exits, not on the press release.

None of this is legal advice, and your counsel should read the documents. But you should walk into that conversation already knowing which questions decide the answer.

The Horse, the Jockey, and the Tension

Kaplan, Sensoy, and Strömberg, “Should Investors Bet on the Jockey or the Horse?” Journal of Finance, 2009. And Bernstein, Korteweg, and Laws, “Attracting Early-Stage Investors,” Journal of Finance, 2017.

The oldest argument in venture capital is whether you back the jockey or the horse. Unusually, there is good evidence on both sides, and the honest answer is that both findings are true and they are about different things.

One team tracked fifty venture backed companies from early business plan to IPO and then three years into public life. What they found was that business lines stay remarkably stable while management turns over substantially. The company described in the plan is largely the company that goes public. The people running it frequently are not the people who started it. The same pattern held when the study was repeated across all 2004 IPOs.

Yet a randomized experiment on real early stage investors, in which the information shown about fundraising startups was randomized, found that investors respond strongly to information about the founding team and not to traction or to the presence of existing lead investors.

The business persists. The founders often do not. And yet team information is what moves investors most.

For how you present: lead with the team. Founders routinely bury the team slide behind traction charts, and the causal evidence says that ordering is backwards.

For how you decide: choose the business carefully, because the horse is the part that endures. A strong team inside a weak market tends to end with the team replaced and the market unchanged.

For what you should expect: if you raise venture money, the possibility of an outside CEO is real and documented, not a punishment. Decide in advance whether you can live with that outcome.

The Price of Prestige: 10 to 14 Percent

Hsu, “What Do Entrepreneurs Pay for Venture Capital Affiliation?” Journal of Finance, 2004. With Hochberg, Ljungqvist, and Lu, “Whom You Know Matters,” Journal of Finance, 2007.

Every founder who has held two term sheets has faced the same question: is the better known firm worth the lower number? There is a measured answer.

The study looked at startups that received multiple competing offers at their first professional round, which holds the company constant and isolates the effect of the investor. Offers from high reputation venture firms were about three times more likely to be accepted. And those firms acquired their equity at a 10 to 14 percent discount.

In other words, founders systematically and knowingly give up roughly a tenth of their valuation to work with a more reputable investor. The premium is real, it is priced, and it is paid willingly.

There is a related finding worth holding beside it. Firms with stronger positions in the syndication network produce portfolio companies more likely to survive to the next round: a one standard deviation increase in the lead investor's network centrality raised first round survival from about 66.8 percent to 72.4 percent.

The market price of a better investor is roughly 10 to 14 percent of your valuation.

The trade is legitimate, so make it deliberately rather than emotionally. If the more reputable firm is asking for about ten percent more of the company, that is the going rate rather than an insult.

But interrogate what you are buying. Reputation is a proxy for something more specific: introductions to the next round, to customers, to executives. Ask directly which partner will hold the seat and what they have done for comparable companies.

Note also what the data does not say. A firm with genuine conviction about your sector, at a stage where you are a priority rather than an afterthought, often beats a marquee name where you are one of forty.

One Firm in a Thousand

Puri and Zarutskie, “On the Life Cycle Dynamics of Venture-Capital- and Non-Venture-Capital-Financed Firms,” Journal of Finance, 2012, vol. 67, no. 6, pp. 2247-2293.

The Institute renders a verdict of NOT VENTURE SCALE more often than any other, and founders sometimes hear it as a judgment on their ambition. The data says something different, and kinder.

Using twenty five years of US Census data covering essentially every firm in the country, researchers found that venture financed companies made up 0.11 percent of all new US firms between 1981 and 2005, rising to 0.22 percent at the peak of the internet era. Measured by the number of businesses started, venture capital is close to irrelevant.

Measured by employment it is enormous: those same companies came to account for roughly 5.3 to 7.3 percent of US employment by the early 2000s.

The most useful finding is the third. Venture financed firms achieve substantially larger scale than matched firms without venture money, but they are not more profitable at exit. Venture capital buys scale and speed. It does not buy profitability.

About one new American company in a thousand takes venture capital.

If your business is sound, growing, and profitable but does not have a credible path to the scale a fund requires, you are not failing. You resemble 99.9 percent of American companies, including a great many excellent ones.

The question worth asking is what you actually want the money to do. Venture capital is the right instrument when speed and scale decide whether you win a market that will not wait. It is the wrong instrument when the business could compound quietly on its own revenue, because taking it commits you to an outcome that must be enormous or nothing.

Available through journal subscription only. We link only to legitimate publisher and author copies.

Grants Are the Road to Venture

Howell, “Financing Innovation: Evidence from R&D Grants,” American Economic Review, 2017.

Non dilutive capital is usually presented to founders as the consolation prize, the thing you pursue when investors say no. The evidence suggests it is closer to a launch ramp.

The study examined ranked applicants to the US Department of Energy's SBIR grant program, 7,436 small high technology firms and more than $884 million in awards between 1983 and 2013. Because applicants are ranked, the researcher could compare firms just above and just below the funding cutoff, which are otherwise very similar.

An early stage award approximately doubled the probability that the firm subsequently raised venture capital, with large positive effects on patenting and commercialization as well. The effects were strongest for the youngest and most financially constrained firms.

The mechanism matters. The benefit did not come mainly from certification, the prestige of having won. It came from the money funding a working prototype, which resolved the technical uncertainty that was keeping investors away.

A grant roughly doubled the odds of raising venture capital afterward.

If you are building something with genuine technical risk and investors keep asking whether it can be made to work, a grant is not a detour. Funding the prototype is the shortest path to the answer that unlocks the round.

This is why a verdict of FIXABLE frequently comes with a grant recommendation attached. The gap is often not ambition or market size; it is an unresolved technical question that non dilutive money is unusually well suited to settle.

And the capital is free in the way that matters most: it costs no equity and no control at the stage when both are most expensive to sell.

The Fastest Growing Founders Are Forty-Five

Azoulay, Jones, Kim, and Miranda, “Age and High-Growth Entrepreneurship,” American Economic Review: Insights, 2020, vol. 2, no. 1, pp. 65-82.

Every founder who started later than they meant to has been told, usually by implication, that they missed the window. The largest study of the question finds the opposite, and finds it in the place where the evidence is hardest to argue with.

Researchers linked administrative records on firms, workers and owners covering 2.7 million people who founded a company in the United States between 2007 and 2014 and went on to hire at least one employee. Not a survey, and not a sample of famous companies: close to the whole population. The mean age at founding was 41.9.

Among the very fastest growing new ventures, the top one in a thousand by employment growth, the mean age was 45.0. Success did not skew younger. It skewed older. The pattern held across high technology, where the mean was 43.2, among founders of patenting firms at 44.6, and across the entrepreneurial hubs. Prior experience in the specific industry predicted much higher rates of success.

Across 2.7 million American founders, the mean age at founding is 41.9.

If you have been treating your age as a liability to be explained away, stop. On the largest available evidence it is the ordinary profile of a founder who succeeds, and the industry experience that came with those years is itself among the strongest predictors in the study.

There is a sharper point in the data, and it is aimed at investors rather than founders. Across almost every category the researchers examined, mean founder age rarely dipped much below forty. The one exception was venture backed companies, and the youngest group of all was venture backed founders in New York, at 38.7. The authors note the tension plainly: younger founders have substantially lower success rates, which makes the industry tilt toward youth look less like an edge and more like a habit.

Use that carefully rather than combatively. It is not an argument to make in a pitch meeting, where it will read as defensiveness. It is a reason not to accept the premise if a room treats your age as the problem, and a reason to lead with the industry experience that the evidence says actually predicts the outcome.

Whom Your Investor Knows

Hochberg, Ljungqvist, and Lu, “Whom You Know Matters: Venture Capital Networks and Investment Performance,” Journal of Finance, 2007, vol. 62, no. 1, pp. 251-301.

Founders are told to optimise for the partner, the valuation, or the brand. There is a measurable case that the thing to optimise for is who else the firm can call.

Researchers mapped the syndication network of the venture industry, treating every co-investment as a relationship between two firms, and then asked whether a lead investor’s position in that network predicted what happened to its portfolio companies. Across funds raised between 1980 and 1999, it did, and the effect was large enough to matter to a founder choosing between term sheets.

A company’s chance of surviving its first funding round sits at 66.8 percent unconditionally. Raise the lead investor’s network centrality by one standard deviation and that becomes 72.4 percent. The effect compounds rather than fading: from 77.7 to 82.8 percent at the second round, and from 79.2 to 86.6 percent at the third.

A better connected lead investor lifts first round survival from 66.8 to 72.4 percent.

The paper is specific about which kind of connectedness pays, which is what makes it usable in a meeting. What mattered most was the size of a firm’s network, how often it was invited into other investors’ syndicates, and whether it had access to the best connected firms. What mattered least was acting as a broker between investors who were not otherwise connected. Being wanted by the people who matter beats being a hub.

So the question worth asking is not how large the fund is. It is who invites them into deals, and who they will introduce you to for the next round. Roughly a third of companies do not survive past the first round, and follow-on funding is exactly where a lead investor’s relationships do their work.

It is also a caution about a common piece of advice. Taking the highest offer from a poorly connected fund is a real trade rather than a free one, and this is the paper that puts a number on what is being traded away.

The Experiment Behind the Team Slide

Bernstein, Korteweg, and Laws, “Attracting Early-Stage Investors: Evidence from a Randomized Field Experiment,” Journal of Finance, 2017, vol. 72, no. 2, pp. 509-538.

Most of what founders are told about pitching is folklore: someone raised, someone remembered why, and the reason hardened into advice. This study is the rare exception, because the researchers were able to run the industry's central question as a true experiment.

They randomized the information that real early stage investors saw about real startups that were raising. Some investors saw more about the founding team. Some saw more about traction. Some saw whether a prominent investor was already leading the round. Because the variation was random, whatever moved the investors can be read as cause, not correlation.

One thing moved them. The average early stage investor responded strongly to information about the founding team, and did not respond to traction or to the presence of existing lead investors.

The authors go a step further: the team result is not merely investors using the team as a shorthand signal of quality. The evidence suggests that investing on team information is a rational strategy, because human assets causally matter for how early stage companies turn out.

Team information moved real investors. Traction and existing backers did not.

Order your pitch accordingly. Founders bury the team slide behind product and traction because that is the order in which the company matters to them. To the person deciding, the causal evidence runs the other way.

Spend less on social proof. The presence of a respected lead in the round, the thing founders spend weeks engineering, did not move the average investor in the experiment at all.

And read the boundary honestly: this is evidence about early stage decisions, where there is little else to evaluate. As real numbers accumulate they begin to speak for themselves. Until then, you are the evidence.

Harsh Terms Are a Diagnosis

Kaplan and Strömberg, “Characteristics, Contracts, and Actions: Evidence from Venture Capitalist Analyses,” Journal of Finance, 2004, vol. 59, no. 5, pp. 2177-2210.

Founders read term sheets emotionally, and the temptation is strongest when the terms are hard: the milestones feel like distrust, the control terms like a taking. It is worth knowing what actually produces them.

The researchers read the internal investment analyses that venture firms wrote for themselves about 67 portfolio investments across 11 firms, the private memos in which a partnership tells itself the truth, and then compared what the memos worried about with the contracts that followed.

The risks in the memos sort into three kinds: internal risk, in the team and the execution; external risk, in the market, the competition, adoption and exit; and complexity, the sheer difficulty of pulling the plan off.

Each kind leaves its own fingerprint on the contract. Greater internal and external risk brings more investor cash flow rights and more control rights. Internal risk specifically brings more contingencies on the founder, which is to say milestones. And greater complexity brings less contingent compensation, because when the plan itself is hard enough, tying pay to milestones stops making sense.

The memos also say what the investor intends to do after wiring the money. In at least half of the investments, the firm expected to play an important role in recruiting management; in at least a third, to provide other help, such as developing strategy or facilitating partnerships. Greater investor control went with more management intervention, and larger equity incentives with more value-added support.

Milestones are a map of what the investor doubts. Read them as information.

When your term sheet arrives, decode it rather than absorb it. A heavy milestone load means the doubt is about team and execution. Heavy control terms with light milestones suggest the worry is the market. The contract is telling you which risk you are being priced as.

That reading gives you a negotiating strategy that charm does not: reduce the actual risk, and the term attached to it loses its justification. A signed pilot customer argues against a market-risk term better than any meeting will.

And expect the involvement the memos promise. The firms wrote down, before investing, that they intended to help recruit your management. That is the deal being described in advance, not a betrayal discovered later.

The Average Fund Is the Stock Market

Kaplan and Schoar, “Private Equity Performance: Returns, Persistence, and Capital Flows,” Journal of Finance, 2005, vol. 60, no. 4, pp. 1791-1823.

Venture capital carries an aura of outsized returns, and founders absorb it: surely the money chasing them is money that beats everything else. The measured answer is humbler, and far more useful.

Examining fund level returns collected by Venture Economics, the researchers found that the average fund, net of fees, performed approximately in line with the S&P 500. An investor in the average venture fund would have done about as well in an index fund.

The average, though, is the least interesting number in the paper. Around it sits enormous dispersion: some partnerships dramatically beat the market and many trail it. And unlike mutual funds, where last year's winner tells you almost nothing, performance in venture persists strongly. A partnership whose fund outperforms is markedly more likely to outperform with its next fund as well.

The paper also documents what booms do. Money floods in, new partnerships form, and funds started in boom times are less likely to raise a follow-on fund, which the authors read as a sign that they performed worse. Established partnerships ride the cycles far more steadily than new entrants.

The average fund matches the index. The good ones repeat. Which firm you take money from is the whole game.

This is the empirical basis for a rule the Institute repeats often: which investor matters at least as much as whether. The industry's edge is not spread evenly across it; the edge is concentrated in specific partnerships, and it stays there.

Track record is therefore a legitimate question for a founder to ask, not an impertinence. A partnership's prior funds carry real information about its next one, which makes asking about them as reasonable as their asking about your metrics.

And it is one more reason the price-of-prestige trade documented elsewhere in this Library is rational rather than vain. Paying roughly a tenth of your valuation to work with a persistent outperformer is buying something the data says exists.

The Numbers You Hear Are Survivors' Numbers

Korteweg and Sørensen, “Risk and Return Characteristics of Venture Capital-Backed Entrepreneurial Companies,” Review of Financial Studies, 2010, vol. 23, no. 10, pp. 3738-3772.

Every founder carries a folk statistics of startups: the average outcomes, the legendary multiples, the stories that prove the upside. This paper is about a flaw in how nearly all of those numbers are made.

A startup's value is only observed at particular moments: when it raises a round, goes public, or is acquired. Those moments are not distributed evenly across companies. The better a company is doing, the more often its value gets written down somewhere. The struggling company raises no round and prints no number; it simply goes quiet.

Any statistic computed from observed valuations therefore leans, automatically, toward the winners. The authors built a model that corrects for this dynamic selection, and the correction is not small: estimated returns fall markedly, and estimated risk rises, compared with the studies that came before.

Valuations are recorded when things go well. Statistics built on them flatter the industry.

Discount the folklore. When someone quotes average startup returns at you, ask where the failures sit in the average. Usually they are simply absent, unrecorded, because failure prints no valuation.

Set your expectations on the corrected picture: riskier, and less lucrative, than the visible record suggests. That is not pessimism. It is what the data says once the silent companies are counted.

It is also the Institute's answer to a fair question: why a fundability verdict here rests on the structure of your business rather than on inspirational precedent. The precedents you have heard of are, by construction, the exceptions.

What the Money Does to the Company

Hellmann and Puri, “Venture Capital and the Professionalization of Start-Up Firms: Empirical Evidence,” Journal of Finance, 2002, vol. 57, no. 1, pp. 169-197.

Founders evaluate venture capital as a financing decision. The evidence says it is equally an organizational one: the company on the other side of the round is run differently, and sooner than founders expect.

Using a hand-collected data set of Silicon Valley start-ups, the researchers compared companies that took venture money with those that did not. The funded companies professionalized faster and more thoroughly: they adopted human resource policies, put stock option plans in place, and brought in a marketing VP earlier.

The same hand shows itself at the top. Venture-backed companies were both more likely and faster to replace the founder with an outside CEO, and the replacements were not only hostile ones: they occurred in situations that appear adversarial and in ones that were mutually agreed.

The authors describe two facets working together: a soft one, in which the investor helps the company build its human organization, and a hard one, in which the investor exercises control at the top. Both come with the same signature, and together they place venture capitalists well beyond the role of a traditional financial intermediary.

Venture-backed companies professionalize faster, and replace their founders faster, by agreement and otherwise.

Take the money knowing what it buys as well as what it costs. The professionalization is real value: an option plan, real HR, a serious commercial hire, all earlier than you would likely get there alone.

The CEO finding belongs beside that value, not hidden underneath it. This Library documents elsewhere that business lines persist while management turns over. This paper shows the venture investor is an active agent of that turnover, not a bystander to it.

So decide before the round, calmly, which founder you are. Those for whom the company's outcome matters more than the seat take this trade knowingly, and some founders are right to refuse it. Either answer is respectable. Discovering your answer mid-crisis is the only wrong plan.

The Part of the Accelerator That Works

González-Uribe and Leatherbee, “The Effects of Business Accelerators on Venture Performance: Evidence from Start-Up Chile,” Review of Financial Studies, 2018, vol. 31, no. 4, pp. 1566-1603.

Accelerators bundle several things: money, a desk, a network, and a programme. Founders choosing between them rarely know which ingredient does the work. In one setting, the bundle came apart cleanly enough to measure.

Start-Up Chile, a large ecosystem accelerator, admits startups near a scored cutoff, which let researchers compare near-identical companies just above and just below the line. Some participants received the basic services, a cash grant and coworking space. Others received those plus entrepreneurship schooling: a structured programme of accountability, mentorship and instruction.

The result was one-sided. The basic services alone, the cash and the desk, showed no detectable effect on venture performance. The schooling, bundled with those same services, significantly increased it, raising the new ventures' fundraising and scale.

One further detail is worth carrying. The schooling did not measurably change whether companies survived; founders keep companies alive by persistence. What it changed is whether the company grew and raised.

The cash and the desk did nothing measurable. The schooling moved fundraising and scale.

Choose programmes by their programme. When you compare accelerators, weight the structured parts: the cadence of accountability, the quality of the mentors, the instruction. The stipend and the office are the parts that were tested alone and did nothing.

This is why the Institute's accelerator directory dwells on what each programme actually provides rather than on its headline check. The check is the least evidenced part of the bundle.

And it pairs with the grant finding elsewhere in this Library: non-dilutive money works when it resolves a specific technical uncertainty. Money as mere fuel, without structure around it, has a much weaker record.

The Practitioner Canon

Sahlman (1997), Andreessen (2007), Suster (2010), Gurley (2011), Graham (2015), Dixon (2015). Six essays from inside the arena.

The academic wing of this Library measures the industry from the outside. These six essays are the industry explaining itself, written by people deciding with real money, and they have shaped more term sheets than any journal.

William Sahlman of Harvard, in How to Write a Great Business Plan, gave the durable four-part frame: people, opportunity, context, and risk and reward. Decades of pitch advice since are footnotes to it.

Marc Andreessen, in The Only Thing That Matters, argued that of team, product and market, the market wins, and that reaching product market fit divides a startup's life in two: everything before it is one company, everything after it another.

Mark Suster, in Invest in Lines, Not Dots, published on his blog Both Sides of the Table, explained why investors prefer to watch you over time rather than decide from a single meeting: each meeting is a dot, and what an investor funds is the line through several of them. It is the essay to read before asking why the process takes months.

Bill Gurley of Benchmark, in All Revenue Is Not Created Equal, showed why two companies with identical revenue deserve wildly different valuations: margins, recurrence, customer concentration, and the moat behind the revenue decide its quality.

Paul Graham, in Default Alive or Default Dead?, gave founders the single most clarifying operating question: on your current trajectory, do you reach profitability with the money you have? Founders who cannot answer it usually learn the answer too late.

Chris Dixon, in The Babe Ruth Effect in Venture Capital, described the power law that governs the whole business: the best investors do not succeed by failing less, they succeed by winning bigger when they win. It explains, better than any other single page, why a fund can praise your solid business and still pass.

People, opportunity, context, and risk. Market over product. Lines, not dots. Default alive. The power law.

Read these before you raise. They are short, free, and written by the room you are walking into. Together they explain most investor behavior that otherwise reads as rudeness: the slow courtship, the market obsession, the pass that comes wrapped in praise.

Shawna draws on all six in interviews, and every claim she makes from them is kept in this catalogue.

Each essay above is linked directly on its author's own site, except Suster's, which his blog serves only through Medium. We link only to legitimate author and publisher copies.

The Chasm, and the One Segment That Crosses It

Moore, Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers, HarperBusiness, 1991; third edition, 2014.

A great many companies that come to the Institute have real users and no business. The users are enthusiastic, the numbers are small, and the founder cannot understand why the enthusiasm does not spread. Geoffrey Moore wrote the book on why, and it has held up for three decades because the mechanism it describes is about buyers, not technology.

Moore starts from the technology adoption life cycle, a bell curve of five groups: innovators, early adopters, the early majority, the late majority, and laggards. The received wisdom treated this as a smooth progression. Moore's argument is that it is not smooth at all. Between the early adopters and the early majority lies a gap he calls the chasm, and most technology companies fall into it.

The gap exists because the two groups buy for opposite reasons. Early adopters are visionaries: they buy a change, they will tolerate a rough product to get ahead, and they do not need anyone else to have bought first. The early majority are pragmatists: they buy a proven productivity improvement, they want the whole thing to work, and above all they want references from other pragmatists. A visionary's endorsement means nothing to them. So a company can win a dozen visionaries, show impressive early logos, and find that none of it transfers, because the people it must sell to next do not take the word of the people it has already sold.

Visionaries buy a change. Pragmatists buy a proven gain, on the word of other pragmatists.

The way across, in Moore's account, is deliberately narrow. Pick one segment of pragmatists, small enough to dominate, with a compelling reason to buy that the product can fully satisfy. Serve it with the whole product: not the core technology but everything the customer needs for the promise to be kept, including the integrations, the services, and the partners. Become the obvious choice inside that segment, so that pragmatists there reference each other, then use that base to enter the next segment. Moore's image is a beachhead: a narrow landing, taken completely, from which the rest of the territory follows.

It explains a verdict the Institute renders often. Fifty enthusiastic users who cost nothing and reference no one are pre-chasm. The finding is not that the product is bad; it is that the evidence so far comes entirely from the group whose word the next buyer will not take. The question a partner will ask is whether a single pragmatist segment has been won, and won completely.

It also argues against the instinct most founders have when growth stalls, which is to widen. Moore's answer is to narrow: one segment, whole product, dominance, then the next. A company that is a big fish in a small pond has references. A company that is a small fish in ten ponds has a mailing list.

And it tells you what to build. If the core product is finished and sales are stuck, the missing piece is usually the rest of the whole product, the unglamorous parts that let a pragmatist buy without a leap of faith. Moore's is a framework from practice rather than a measured finding, and the Institute cites it that way, but few frameworks have named the failure mode of more good companies.

A book, in print from HarperBusiness. The third edition, 2014, is the one to read. We link only to legitimate publisher and author copies.

What a Network Is Worth

Shapiro and Varian, Information Rules: A Strategic Guide to the Network Economy, Harvard Business School Press, 1999.

Founders of marketplaces, platforms and software companies use the phrase network effects the way an earlier generation used synergy: constantly, and as if saying it made it so. The book that put the idea on firm economic ground was written by two Berkeley economists at the end of the nineties, and it remains the clearest account of when the phrase is earned.

Shapiro and Varian begin with a fact about information goods that reorganises everything downstream: they are expensive to produce and almost free to reproduce. The first copy of a piece of software or a database costs a great deal; the second costs nothing. Cost-based pricing therefore collapses. Information must be priced by its value to the customer, and because customers value it differently, the sellers who do best offer versions, so that buyers sort themselves by what they are willing to pay.

The second pillar is lock-in. Every switch has costs, in retraining, data, integrations, and habit, and the authors' rule is blunt: an installed base is worth the total switching costs of the customers in it. That is why a company will spend heavily to acquire a customer it will not profit from for years, and why customers who understand this negotiate hardest at the moment of first adoption.

The third is the network effect itself: the value of a product to each user rises with the number of other users. This produces positive feedback, in which the strong get stronger, and markets that tip toward one winner. It also produces the problem every network founder knows, which is that a network with few members is worth little to anyone, and so must be pushed past a critical mass before the feedback begins to work in its favour.

An installed base is worth the total switching costs of the customers in it.

Be precise about which of these you actually have. Many companies that claim network effects have economies of scale, or lock-in, or a good product. Ask whether each new user makes the product more valuable to every existing user. If the answer is no, say so plainly and build the case on what is true, because a partner who has read this book will check.

For a two-sided marketplace, the authors' trade-off between openness and control is the central design decision. Openness grows the network fastest; control captures its value. A free side that generates the signal a paying side buys is a bet on openness, and it works only if the free side stays engaged long enough for critical mass to arrive. That is a question of product, not of pricing.

And on pricing, versioning is the practical gift. If one customer will pay ten times what another will, do not choose between them; build the version each will buy. The book is a strategy text rather than a measured finding, and the Institute cites it as such, but its economics have not aged, because the marginal cost of a copy is still zero.

A book, in print from Harvard Business Review Press. We link only to legitimate publisher and author copies.

Where the Profit Sits, and Which Way It Is Moving

Slywotzky and Morrison, The Profit Zone: How Strategic Business Design Will Lead You to Tomorrow's Profits, Times Business, 1997. With Moser, Mundt, and Quella, Profit Patterns: 30 Ways to Anticipate and Profit from Strategic Forces Reshaping Your Business, Times Business, 1999.

Every interview at the Institute reaches the same question: how does this business make money, and why should the profit sit there rather than somewhere else in the chain? Most founders answer the first half. Two consultants at Mercer Management wrote the books on the second half, in the late nineties, when the question was becoming urgent for the first time.

Slywotzky and Morrison's starting observation is that profit in any industry is not spread evenly. It concentrates in a profit zone, a particular position, activity or customer set where the economics work, while the rest of the industry can be large, busy and profitless. Their sharper claim is that the zone moves. As customer priorities shift, profit migrates from one position in the value chain to another, and a business built for yesterday's zone can find itself doing everything right in what they call a no-profit zone. Their case studies were the giants of that decade, but the mechanism has nothing to do with size.

The method they propose runs backward from the customer. Start with what the customer's priorities actually are now, not what they were; ask what the customer will pay for and where; only then design the business that captures it. The Profit Zone catalogues twenty-two profit models that recur across industries: the switchboard that profits from connecting many buyers with many sellers, the installed base that profits from what it sells after the first sale, the de facto standard that profits from being the thing everyone else must fit, the blockbuster, the customer solution, the time profit of being first, and others. Profit Patterns, the sequel, extends the idea into thirty patterns of migration and argues that the strategic advantage lies in recognising a pattern early, while it is still forming.

Profit concentrates, and then it moves. The question is not where it is but where it is going.

Name your model. When a founder says we are a marketplace, the useful next sentence is which profit model the marketplace runs on, because a switchboard, an installed base and a de facto standard make money in different places and fail in different ways. A partner who has read these books will hear the model whether or not you name it, and naming it first is the difference between describing a business and understanding one.

Then show the migration. The strongest form of a why-now argument is not that a technology has arrived but that profit is moving toward the position you occupy: the customer's priorities have shifted, the incumbents are built for the old zone, and the new zone is still cheap to enter. A company positioned where profit is going is fundable in a way that a company positioned where profit already is, and where incumbents defend it, rarely can be.

The books are frameworks from consulting practice rather than measured findings, and their examples belong to another decade. The Institute cites them as such. But the question they teach a founder to ask, where does the profit actually sit in this industry and which way is it moving, has not aged a day.

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