Joining Sutter Hill Ventures

I’ve joined Sutter Hill Ventures to find and build companies with our next generation of EIRs and CEOs.

Over the past several years of writing and working with founders I have been driven by the question of how our industry can best compound at creating companies that matter. What became clear is that the earliest stages of exploration are not only the least institutionally focused on. But also where it is most valuable. I was led to SHV early in the course of this exploration, and when the opportunity to join and help build its next chapter presented itself it was obvious I needed to accept it.

Few firms take the earliest stages of company formation seriously. The gravity of fund math pulls them away from the flux of building new things from the ground up. Much less doing it repeatedly. This raw experience with the formation stage and ability to generate outsized returns while focused on it, is what makes SHV special. There is a familiarity with the nebulosity that can only be developed from having repeatedly been through the founding journey from beginning to end.

SHV is instrumental when the clay is wet, in foundational companies that redefine their industries like Snowflake, Nvidia, and Pure Storage. It believes that the beginnings matter, even when they are at their most delicate and feel like they don’t. And in being a partner in the exploration of what is truly possible and worthwhile.

At its core, the team is focused on the art and science of creation. And what has impressed me most is their commitment to continuously redefine and refine themselves to serve founders through the nebulosity of the founding journey.

SHV understands that we cannot make starting companies easier. I don’t know that anyone can. But we can make that effort go further. And that can make all the difference.

In just the first few months here I am already grateful for the experiences of being at SHV and exploring with our EIRs. I’m excited to contribute to making SHV the best place for founders to create their life’s work.

If you have a vision for a future you want to pull forward, or you’re a deep technical expert, experienced executive, or repeat founder and would like to learn more, please reach out.

Narrative Distillation

The shifting sands of tech IPOs

If the IPO process is like a debutante ball, the top investment banks are akin to a finishing school. They help gussy up companies, teach them proper manners like how to do GAAP accounting, and bring them around to call on prospective investors and eventually debut to society.

This is the role investment banks have played for decades, but in recent years this dynamic has begun to break down. Not in all sectors—in most sectors investment banks still occupy the same role—but in tech, the importance and role of investment banks has shrunk and commoditized.

Historically, raising capital was difficult and public market investors had little awareness of the companies going public. Investment firms were not focused on tech companies. And especially for enterprise startups, retail investors had no exposure or familiarity with them. In this environment, it was the idiosyncrasies of the capital markets that mattered, not the uniqueness of each company. In this model, the company is not that special. Instead, what matters is the standardized process for making the company fit the mold investors expect from an investment asset. Companies may have potential, but they don’t know how to introduce themselves to the investor community in the public markets.

In every marketplace one’s power is proportional to their value added to the transaction. While investment banks view themselves as having an important role in guaranteeing the quality and rigor of which companies are ready to IPO, that seems less true today.¹ Banks used to be gatekeepers because markets needed to be told which companies were good. However, discovery is no longer the constraint. Companies are more known and thus the relative leverage and importance of the sell side is falling. And as investment banks increasingly manage only the logistics of the IPO process, they become less important in dictating its terms.

The best founders have figured out that owning their narrative gives them meaningful leverage. Founders and companies can increasingly communicate their narrative in a direct and compounding way to investors. And the roadshow is a progressively smaller component of investors’ views on the company.² What makes SPACs and Direct Listings notable is not their cost structure, but that they allow companies to much more directly market their IPOs.³

i honestly wrote this entire piece only as a setup for this joke. and i have no regrets.

The ability for companies and public market investors to connect more directly has never been easier. Many of the tech equity funds now do both public market and private pre-IPO investing (and increasingly even earlier stage). Firms like Tiger, Coatue, Durable, and D1—or even T Rowe Price or Fidelity—don’t need to be introduced by bankers to companies. They already have been tracking companies for years and want to know the founders and their companies directly. Founders have the ability to directly build a relationship with the investors that will anchor their IPO. In fact, letting that relationship be primarily mediated by the investment banking process adds friction to the process and is less effective.

Most tech startups are also way better known by the time they go public. This is especially true for consumer startups. Companies like Airbnb, Roblox, Robinhood and Coinbase are widely used and known. When your product is used on a regular basis by investors (or their families) and has been covered extensively by the media, the incremental investor roadshow meeting is less important. Direct daily experience using the product is better marketing than any roadshow presentation. Even non-consumer companies are increasingly well known. By the time companies go public today, they have orders of magnitude more traction than companies going public decades ago did. But also investors just care about tech more. With companies able to IPO at $50B rather than $500M, they are more important and known.

Another characteristic of the current landscape is that the revenue multiples companies can get in the public market have a very wide range. Look at the multiples we’re seeing: everything from 2x to 2000x. This is true both for small SPACs and large direct listings. What separates these companies is how effective they are at conveying a compelling narrative to the public markets.

When the multiple range is so high, the difference between an alright and amazing IPO is a function of people’s belief and confidence in the future potential of business. It can be speculation. It can be based on traction. But also can be on the founders competence in explaining how to think about business today and why that sets it up not just for consistency and predictability but for continual compounding.

As a founder in a world where capital is easy to get, what matters is how to explain yourself, distill the company, and get public markets to understand you in the right way.

Narrative already drives venture fundraising

If this description of the public market sounds familiar, it should.

Where the public markets are heading should be no surprise—it’s what is already done in the early stage venture ecosystem.

In startup fundraising over the last decade, rounds have grown in size, and more importantly, bifurcated into different classes of companies. There are orders of magnitude across the range of valuations for companies with the same level of revenue. Some companies raise at 5x while others can raise at 300x+ ARR multiples. Among pre-revenue companies, the spread of valuations is even greater.

More crucially, in the venture ecosystem, there is universal acknowledgement that founders should drive their fundraising process and pitch directly to investors. A half century ago, companies used to hire bankers to help them raise capital. Today, no VC would take a company doing that seriously.

Being able to best convey the progress and promise of a startup is the job of the CEO. No one has better context on and ability to change the business. And no one is more responsible for conveying that not just during fundraising, but every day—to everyone.

There are three types of fundraising pitches: narrative, inflection, and traction raises:

Narrative pitches are driven by a compelling story of what could be
Inflection pitches are driven by secrets discovered. The company has hit some inflection point that, if investors were astute enough to understand, would make them realize now is the ideal risk-adjusted time to invest
Traction pitches are driven by the results of what’s already been done. The company could be a black box and investors would invest solely off the metrics

the original ngmi. see i am hip with the kidz terminology

The dirty secret is that there is no such thing as traction pitches anymore. Because as every company knows—our best days are always yet to come.

Ironically, the companies with the best traction want to be given credit for their future potential the most. No one wants to rest on the laurels of the past. And achieving the highest multiples requires having a narrative of why even more is to come.

Fundraising and IPOs are natural loci for companies to take a step back and shape their narrative. They are natural points for companies that are often mired in the day-to-day to think hard about their business from a multi-year standpoint. However, while fundraising is a good prompt for companies to think about their narrative,⁴ the importance of carefully distilling a company’s narrative is increasingly ubiquitous.

there’s a lot going on in this chart. I was told to cut down graphics so i just jammed them all into one gif. malicious compliance.

Narrative leverage in an anomalous world

The more static and predictable the world is, the less narrative matters. Historically most businesses followed clear precedents and fit neatly into paths. If starting a restaurant, the potential range of revenue and costs is known. There are few surprises, so it’s easier to look at the current state of the business and know what it will look like in a few years. There is little volatility.

Tech startups radically break this mold. By definition they will be unrecognizable in five years, whether that’s because they are a unicorn or because they are extinct.

One essence of the tech industry relative to others is the ability of tech companies to precisely select their atomic units and where they sit within their ecosystems, leading to hugely different outcomes.

Empirically, one way to see this is in the widening spread of valuation multiples. Every week, we hear of another company raising at high valuations, but more importantly wild revenue multiples. At the series A and B rounds, we’ve seen multiples of 100x+ or even 200-300x+ ARR become regular occurrences. They’re not common; most companies’ rounds are still raising nowhere near this multiple. But they do exist: there is a subset of companies that are able to raise at orders of magnitude higher revenue multiples. The spread in revenue multiples is widening. Companies with the same amount of revenue increasingly get wildly different valuations. This is the power of narrative, in contextualizing the snapshot of a company’s performance.

There are a number of reasons narrative is becoming more important:

Large dynamic range of outcomes. Startups have a huge range of outcomes. They can be worth nothing and a decade later be worth billions. And this spread is expanding. The largest tech companies are now worth trillions. More topically, over the last decade the distribution of outcomes for most tech IPOs has increased by an order of magnitude. Enterprise investors, for example, used to assume that beyond the once-in-a-generation company, most enterprise IPOs were hard capped at single digit billions in market cap. The entire business model of enterprise venture investing was built on this assumption. And it is no longer true.⁵ When the potential range of outcomes of companies has many orders of magnitudes within it, then one’s confidence level in the probability distribution of outcomes becomes incredibly important.

Back-weighted LTV. Increasingly tech companies don’t make the majority of their revenue from the first interaction with customers. In each sector this is done via a different approach, but fundamentally SaaS, freemium, Open Source, etc are all examples of this. This is one of largest trends in tech over the last few decades and is worth further exploration*. Revenue being a lagging metric isn’t bad, but it means that understanding the value of a company requires a rigorous understanding of the leading metrics that will drive future revenue. This again means that a company’s ability to explain to others how to think about the business and why they are so confident in the inevitability of future revenue based on current product metrics is crucial.

Sequencing to Multi-Product / Platform. With enough scale, there is a truth about modern public tech companies: you either die a single product company, or live long enough to be multi-product or a platform. This is the inevitable path for almost every successful company, but the likelihood of success is hard to tell while the company has a single product and has never tried to make the leap to multi-product or platform. The best companies get valuation multiples that give them credit in advance for future business model sequencing This again puts the onus on the company to explain why they can become multi-product and what it will mean.

Compounding loops. Finally, we are still very early in our understanding of how to quantify and predict the returns of compounding loops. Network effects, economies of scale, and still unnamed types of loops all can have a very disproportionate impact on long term value of a company—but there is no simple way to infer it from an early snapshot of performance. Companies must work on explaining the compounding loops they are building, what leading metrics to look at to see their potential, and why they will be so powerful.

Self-fulfilling prophecies: When is narrative justified

Narrative leverage in tech is most commonly understood in the sense of Steve Job’s Reality Distortion field.

Personally, I like to think of this narrative leverage as a form of PE ratio (Price to Earnings ratio). I sometimes call it the PR ratio (Perception to Reality ratio).

How do we know if narrative leverage is good? It’s easy to think of many companies where the reality of the company didn’t live up to the hype. Some of these were fraudulent and illegal, like Theranos. But tougher are the ones that lie somewhere on the spectrum of over promising. There’s a blurry line between the need to project confidence in order to gather all the resources to make the hype a reality and lying about things that will never happen. And while there’s no perfect way to separate these, how should one think about that spectrum and about the appropriate amount of narrative leverage a company should have?

It’s also important to note that narrative is not just stories founders make up isolated from reality. At their best they are distilling for outsiders truths already known internally. These can be explaining the leading cohort metrics or early signs of TAM expansion potential that give confidence.

Here is where I think the analogy to PE ratios is useful.

In the public markets, the PE ratio of a stock is the ratio between its market cap and earnings. This is a reflection of how high investors will value a company relative to its current earnings.

If a company’s value is the net present value of future cash flows, its PE ratio in a rough sense is a proxy for how confident investors are of high future cash flows.This is a simplification, but you can generally view a company with much higher PE ratio than another company with the same earnings as one investors think will have higher growth and future cash flows. Over time the rate of Earnings growth will help catch up to the expectations, bringing the ratio down—or investors will continue to believe, keeping the ratio high.

When we talk about revenue multiples of startup funding rounds, this is the private market equivalent of PE ratio.

What is the benefit of a high PE ratio? It is cheap cost of capital. It allows companies to raise capital with the benefit of getting credit for what they will be in the future. It is a loan pulled forward from the future.

Is a high PE ratio good? The answer is not a simple yes.

A PE ratio is good if there is appropriately high ROIC (return on invested capital) by their usage of it. If getting a loan on a future promise allows you to deploy it effectively to better live up to those aspirations, then it was not just worth it—the aspirational perception helped catalyze and make its own prediction inevitable.

That is some kind of magic, creating something from nothing.⁶ An ouroboros eating only itself yet somehow growing. Perhaps modern time travel is our ability to take a loan out from our future success to ensure we achieve it.

PE ratios are a promise continually renewed—and they can be warranted or misplaced. When companies’ earnings cannot keep pace and live up to these expectations, we see the price and PE ratio eventually fall to reflect this, even more so where companies don’t have the ability to take advantage of their PE ratio and the lofty expectations of them to improve the business now.

Outright frauds like Theranos cannot live up to their valuations. Their PE ratios are bad since with or without the benefit of a high multiple, they can never grow into their valuation.

More complicated are companies like Tesla. For years there were vicious debates over whether Tesla was over or undervalued, with vitriolic takes from both sides. What made the question hard to answer was that they were both right. Elon takes on industries where new approaches can work as the industry cost curve improves, but require massive and cheap access to capital for extended durations to work. Elon is not unique in this dynamic. It can also be seen in fields like AI research.

Thus Tesla’s PE ratio is in many ways self-fulfilling. If Tesla could get people to extend the access to capital it needs for long enough it will be successful. If it could not, then it would have collapsed. Ironically, this means that far from Elon’s antics being distracting, his ability to maintain these high PE ratios might be the most important driver of the company’s ability to succeed.

But this general concept is not unique to the public markets, or to money. In some sense, even people have a social capital PE ratio. PE ratios in the public markets are just one instance of a more general concept of having some view of what something can become—and giving them today the benefit of that future tomorrow accordingly.

Narrative leverage is the PE ratio of a company. Not just for cheaper cost of future capital, but also for everything else companies care about too. Cheaper cost of recruiting, customer development, and perhaps most importantly—internal coordination.

Venture as social staking: the future is founders

Modern venture itself is not just about money and the cost of capital. Most founders would give a discount in pricing to the top VC firms. What the top VC firms are selling today isn’t money—they’re lending their own brand to startups. Having Sequoia invest will lend the stored PE ratio of Sequoia to the portfolio company. This will help give them a cheaper cost of capital, recruiting, and customer acquisition.

LPs may care that a firm has good returns, but that’s not intrinsically relevant to founders. It only matters insofar as those returns translate to stronger brand value or direct relationships that can be used on behalf of the founder’s company. This is why venture today exhibits power laws with the top firms attracting disproportionate returns.

In today’s ecosystem, however, companies are increasingly able to have as much, if not higher, narrative leverage than VC firms. The top companies—and especially their founders—are more known than their VCs. At the extreme end, Patrick Collison has much more ability to attract investment, customers, and hires than any of the VCs on his cap table. Every year there’s an increasing number of founders at each stage who have higher brand leverage than what VCs can bring to the table. This is both due to the growing primacy of founders as well as the relative stagnation venture has had beyond brand network effects.

employees is on this chart. oh you can’t see it. oh…how weird.

And founders have so much more surface area to compound this brand leverage than their investors can. Founders can uniquely refine their narrative hand in hand with building the business to make it best resonate with all their prospective investors, hires, or customers. They also have the full resources of their company to bring that narrative to bear.

Narrative distillation is a core part of company building

The largest trend in every function within companies is that they’re being pulled internal. Engineering was the first. The birth of modern software companies began when companies first understood that engineering wasn’t a back office job to outsource, but a core part of the primary job of a company. Core, not commodity.

Endogenous compounding is increasingly the foundation of all modern successful companies. In a world where it is hard to be successful and unknown, all external channels increasingly get arbitraged. Companies that discover some novel market or promising acquisition channel quickly find themselves joined by many competitors. And the outsized returns they briefly got fall back down to earth under the weight of competition. It is internally compounding advantages that fight the gravity of this reversion to the mean. This is why we talk so often about network effects & economies of scale. Because like any polynomial equation, as scale rises & approaches infinity, only the highest order bit matters. And it is the aspects of the company that are internal to its organization or ecosystem that can most compound unimpeded by the outside world.

While engineering was first, it is not unique. Every function whose returns on iteration are high and non-commodity will follow the same path.

The transition from marketing to growth was this exact same process. Traditionally marketing was something done after the work on the product was already complete. Companies would finish the product and throw it over the fence to the marketing team. The easiest way to know if a function is core or commodity is 1) whether the function is identical at other companies, or unique to the particulars of their company and 2) whether it has feedback loops in the company or purely uses external channels.

Modern growth teams are impossible to remove from the core flow of their companies. In fact, they are fused to core product and engineering. How can you do growth without it being inextricably tied to the core flows of the product?

Brand marketing is still important, but on a relative basis it is increasingly shrinking compared to paid acquisition and more importantly core product driven distribution. If you think about bottoms up, product driven SaaS companies or viral social networks, they are examples of how impossible it is for traditional marketing to compete with the product itself. The best companies understand that distribution is a first party concern when thinking about a product, not some checkbox to finish after.

In “Why Figma Wins” I wrote about how design is undergoing this exact same transition. Design at the best companies cannot be relegated to artists told what to make after all the decisions have been made. They must be part of the core decision making throughout the entire process and all its iterations. This doesn’t just fall on the companies. It also means designers must accept more responsibility. The best designers want to be at the table. And they understand that they must not just think at a creative level, but also in how their design process and output shapes the core business. The best designers not only do this, they relish it.

The same is happening to the narrative of companies. Increasingly, narrative isn’t primarily about external framing. It’s not something done after the work has been completed.

Adobe has continually shown over the last few decades how core managing the narrative is to getting the support and coordination of investors and employees as the company makes fundamental shifts to their business model. Whether that be in adding new products, transitioning to the faster internal cadence of a SaaS company, refactoring into a cloud-first infrastructure and pricing model, or the myriad other endeavors Adobe has undergone from building printing software to the full expanse it is now.

Those shaping the narrative must intimately understand how employees, investors, and customers think about the company. Refining and expanding the narrative is entwined with the company’s progress. Narrative is shaped by each iteration of a company’s processes and products. And in turn a company’s evolving narrative shapes how it focuses its processes and builds its products.

Founders are responsible for holistic narrative distillation

Too often we focus on how much money a company can raise. But money is rarely an ends. Instead, it’s a resource to spend to functionally derisk the company. Historically, capital was the scarcest resource. Venture capital as an industry was built and structured around capital scarcity as the most important blocker on company success.

But increasingly it isn’t scarce anymore. And it certainly isn’t the main blocker for many of the top companies. Talk to top tech companies today and raising capital is ironically one of the easier aspects of building and derisking the company. Hiring and retaining a talented team is far harder. Acquiring and retaining customers is harder. Understanding and getting the team coordinated on what to build is harder. Oh, and did I mention that hiring and retaining a talented team is far harder?

This is the CEO’s job: to raise and allocate the capital needed, but also to build a team capable of building the product needed and getting distribution. All while understanding what the company needs to build and helping the team understand and orient around it.

Narrative leverage is not just an advantage on the cost of financial capital. And not just a PE ratio with the financial markets. It exists in the leverage with all stakeholders both external and internal. It’s what makes prospective employees excited to apply and work for your company—despite all the tech companies fighting for talent. It’s what gives your customer confidence you will not only not go under—you’ll be focused on building a product that’ll continue to blow them away.

And perhaps most importantly, it’s what makes the team understand not just what the company looks like today—but what it could look like in five years. And makes employees able to see beyond their role, to how they fit into the larger picture of the company’s strategy.

Who’s in charge of that narrative? The answer is complicated and different depending on the audience.

From the employees’ perspective, it’s internal comms. From the customers’ perspective, it’s marketing—or perhaps the product itself. From the investors’ perspective, it’s investor relations.

But at most companies, these are primarily teams who manage how the narrative is distributed and shared. They are rarely the ones shaping and iterating on it, especially where it must bend the direction of the company itself.

There is no team that owns the narrative of a company. No team that determines its atomic concepts. This is why we often see large disagreements within a company on how to think about itself.

Some of these differences are natural. After all, customers care about different aspects of a company than its investors. And employees in different roles may have good reason to be focused on different timescales. But too often, the disagreement is unintended and harmful.

At most companies, only the CEO or founders can shape and reshape narrative.

Top companies already recognize primacy founder led narrative

Suggesting CEOs should prioritize this narrative distillation and go direct to their audiences isn’t idle prognosticating. The top CEOs already do care, and spend significant time on it.

The company that was first and best at building their brand is Stripe. There may be no company with higher narrative leverage than Stripe and the Collison brothers. From its earliest days, Stripe has excelled at this.

Stripe has long since grown into much more. But in its early days a significant amount of its value was simply in its ability to get great engineers to work on payment integrations and internationalization. Today, working on developer-first API companies may be sought after, but that did not used to be true. If a company tried to get its best engineers to work on internationalization of payments, they’d just refuse. Or quit. Stripe was able to get great engineers to work on these distinctly not high profile areas. And that alone, is worth a lot.

It’s not that the Collison brothers set out with some deliberate master plan to build a brand, culture, or personal reputation that would attract developers to work on Stripe. More likely, is that they filled a structural hole around payments. Payments needed a company that was developer first and engineering driven, and only founders with the predispositions of the Collisons could attract and build that kind of team in a space that those engineers would have otherwise dismissed.⁷

KK Note: Even today, the ability to get strong engineers to work on a problem engineers normally don’t want to work on remains a very strong formula for returns.

The Collison brothers may not have started with narrative in mind. But they have been quick to understand and capitalize on it. Stripe’s brand leverage among prospective employees in tech is incredibly high.

Stripe’s slogan, “Increase the GDP of the internet” points at a far loftier vision and more ambitious goal than the mundanity of payment processing. And this is reinforced in both much of Patrick Collison’s projects outside of Stripe as well as initiatives like Stripe Press.

And if Twitter is an increasingly strong channel for hiring and customer acquisition of tech startup customers, Stripe is the most dominant brand among tech Twitter. So much so that there appears to be an entire genre of Twitter content that is new Stripe employees tweeting about their onboarding experience.

You can increasingly see other top companies shifting to invest more in their company and founder brands. Shopify and its CEO, Tobi Lutke, are a good example of this.

In the last few years Tobi has become much more visible publicly. He goes on podcasts, hangs out in Clubhouse, does AMAs while streaming videogames on the Internet, and much more. If allocation of attention is the best proxy for prioritization, Tobi has strongly signaled his view of the importance of building personal brand and shaping Shopify’s narrative.

Increasing brand awareness and expanding the CEOs’ reach is real leverage. Reinforcing that Shopify is a tech company doesn’t hurt their multiple in the public markets, but cheap cost of capital is likely not what limits Shopify. Like all tech companies of this scale and success, their ever present constraint is recruiting. Having a strong brand and easy access to capital helps, but all their competition have that as well. The red queen race for talent is unceasing. Especially as Shopify has expanded its executive hiring outside of Canada to the US where it is relatively less known.

Founders have a unique ability to build brand for their companies. But of course companies must build it beyond them. Shopify has many other initiatives, like a studio producing TV shows and movies on entrepreneurship and starting an esports team.

Shopify is not alone. Spotify is now making podcasts about how they build their product, and Daniel Ek is doing interviews on podcasts and blogs. Twilio is launching a magazine for their customers. And of course Elon is…being Elon.

It’s an advantage today. And will be table stakes tomorrow.

Final thoughts

Narrative is the other side of the coin of functional derisking. If a company is a series of functional derisking loops, then narrative is the leading edge of what is to come.

my editor told me no one would understand this chart. so it’s for me, not you.

Founders want credit not just for what they have already done, but what they are going to do: launching new product lines, changing their business model, becoming a platform. They want to pull forward credit for these future developments to the present to help make them inevitable.

Even more so, they want their team to have synchronicity around what is most important for the company’s future and how to prioritize and make tradeoffs.

What’s the difference between future investors and potential hires thinking a company is distracted and unfocused versus inevitable and defining? It’s in the coherence of the company’s logic for each sequencing of steps and how legible that narrative is made to them.

And in today’s market it is increasingly the founders who are able to distill and manage the overall narrative. This is only increasingly as companies undergo significant business model changes as they scale and the capital markets treat startups more as a fungible asset class.

Product market fit is just narrative distillation for customers. It only makes sense that this same process is as crucial for investors and employees, too. And just as we have spent so many years reinforcing the primacy of founders focusing on product market fit—and the process of how companies converge on it—so too must founders take distilling their narratives for all audiences equally seriously.

Appendix: Making companies that matter

Recently⁸ I tweeted that I was glad to see Discord hadn’t sold and that there’s some list of companies I hope never sell. While I do think it would have been underpriced, this isn’t the reason I don’t want them to sell.

Companies like Discord are not important because of the returns they may have. There is no shortage of companies that can drive returns. There are far fewer that can change their industries. And Discord is not alone—there is an entire rising generation of companies this applies to. Companies like Figma, Canva, Flexport, Benchling, and others are all at the cusp of getting to meaningful scale within their industries. (KK note: I don’t think I need to worry about any of them selling. But yes if you are a founder of any of these companies please don’t sell).

In prior essays I wrote on how we should judge venture firms not on their returns but on the value they added above replacement to companies. This is true for companies as well. Companies should be evaluated on the value they add above replacement.

Many companies simply occupy a structural hole in the market. In a world where they did not exist, some other company would simply have occupied their slot, without loss of generality. These companies may have significant profits, but they don’t matter. In some sense the profits were going to be realized regardless, and should not be attributed to their contribution. Great companies pull forward the future. They introduce solutions or business models that would otherwise take many more years to come about.

And the most defining companies change their industries’ trajectories and hurtle their ecosystems into shapes that otherwise wouldn’t have been seen at all.

There are only a few dozen companies at a time that have line of sight to being defining companies in their industry. While correlated to profitability, this isn’t about their ability to generate money. It’s about the gravitational force they will exert, re-orienting their industry into a new structure and alignment and proving out new business models whose structure will be replicated by all companies to follow.

This is the most compelling narrative that a founder can create around their company: that they have bigger ambitions than just succeeding as a business, that they have a chance to change the nature of business itself. For a select few, it’s not just a pipe dream—it’s the truth.

Endnotes

[1]: The current public markets are like a bar, and the investment banks are the bartender trying to regulate how many IPOs get served. But investors don’t want to be held back from more IPOs, whether they have had too many or too few drinks. And at this point investment banks have given up on trying to regulate this. Whether investors have had one drink, or one too many drinks, is an exercise left to the reader. Or rather how leveraged long tech beta the reader is.

[2]: If we are mutuals and you are planning to IPO soon. First, congrats. Second, please let me convince you to not let the investment banks run your IPO process the traditional way.

[3]: Direct listings have financial benefits relative to traditional IPOs, but I think these are secondary. And for example, modern DPOs seem to shift who gets the preferential pricing from the investment bank’s clients to a friendly hybrid fund that gets the pre-IPO floor setting round. This is a positive shift, but more incremental than transformational. It is the shift to companies owning their own narrative and marketing that is overlooked but most important.

[4]: Ironically, the hotter and more founder friendly the fundraising environment is, the less it is a fitness function forcing narrative clarity from founders. For many founders lucky enough to have VCs throwing money at them, there is no longer anyone but themselves who can force them to really refine their narrative.

[5]: Over a long period of time, the form of the venture industry is dictated by the scale, expected value, and risk distribution of the ecosystem of startups. If that distribution shifts, so too will the venture industry.

[6]: No, seriously. That is true magic.

[7]: Paul Graham has a great essay about schlep blindness. In it he posits that the reason we see founders avoid building companies addressing schlep problems is an unconscious avoidance of unpleasant work. This may be true, but I suspect the larger reason is that we don’t value solving these schleps appropriately. Historically the social capital in working on these areas was far less than on public facing products. Which made it hard not just to get excited about personally working on them, but also to hire teams and bring on investors. The best solution then is not ignorance as Paul Graham suggests, but rather a collective reappraisal of schlep industries. Which seems like what has happened over the last decade, as areas like b2b SaaS have become desirable. Also suspect the bigger macro driver is demand side fragmentation and growth. But shhh.

[8]: Given how long ago I started this essay “recently” is no longer accurate.

Acknowledgements

Many thanks to Keila Fong for all the help editing this piece.

Additionally, thanks to Kane Hsieh for advanced gif manufacturing assistance.

All graphics in this piece were created with Procreate and Figma. ~~An integration between these two might have a target audience of only me. But I would love it.~~ Many many thanks to Rogie for creating this amazing Procreate Import for Figma plugin. I’ve been very excited to take it for a spin, and it is life changing. Being able to import all the layers of a procreate file into Figma allows for so many more powerful combinations of the two tools. If you read my blog you don’t need me to repeat my love for Figma and what its plugin ecosystem enables. But this really is a perfect example to me of the power of Figma’s plugins—and even more so its community. Also, the power of twitter. heh.

Edit: Thanks to Nitesh for keeping me accountable on holding the line against title case hyperinflation

* Further pieces to be written on these subjects**

**I’m probably lying about this. To you, but mostly to myself.

New video series, Closing the Loop

If you follow my blog, then you likely follow Eugene Wei on twitter. Eugene and I have started a new video series, Closing the Loop.

You can find it here on Youtube.

Some of the topics include:

Graph Design and the Limits of Social Graphs
Unconstraining Emergent Creativity
Decentralization, Creators, and Platforms
The Power of the Internet and China
Social Platforms
Algorithmic Design, Tinder, and Platform Governance
And a bunch of other random stuff

I’d pretend we’re doing it because people say our posts are too long. But I checked the word count of the transcript, and it was 15k words which is significantly longer than most of my posts. So I guess you won’t really be saving much time.

We’re hoping to go into more follow up discussions on many of the topics we write about. As well as have some guests on.

Fear not, I’m not shifting away from writing. In fact, have been editing a new post I’m hoping to publish soon. It just turns out this is coming first because…video is a lot easier than writing. At least the way we are doing it with no second takes. We’ll see when I can publish. The process of making videos has also been fascinating. Whether here or on Twitter, I’m guessing will have some post-mortems on many parts of that process to share.

How to Eat an Elephant, One Atomic Concept at a Time

How Figma and Canva are taking on Adobe—and winning

In 2010, Photoshop was ubiquitous. Whether you were editing a photo, making a poster, or designing a website, it happened in Photoshop.

Today, Adobe looks incredibly strong. They’ve had spectacular stock performance, thanks to clear-eyed management who’ve made bold bets that have paid off. Their transition to SaaS has been seamless, for which the public markets have rewarded them handsomely. And they’re historically one of the best companies at M&A; their product lineup is a testament to their ability to acquire new product lines and integrate them well into their multi-product ecosystem. Perhaps most importantly and least appreciated, they have dramatically sped up the cadence of their internal product development process and feedback loop. Like Microsoft, they have successfully shifted from a legacy company operating on an annual (or longer) release schedule to a truly cloud company shipping updates at a sub-weekly pace.

Nevertheless, there are a few segments of design where they’re no longer the market leader. Companies like Figma, Sketch, and Canva are examples of products that have been able to become top products despite Adobe’s ubiquity in all things design. Figma showed up in Adobe’s annual report for the first time in 2019. They reprised in 2020, and I’m not uncertain they will continue to be in it going forward.

How should we understand these market transitions and why these young companies are able to thrive, even against a strong incumbent like Adobe?

These companies have distinct atomic concepts from Adobe. The primitives that their products are built around are fundamentally different from those of Adobe’s product lineup. It’s these different fundamental atomic concepts that turn Adobe’s advantage of an established product and existing userbase into a weakness that hinders their ability to counter these upstarts. The opportunity for these new atomic concepts to thrive is driven by the new use cases and types of users unearthed during market transitions.

Understanding the phases of market transition and what drives them is a universal process worth examining.

New use cases: designing for digital

For most markets, there are advantages to being an incumbent. Markets converge as companies arrive at the preference frontier of customers. This leaves little potential energy for new startups to take advantage of.

Market entropy is good for new entrants.

It’s not impossible to break into a market by brute force, but it’s hard. Very hard. Most successful companies, especially startups, have found tailwinds to harness that help pull them forward.

Changing customer needs are the largest source of entropy in markets. When customer needs rapidly change, there is less advantage in being an incumbent. Instead, legacy companies are left with all the overhead and a product that no longer is what customers want.

There are many causes of changing customer needs. Often there are new and growing segments of customers with different use cases. Existing products may work for them, but they aren’t ideal. The features they care about and how they value them are very different from the customers the legacy company is used to. Companies resist changing core parts of their product for every new use case since it’s costly in work, money, and attention. But every once in a while, what was once a small use case grows into one large enough to support its own company.

Other times the scale or dynamics of a market shift enough to make a product no longer work despite having been a great fit. Companies are often caught flat-footed by these situations because what they have done successfully for years suddenly starts to falter—and they aren’t sure why. Ebay is a good example of this. Their decentralized auction model was very good in a nascent internet economy when there was a scarcity of items being sold online. Once ecommerce became commonplace, price and speed became much more important factors and Ebay’s decentralized model was at a disadvantage. Amazon was much better at building economies of scale in this post-liquidity ecosystem.

Another source is when the customers themselves change. Often the function of a tool remains the same, but the type of user changes. These new types of customers often have different things they care about and resulting product needs.

The internet drove entirely new design use cases. Photoshop was built for editing photos and images. It’s a powerful tool that operates at the pixel level. However, many of these new uses weren’t about image manipulation. Images were a component—not the essence—of the job users were trying to accomplish.

For some users, this was designing digital products. Designers at software companies or any company with a website wanted to create the websites and software products they worked on. This is less about image manipulation and more about designing the UI and UX of these digital products. Vectors are more important than raster graphics. The complexity and process of designing these high-value designs also got increasingly more sophisticated. These designers worked with teams of other designers and non-designers. Their designs are part of a larger product development process and what mattered wasn’t just making a design, but how that the entire process could be improved to make collaboration easier and handoff of designs better. Iteratively.

The complexity of the designs and the components in the resulting code became more complex, too. The need for their tools to have a higher-level understanding of the components and variants became more important. It’s increasingly useful for designs to understand the same concepts and abstraction levels as the HTML and CSS in the resulting end product.

For some users, this was designing content for social platforms, digital ads, or even wedding invitations. These were often made in Photoshop, but again, pixels are the wrong abstraction level. Images are not the sole component; they are just past of a larger design that includes graphics, text, and more. Similarly, the customers are very different. Many of the people now doing what is, in essence, design work don’t think of themselves as designers. They just have a very specific thing they want to create, with the least friction possible.

The internet dramatically scales up the volume and type of new use cases for design. In many ways, this helps Adobe. With platforms like Instagram, the number of people editing photos has expanded by many orders of magnitude. While editing on platforms like Instagram may have increased significantly, Adobe has been a huge beneficiary of the internet and the shift to cloud—and their stock price is a testament to this.

[KK Note: Platforms like Instagram strapping editors onto their social platforms and eating into Lightroom from the bottom up is well worth its own discussion. And perhaps someone will convince Mike Krieger to do the definitive piece on that.]

Software may be eating the world. But it’s also building new worlds? I’m going to need a refresher on remembering the Andreessen Horowitz talking points

This is even more true in video. There are orders of magnitude more video creators as the ability to record video has become ubiquitous and the platforms where video is the default format have grown. Even more striking, many of the dominant video platforms—like Youtube—are purely distribution focused. They don’t even have any editing capabilities. Instead, companies like Adobe end up being large beneficiaries of this need.

[KK Note: Platforms like Youtube still having not built any semblance of an editor into their platform is *also* well worth its own discussion. I’d say we’ll never know what could be, but then I look at TikTok and all is right with the world.]

But Adobe hasn’t captured it all. And in many of these new emergent use cases and customer types, Adobe has lost the lead to new startups.

Tapping into the right level of abstraction

The best products map to how customers think about their workflow. They match the abstraction level of their customers: not too high that it’s unusable, but not too low that it’s hard to use easily or extend in more complex ways.

They choose the right atomic concepts.

These are the core concepts around which the entire product is built. They not only align with how customers think of their workflow, but often crystallizes for customers how they ought to. Great atomic concepts are honed and then extended and built upon in more complex compounds that…well for lack of a better word…compound.

Similar companies often have slightly different atomic concepts that end up making them meaningfully distinct. Photoshop is focused on pixels and images. Its focus is on editing images and pictures. And its functions operate by transforming them on a pixel level.

Illustrator is similar, but it operates on vectors, not pixels. This is a higher level abstraction. Neither is better or worse, they are just more suited to different use cases. Photoshop is better for modifying images, while illustrator is built for designs where scale-free vectors are best.

Sketch, like Illustrator, is vector based. But is designed for building digital products which means things like operating at a project level. It is not individual designs, but crafting entire products and user interfaces—and the needs for repeatability and consistency inherent to that.

Figma builds on Sketch’s approach, but also includes a greater focus on not just projects but the entire collaborative process as the relevant scope. Similarly, it also treats higher level abstractions like plugins, community, and more as equally important concepts.

Canva is similar to Photoshop and Illustrator, but its users aren’t designers who care about low level tools. Instead Canva’s core atomic concepts are around the different templates and components to help them easily accomplish the job they are doing. And the designs they are working on are not quite at the project level of making a digital product. They are canvases that include images and design.

There are many more axes, but they don’t fit in this stupid 2D chart

Atomic concepts are fundamentally linked to the core loops of a company. Expanding or changing these loops often involves adding to a company’s vocabulary of atomic concepts or adding them together in more complex ways.

Emergent use cases and new customer types lead to new ideal atomic concepts. These new workflows and different customers have different priorities than existing customers. How they think about their problems and weight possible solutions is different, even if often the end output has similarities. Of course, astute readers will pick up that causality is reversed here. New types of customers are a good proxy for where to pay attention. But it is actually the changed atomic concepts that are what make startups a compelling contender against incumbents in the space.

Customers don’t care about your technical architecture or internal org structure. When these no longer align with the job they are trying to do, then all the sprawl of the company becomes harmful, not helpful. These are the core bedrock that are much more difficult for a company to change mid-flight. Everything that makes an established company strong is built on top of this foundation and will fight back against changing them. Take Blockbuster and its reliance on physical stores and late fees. People often fall into the easy narrative that incumbents are asleep at the wheel. That they are too stupid to see the coming threat. This can be true but it isn’t the most common reason. Contrary to popular belief, many execs at Blockbuster not only saw the threat Netflix posed, but also the opportunity for Blockbuster to have claimed the mantle Netflix now holds. They even spun up a team to take Netflix head on. But what made retail stores and late fees so powerful and profitable for Blockbuster is also what made them so hard to displace. Every move to prepare Blockbuster’s core for a digital future was resisted by execs who generated more revenue, store operators who were livid at being cut out, and Wall Street investors uncomfortable with turning a consistent business into a high risk venture.

Rare is the company that can change its core atomic concepts. It’s why companies like Amazon are so impressive and so daunting. Startups thrive by finding asymmetric angles on incumbents that they are unable to follow. What is safe from a company with no sacred cows?

Understanding the core abstraction levels of a company is hard to understand from a distance. Which is why looking for emergent customer types with different needs is a useful substitute.

Figma bet on collaborative product design

Sketch was the company to first understand the market opportunity in designing digital products. Launched in 2010, Sketch was built entirely for designing the UI and UX of these products. Its atomic concepts were those best for digital products: vectors and projects. These were also what made it hard for Adobe to compete with their pre-existing product line.

In a classic innovator’s dilemma, Sketch’s best feature against Adobe was that it dropped everything that wasn’t best for making digital products. This allowed it to focus only on creating the best experience for vector-based digital design. Unlike Photoshop, it was vector based. And unlike Illustrator it was built with larger complex projects as the focus rather than specific isolated designs.

In retrospect, Sketch stopped at a half measure. Designers creating digital products did need vector-based design tools. And Sketch also understood that they were working on more complex projects vs one off designs that needed better project-first features. But these designers were also often working on teams—both with other designers and, more importantly, with non-designers. They weren’t designing in isolation, but as part of a larger process.

Sketch, like Adobe before it, lacked in this area. Everything from Sketch’s technical architecture and desktop based product to its pricing model and platform structure were a poor fit for this collaboration. The demand for these features could be seen in the messy ways that companies hacked together solutions to this and the many products that sprung up to fill these holes. Companies like Zeplin, Sympli, and Invision grew out of designers’ needs for better ways to coordinate with the other designers, PMs, and engineers they worked with. Sketch’s plugin system, like Adobe’s, felt more bolted on than core to the platform.

When Figma first started, it was more directly a Photoshop competitor. Over its first two years, though, they shifted their focus specifically to designers working on the UI and UX of digital products as they talked to more potential users. Building out the product to enable collaboration uniquely was key to these designers. Doing this was non-trivial. The technical challenges to do so were very hard, though Figma was well set up due to Evan Wallace’s technical prowess and specific knowledge in new technologies like WebGL. Building for collaboration to its fullest extent has led Figma to rethink almost all of the company—leading to new pricing models, distribution models, and sharing form factors.

For those interested in reading more on Figma, I have a prior post that can be found here so will avoid rehashing many of the same observations. Figma’s success came as it honed in on this growing use case of complex digital products built by larger teams of designers and non-designers—and in finding the atomic concepts that were uniquely needed for this new skew of users.

As discussed in Why Figma Wins, over the last few years this is most visible in their expansion into larger enterprise customers. Large companies have the same (if not greater) need for design tools that are built for the collaboration in their org as small startups or smaller teams within them. However, the set of features and tools they need around this look very different from a small team. When Figma started, it found its fit first with small teams, but as entire large companies started to look at it seriously it needed to understand how to think about collaboration and building a design tool not just at a team level—but at the scale of an entire company.

Canva bet on marketing design by non-designers

With the rise of digital platforms like Facebook, Instagram, and Youtube, marketing and advertising have increasingly shifted online. Online advertising has many differences from traditional advertising. Most notably, it is much faster paced—and often more targeted. Companies now do many small variations on the same campaign: testing which versions do best, making personalized versions for different customer cohorts, and adjusting them to the different required form factors of each ad platform. The traditional process of having a few large campaigns each year looks increasingly archaic. The cadence was a function of the primary channels being areas like TV and print, where campaigns are costly so only a few large campaigns can be run a year. As the channels shift, the campaigns, tools, and teams adjust to match the new dynamics.

Increasingly, marketing teams don’t need whole design teams working on each campaign. Rather, they want tools that made it easy for them to adjust their marketing designs in small ways—like being able to format it for both their instagram ad as well as their Youtube banner. The background of the person needed to do this changes, too. Instead of hiring design agencies, companies bring this work in house, both because more of the work can be done by non-designers and because the pace of iterations makes working with an external agency too slow.

Once again I am asking you to be impressed by my multimedia use of graphics, drawings, and logos

Marketers and people posting on Instagram don’t think of the design work they want to do in terms of pixels. It’s the wrong abstraction level. They aren’t trying to directly edit the photos themselves. The photos are just an aspect of the specific goal they have in mind. They think of it in terms of the aesthetics and purpose of the design—not just the images but also the text and graphics and more.

Photoshop can do everything they want, but it is too low level. Photoshop’s atomic concepts are images and pixels. Editing at the pixel level is perfect for photos and image manipulation. Canva operates at a higher abstraction level—the one its users care about. Canva designs start with their purpose in mind, whether that’s designing a pitch deck, an Instagram post, or a wedding invitation. Canva has templates and layouts built for that specific purpose, while making it easy for users to add their own creativity, whether by putting in their own photos or using any of the many graphics and components made by the community.

This need is even more felt by SMBs and teams who can’t have a full design team work on every project. Canva’s lightweight editing with easy templates and process for making many small changes like formatting for different social platforms made it ideal for these customers.

This also allows Canva to extend its platform around these molecular levels. Canva’s distribution is driven in large part by their SEO. Unsurprisingly, the very same use cases people use Canva for are what people looking for design tools want to do and search Google for. With their product and templates built around these use cases, it’s easy for Canva to expose that externally and have lots of templates and examples ready to go for potential new users looking to do a specific design. Everything about their user acquisition and onboarding is built around the specific use cases people have and Canva’s atomic concepts. They are built around the functional workflows people have, whether that’s making a Twitter background photo, a wedding invite, or a keynote presentation. And Canva is committed to making that as easy as possible.

There is something very illuminati about this pyramid and sun. You heard it here first

Defensibility through becoming a platform

As they’ve grown, Canva has expanded their ecosystem by creating marketplaces and communities around templates, layouts, fonts, and more. Most users don’t want to build from scratch. With Canva’s marketplaces there is an entire ecosystem of pre-built components they can use, both free and paid.

Canva having this strong ecosystem of add-ons is very powerful. Add-ons allow Canva to address the huge scale and varied needs of all its customers, far more than one company could ever do on its own. This makes it possible for each customer to use Canva in a way that will be personalized for exactly the use case and aesthetic they care about.

I did not repurpose the first chart. No one will believe you. shhhhh

There is nothing sadder than the fact that no one will build a Procreate x Figma integration JUST. FOR. ME.

Creating free and paid add-ons have long been a staple for most design tools. However, they haven’t been tightly integrated into the product, adding friction for users. In contrast, Canva builds add-ons seamlessly and directly into the product, making it easy for users to access them directly and leading to higher usage. Treating these marketplaces as first parties has a number of additional benefits. Beyond increasing the value of the product, it also cements platform network effects for Canva. A growing community of creators monetizes by selling add-ons for Canva; this reinforces Canva as the tool to use with the most robust ecosystem.

There is entire category of ecosystem loops that no one seems to talk about. Ecosystem loops deserve love too

This is just one example of how companies can use platform network effects to extend and defend their beachhead. There are few sources of defensibility stronger than the cross-side network effects of platforms. It makes it hard for any new competitors to get traction. Without a large enough user base, a new platform can’t attract developers to build on top of it. As a result, new competitors also lack the ecosystem of add-ons to meet all the needs of and attract users. This is why platforms are so enduring. They allow companies to scale the needs they meet beyond what’s possible for a single company and they create chicken and egg problems for any competitor hoping to follow.

Extending this playbook to other spaces

Design isn’t unique among fields. All these same factors that are driving new and large use cases in demand are similarly arriving in most fields, especially in all forms of digital content. It’s inevitable we will see many of these same changes happen to video as they have in design and photography, though the specific use cases and needs that emerge will look different.

The most active area obviously undergoing this market transition right now is the broader productivity space. Over the last few years, many of these new companies (Airtable, Notion, Coda, Roam, Retool, Webflow, and Loom, to name a few) have seen remarkable early traction. But it’s also hard to delineate what the exact spaces are within productivity and collaboration and which companies cluster together in which buckets. Many of the companies have lots of product roadmap overlap as they each navigate the amorphous high-dimensional space of customer types and needs.

Even for those companies with early success, many have yet to crisply define the atomic concepts they’re betting on and to position themselves accordingly. Which are competitors with which? Who are their customers and which use cases will be the most important workflows to build around? What factors will determine which companies succeed and centralize their markets?

Companies have trouble navigating these questions because customers themselves don’t think precisely about what they really want. These companies have the opportunity to change how customers think about their own workflows. The best companies introduce better atomic concepts and help push their customers forward. Strong enough products will have ecosystems around them whether or not the companies actively manage it. The best companies don’t just benefit from these ecosystems, they build their platforms to enable and direct these ecosystems in ways that empower their customers more.

Figma is beginning to expand its scope with new initiatives like plugins and communities. These are not the only ones I expect we’ll see (and there’s one that I’m particularly excited to see how they tackle) but they are core ones. As discussed more in Why Figma Wins, if these work they help expand the ecosystems around Figma, enabling users with new abilities and ways to engage with each other. An ecosystem also creates both defensibility and extensibility for Figma.

Beyond design and productivity, many companies today are right at the crux of these decisions. Getting a product’s core loop to work is a tremendous effort and very rare. For those who do, they are then faced with the question of what comes next.

These companies can (and have) comfortably gotten to single digit billions in valuation on their core products. If they want to go public or be acquired, they can do that. But they are also at the point where they can catch their breath, take a step back, and think about what the next decade of their trajectory looks like and what would be next in their roadmap’s sequencing if they were ambitious. For most of them, it will involve fundamental expansions of their atomic concepts. Going multi-product or becoming a platform is the key to compounding into significantly more meaningful companies.

For all the discussion on strategy, running an actual startup is often more a test of tactics and execution than strategy. One of the few exceptions to this is when companies are making new additions to their most core loops. Pre-product market fit is the most common of these moments. But the transition from a single product to a platform (or multi-product) is another common one that most successful companies experience.

Figma and Canva are examples of companies going through this expansion, but they are far from alone. Across the industry you can see a cohort of tech companies at this stage. Companies like Notion, Airtable, and Flexport are all beginning their explorations of the next major expansion of their products and platforms. While not done, they have been successful in building out their core product. As they think about their ambitions for the next decade, they will have to extend their product in fundamental ways.

Final thoughts

Often the smell test of a company is how easily it can be dimensionally reduced. It’s like some variant of Kolmogorov complexity. How few core elements can maximally explain it? People fairly push back that companies are intrinsically messy and cannot be compressed in this way. It is often true that VCs and outsiders simplify their view of companies in ways that are easier to remember but useless in practice. The flaws in this dimensionality reduction aren’t reasons to ignore it—they are the reason it is important.

As a founder, nobody is going to understand the full nuance of your company like you will. Everyone else does see a simplified, compressed, and sadly imperfect shadow of your company. Founders repeatedly underestimate the degree to which their products are complex and opaque to outsiders, because they have it fully loaded in cache. They have seen every iteration and revision and imagined in painful detail all the alternate lives their product could have lived.

Most users never talk to someone at a company. Even if they do, the vast majority of their interactions with a company are with the product. Your users know nothing about how your company operates. They don’t see all the late night whiteboarding sessions and careful deliberations that led to the specifics of each feature they use or the many iterations that were tested and rolled back and refined. They often only understand half of how your product can be used, much less your vision for how it should be used as it matures. And your future potential users don’t even know you exist.

As product becomes the driver of most interactions with a company, external gatekeepers and proselytizers like journalists and bankers become less important. Instead, it’s the clarity of a company’s product and product—and founder—driven distribution that become most key. We’re still early on in companies internalizing this.

This clarity is not just for users. It’s even more important for employees. They are the people who build complex compounds around these atomic concepts, and their misunderstandings are the root of future deviations and issues that arise. Founders get advice to repeat what matters more regularly than they think they need to. Repetition may help employees remember what’s important, but it pales in comparison to the clarity that comes from having strong atomic concepts to begin with. Like memes, simplicity is what makes them so transmissible.

One exercise I’ve often found useful for CEOs to do with their co-founders and team is to ask an important question about the company—and see how much everyone’s answers differ. People are always shocked at how much they differ from even their co-founder. It’s natural to have differences and that doesn’t even mean either person is wrong. But these unexpected differences in how to think about the company are the underlying faultlines that make it difficult to synchronize as a company on what matters and to have a common framework by which to discuss and debate important decisions.

All of this shouldn’t be misinterpreted. Very few companies come out of the womb with crisp atomic concepts. The nature of building a company is messy and complicated. Critics are right to say that many analyses over-simplify and give post hoc explanations of how to think about companies (yours truly included).

But the process of examining that complexity and finding the most lossless ways to dimensionality reduce is not the province of armchair analysts. It’s essential for founders and companies themselves to regularly do this refactoring. Just as companies build up technical debt, so too do they build up narrative debt.

Typically fundraising is a natural fitness function for doing this refactoring. For top companies this is increasingly no longer true—but the importance of this clean up has not shrunk. Whether for the sake of their users and employees—or so they can expand into becoming more complex platforms—companies must grapple with who they truly are, before they can go after who they want to be.

Appendix: Figma’s ecosystem and open source

There is a lot more that can be discussed on the platform ecosystem chart that is out of scope of this essay. This is a highly simplified chart, but it is one that comes to mind often when talking with founders of companies that are beginning to think through sequencing from single product companies to platforms. And are seeking a framework to think about their ecosystems (or analyze others) in a more structured way.

These charts can look very distinct for different companies. And even for the same company it moves over time as their user base shifts and they shape their ecosystem. Companies make intentional choices that have large impacts on what their platforms look like.

Figma is a good example of this. Unlike many platforms, Figma’s plugins and community initiatives put a large focus on being accessible to individual designers building out solutions to their own problems, whether just for themselves or to share freely with others. This focus is at odds with many other platforms that are mainly meant to be used by third party companies building products to be sold to users on top of the platform.

One impact of this is a bet on the importance of the long tail of niche use cases in Figma as seen below. There are many use cases that often are too niche to be supported as products to purchase that never are addressed in most platforms. But by making it easy for individuals or companies to build their own plugins, Figma hopes to see even these be addressed—and then shared out with the community in the way we see it often in the open source developer ecosystem.

Perfectly balanced, as all things should be

Acknowledgements

Many thanks to Keila Fong and Eugene Wei for the many discussions about this topic and help with this piece.

Additionally, thanks to Casey Winters for the many discussions about Figma and Canva. And our discussions for many years on these very topics.

Thanks also to Fareed Mosavat and Brian Balfour at Reforge. The Advanced Growth Strategy course was the origin of many conversations about Figma’s loops. And I still teach the Figma case study every semester. If interested in many of the areas in this piece, Reforge is the best place to learn them but also from people who’ve spent far more time actually putting them to practice in companies than me.

All graphics in this piece were created with Procreate and Figma. Procreate is a fantastic drawing app for iPad. If you have made it all the way through this essay and don’t know what Figma is then I don’t know what to tell you. Once again will put out into the world how much I want an integration between these two. What is the point of Figma’s platform solving for long tail niche use cases, if not to solve primarily for my long tail niche use cases.

The Mike Speiser Incubation Playbook

In Formula 1 racing, you can win a world championship as a driver with one team but then not even make the top 10 without that team’s car and infrastructure. Venture can often feel like this, too. Many top performing VCs would struggle if they weren’t on their firm’s platform. And similarly, a far greater number of VCs might be able to do well if they were just at a firm with a strong enough brand. Most special are those that are the source of their own success.

In Making Uncommon Knowledge Common, I wrote about Rich Barton because he’s one of the rare founders (or investors) with the demonstrated ability to create multiple billion dollar companies. Unpacking and learning from the few who have shown repeatable and internally compounding approaches to building companies is important.

Unlike consumer, traditional enterprise markets lend themselves more naturally to deterministic and repeatable success. There’s a small handful of VCs who have clearly shown they can succeed repeatedly and whose approaches and playbooks are legible enough to imply it’s not a fluke. Speiser is one of them.

Speiser’s portfolio includes companies like Pure Storage and Snowflake Computing. It’s worth noting that Snowflake not only IPO’d and is now at a market cap of over $60B but Speiser and Sutter Hill Ventures owned more than 20% of the company leading up to the IPO. When Pure Storage went public, Sutter Hill held more than 25%. Speiser may have the highest percentage of portfolio companies that have become multi-billion dollar companies—and that trend looks to continue with his newer companies.

But impressive returns are not solely what matters for the industry. It’s tempting to evaluate firms by their returns, and from the LP perspective that may be the correct metric. But another, and more important way to judge VC firms is by the value they add above replacement to their portfolio companies. How much do they help their portfolio companies increase their likelihood and magnitude of success? Firms do this most notably by providing capital, but also by other methods like lending their brand or directly helping with operations.

For founders, this value added is what matters. The returns of a VC firm only matter to a startup insofar as they translate into improved brand, network, or access to capital for the startup. A firm’s financial performance is a reasonable signal that they may add real value and be worth partnering with, especially since some aspects like brand strength for recruiting, future financing, and customer development are a function of perceived firm success. But to prospective portfolio companies, a fund’s returns are important only as a means, not an end.

What makes Speiser intriguing is how distinct his approach is from other VCs. The tantalizing clues suggest that he has figured something out that nobody else has: the formula for creating successful companies from scratch.

you didn’t really think there wasn’t going to be a drawing of a loop did you?

The Speiser playbook

At the core of Speiser’s approach is incubating companies, or “originating companies” in Sutter Hill nomenclature. Instead of investing in existing companies, Speiser stays solely focused on one thing: starting and building companies. Even among others who have been very successful at incubations, he is the most singularly focused on this.

Every year Speiser incubates around one company. The core of his model is to find 2-3 co-founders and be the founding investor. Often he takes on the interim CEO role himself for the first year or two. This has many advantages. The biggest is that it reshapes the ideal founding team profile. He can focus on getting the right top technical co-founders that will have strong views on what to build and the ability to build it—even if they are people who don’t generally view themselves as having a natural inclination to be founders. This is a significant talent arbitrage.

A better package for founders

There has been a dearth of coverage of Snowflake’s three cofounders, Benoit Dageville, Thierry Cruanes, and Marcin Zukowski in both the news and social media. Partially this is because they have not sought the spotlight. But partially, it is due to the veneration of a certain type of founder we have, those who seek the limelight of public presence and being in control of every aspect of the company.

Snowflake’s founders are cut from a different cloth. As Benoit Dageville put it “We never thought of it as building a company. We just wanted to build a cloud product. The company was an afterthought.” Yet, their product and technical decisions have been prescient in threading the narrow path to taking on Amazon and Google in the most important core markets of cloud computing.

There are people who often don’t want to be CEO, or even to start a company. They are driven by their conviction of what the future should look like, as well as their frustration with the internal dynamics they confront at legacy incumbents that prevent them from creating that reality. But they are still unlikely to start a company due to all the inertial cruft that comes with founding a company—and especially with being CEO. They want to build what matters, not set up a new corporate structure, manage fundraising, or build a sales team.

Eric Yuan, the founder and CEO of Zoom, has explained this feeling of being held back at Webex. He knew what should be built and that the Webex team could do it, but given the dynamics of Webex as a subsidiary of Cisco he was unable to get the political capital to do it. And he’s proven himself right by leaving with his Webex teammates and building Zoom. Considering he was VP Engineering at Webex and still unable to build what he thought was important should be a very discomfiting reality shock to large companies about the very real economic harm the malaise of their internal processes have caused. However, for every Eric Yuan, there are countless others that never leave and start a company. The inertial barriers are too high. They can stay at their company and struggle to work on what they know should be built. Or leave and take on more uncertainty and risk than they want.

Speiser introduces a third model that breaks through this Scylla and Charybdis dilemma. Start a company with Speiser and stay focused on what you want: deciding what to build, hiring the team you need, and building it. Speiser will handle fundraising, handling the operations generally, and setting up the sales motion and machine. Founders get much of the autonomy and upside of starting a new company while also getting support and guardrails so they can stay focused while having confidence the business is being built well.

Speiser doesn’t just take on these roles because founders don’t want to do it. There are actually aspects of company building where he should be better than the founders. Sutter Hill Ventures has the capital already, so it’s easy for them to take on responsibility for fundraising and remove that as a blocker. Instead of having to burn a lot of cycles fundraising, Sutter Hill can provide the capital. And they often do, leading multiple rounds into their companies. Or they can bring in outside investors, with the confidence that Sutter Hill can lead the entire round as a backstop if the process becomes too much of a hassle. Also, like any VC firm, Sutter Hill builds a brand that compounds their companies’ ability to raise follow on funding. At this point there are multiple firms that have made their bread and butter following on after Sutter Hill, to great success.

Similarly, Speiser is likely to have more experience in setting up companies and the initial customer development process than the founders will. Perhaps most importantly, he has relationships with customers and an established reputation that can be used to bootstrap the initial pilot conversations, which may be the point of highest leverage for these new startups.

These advantages all compound with every incremental company Speiser originates, and not just because of the typical brand network effects that venture has broadly. In many tangible ways, the spread between Speiser’s process knowledge relative to a new founder should widen with every new company.

As an industry we seem to often want to see machismo and martyrdom in founders. A decade ago it was wanting founders to be willing to mortgage their house and their kids’ college fund. Now it is founders wanting to be in charge of every aspect of companies. If founders aren’t willing to put everything on the line for the company their companies will be worse is the thought. As an ecosystem it doesn’t appear the data bears this out. Everything we do that has expanded opportunity and decreased the friction to more people becoming founders has led to huge benefits for the industry.

Just as Eric Yuan should be a massive shot across the bow for all large tech incumbents, Snowflake’s founders should be a wakeup call to venture that we have much further to go to enable and support even more brilliant people who don’t think of themselves as CEOs to bring their vision of the world into existence.

A better package for CEOs

But this isn’t the only talent arbitrage Speiser’s playbook benefits from. The interim CEO model allows another one too. As the startup does well and figures out its product market fit, Speiser eventually rolls off as CEO and finds a full-time replacement to take on the role as he takes a step back into being solely a board member.

His companies are very advantaged in finding great CEOs to take the mantle. To understand why, look at it from the perspective of an executive looking to become the CEO of a company. Like the potential founders, these executives have their own Scylla and Charybdis dilemma. They want to be CEO of a company, but they also want to join a company that has already derisked product-market fit instead of founding a new startup with all the attendant risks. However, there is significant adverse selection among Series A and B companies that are looking for an external CEO.

Now that most founders want to stay on and scale up as CEO of their companies, it often indicates the company is struggling if the board is looking to replace the founder. Even if turning the company around is doable, the internal and board dynamics are likely to be very acrimonious—with a hostile deposed founder.

There are a few exceptions, like Linkedin and Hashicorp, where the companies were doing very well and the founders wanted to bring on a CEO. That can be extremely effective, with the founders and CEO partnering to great effect. There is a lot to emulate in the dynamic and shared responsibility between Reid Hoffman and Jeff Weiner at Linkedin or David McJannet, Armon Dadgar, and Mitchell Hashimoto at Hashicorp.

But increasingly, founders wanting to bring in a CEO are the exception, not the norm, if companies only look for new CEOs while pushing out the founder.

Speiser can offer a much more attractive package.

When Speiser talks to a potential CEO he can say he’s showing them his strongest companies, because that’s true. His model is built on him leaving his role as interim CEO once they are working—and moving on to do it over again at a new incubation. Finding a new CEO is a feature of success, not a cry for help.

Many of my friends who are executives are bombarded by VCs trying to trick them into taking on their worst performing companies. After they do some light diligence and referencing, they realize that the company is months away from failure or the founder will be hoping for them to fail from day one. Executives grow to realize they should be default skeptical of any companies that VCs try to recruit them for as CEO.

Speiser is one of the few VCs who will really be pushing you towards his best companies. This gives him a huge advantage in building his relationships with the best executives, because they know he will actually be helping them find companies they’d want to be running.

And the results support this. Take Snowflake Computing as an example. After Speiser stepped down, Snowflake brought on Bob Muglia as CEO. Muglia, who was previously EVP of Software at Juniper Networks and before that President of Servers and Tools at Microsoft, was an astounding get as CEO for a two year old startup. And then last year, Snowflake brought in Frank Slootman, formerly CEO of ServiceNow and DataDomain, who may be the greatest enterprise CEO of his generation to run Snowflake.

Potential downsides of model

However, Speiser’s model is not without tradeoffs.

For prospective founders, equity is one example of this. While VCs like incubations because they are able to command higher ownership percentages, this comes at the cost of founders ending up with less ownership. For founders who would start a company no matter what and don’t feel like they need much support, an incubation model like Speiser’s will leave them with significantly less equity than they could get otherwise. Benoit Dageville, the Snowflake co-founder with the most equity, had 3.4% at IPO—less than founders often have at IPO.

For many founders, this tradeoff will be worth it after weighing how much Speiser and Sutter Hill increase the probability and scale of potential success. After all, Benoit’s stake is currently worth over two billion dollars. But for many, it may not be worthwhile.

Another tradeoff is autonomy. Just as founders and later CEOs may want Speiser’s experience and hands-on help building the company, they may regret his involvement where they differ in viewpoint. And the tradeoff of a VC being intimately involved in the business…is that they are intimately involved in the business. This is especially true where company and individual incentives may differ. Bringing Frank Slootman in as CEO, for example, could only come with the departure of Bob Muglia. I’m sure Muglia would have preferred to stay CEO, but the board decided they couldn’t pass up the opportunity to bring in Slootman. With two billion dollars in equity, Muglia may do it all again even knowing that. But there is a tradeoff in autonomy that comes with incubating a company with an investor so closely involved.

There are real tradeoffs for investors in the model as well. Incubating a company, especially as interim CEO, is significantly more work than only investing in a company. Which means an investor like Speiser can make very few investments, so the cost if any of them don’t work out is higher. By only doing incubations, an investor also misses out on being able to invest in promising companies and teams that are already formed. Finally, by primarily incubating companies, it can provide some misalignment with existing founders that may be more reticent to meet since the investor is unlikely to invest but may incubate a competitor.

Supported by structure and process

Speiser’s incubation model breaks many conventions of and assumptions about venture. They even defy the conventions of how most firms do incubations. Spending two days a week with companies consumes more time and focus than most VCs do. Speiser does fewer investments, which only works because of an implicit assumption that his companies have a much higher likelihood of success. Speiser currently has a roughly 20% hit rate of his companies achieving multi-billion dollar valuations. Comparatively, top VC firms are typically at single digit percentages. The ratio of other top firms is lower because they make more investments, but nevertheless Speiser’s hit rate is exceptional. His structure and process are integral to this. And similarly these success rates are what allow him to double down on his model. But how does he choose which spaces and companies to focus on with his scarce slots?

Underlying Speiser’s approach is a belief that ideas matter. And you can make success way more than most believe.

Technical risks and secular shifts

The recurring core of Speiser’s approach is finding a market undergoing a massive secular shift—and betting all-in on the full transition. He favors companies where the demand if it works is high—but the technical risk is high and most don’t believe it can be done yet. All investors like benefitting from market tailwinds, but it is a very different thing to bet on one well before it has manifested and to the exclusion of more conventional and existing approaches.

When Snowflake Computing was started in 2012, most investors and companies were convinced that in order to sell into large companies you had to support on-premise workloads. Hell, most customers were convinced to sell to them you had to handle on-premise. Betting all-in reminds me of something Reid Hoffman once mentioned about principles. Principles are only principles if you’d hold them even when they are costly. It is only betting fully when it comes at a real cost to the business. For example, in customers that can’t be served because they need a hybrid solution.

Ghost Locomotion, another of Speiser’s incubations, is another example of this. It’s a purely computer vision based approach to autonomous vehicles (AV).

In AV most companies take a hybrid computer vision and LiDAR approach. Google’s Self-Driving Car team and the many teams started by Google SDC alums (Aurora, Nuro, Argo, Otto) use this hybrid approach. Though LiDAR is expensive, the thesis is that for a use case as high stakes as autonomous driving it’s important to have heterogeneous sensors that can make up for each other’s shortcomings. Historically these sensor fusion approaches have outperformed those focused solely on computer vision. Of course, general consensus is that on a long enough time scale computer vision alone will work for autonomous driving. After all you and I both use a computer vision approach and are able to drive. The key question is which approach will be first to market with a system that is accurate enough.

The bet for most autonomous vehicle companies is that the progressing along the current hybrid approach will work, and that teams that have worked in autonomy will have an advantage—having seen the ceilings on performance that need to be overcome.

Ghost Locomotion, like Tesla, has a very different approach (it should be noted that Tesla also is a coupled bet on the feedback loop of large scale car telemetry data being key). They only use computer vision and machine learning—a purely software approach, rather than a hybrid hardware and software one. It is a bet that machine learning will improve at a fast enough rate to surpass hybrid approaches, or perhaps that the status quo approach cannot reach sufficient accuracy at all. It’s an aggressive view that prior domain expertise in autonomous vehicles is less important than expertise in AI and software engineering.

What approach will win in autonomous vehicles is entirely out of scope of this essay, but I point this out to say that going all-in on secular transitions is hard. It involves very real tradeoffs that will feel like they are wrong for a while and are often just wrong.

Going all-in on a market transition actually requires a more precise viewpoint on timing. Companies that do so rarely die because the market transition never happens—they die because the secular shift doesn’t happen fast enough. Understanding when the market is ready and how to help catalyze the transition is key.

Speiser explicitly seeks these secular shifts in the companies he incubates. Taking on technical risk over distribution risk. It’s the most publicly prominent feature of his investments. And you’ll see it throughout his thoughts wherever they are documented.

Snowflake Computing is all-in on cloud data warehouses. Not hybrid or on-premise data warehouses.

Pure Storage is all-in on flash storage. Not hybrid or disk based storage.

Ghost Locomotion is all-in on computer vision driven autonomous driving. Not hybrid or primarily LiDAR based autonomous driving.

Speiser’s companies take an aggressive view on transitions in the market, seeking out shifts that would create obvious and differentiated value that incumbents can’t provide, but that many don’t think will come yet. And Speiser’s companies go all in.

They are betting that the shifts will come sooner rather than later. Or perhaps more importantly, that they can be the catalyst that accelerates the market flipping abruptly towards the future.

Speiser is willing to underwrite the technical risk for years. Like many firms, Sutter Hill Ventures has enough capital to support a new company for years. But where most firms would balk, Speiser will continue to invest on that bet that the market will catch up to his view. And he has demonstrated repeatedly a willingness to continue to plow millions more into follow-on rounds of his incubations, even while they are pre-traction. Sutter Hill led the first two rounds in Snowflake, and continued to invest more money into later rounds.

Built-in rigor

As interim CEO, Speiser can bring his own rigor to the entire process from idea conception through finding product-market fit and being set up for scaling. This rigor can be seen even before a company or approach is solidified. My understanding is that Speiser meets with hundreds of potential founders and customers before deciding who to incubate a company with. This is orders of magnitude more than most VCs who meet with a handful of candidates when deciding on an incubation.

The rigor continues once the company is formed. Speiser leads the customer development process as CEO. This is key because in the early days of a startup, sales isn’t about revenue. It’s about product and market discovery, so a tight feedback loop is needed between sales and product. How to run this initial enterprise sales motion is very different from normal sales—and something few have experience in. But Speiser has lots of experience since this is the stage he repeatedly focuses on. These initial customer pilots are also much easier with a strong brand and pre-existing connections to potential customers. Through prior investments, Speiser has more relationships with potential customers to call upon and a more refined playbook for the steps of honing in on the ideal customer profile, how to structure the pilots, and more.

Most VCs fall into an uncanny valley. They won’t do work directly, so founders can’t offload work (and the cognitive load that goes with it) off their plate to their investors. But they don’t have enough context on a frequent enough basis to be able to really help shape the meta-process towards success. Speiser has both as interim CEO.

Considerations for the venture industry

Focus

Fittingly, Speiser’s approach to venture is the same as his approach to other markets: never go hybrid.

We see this in his decisions about structuring his work. Besides his investment in SumoLogic’s Series B, it appears he hasn’t made any investments beyond his incubations. There are other VCs in Speiser’s cohort with similarly impressive track records with incubations, such as Jim Goetz at Sequoia or Asheem Chandra at Greylock. But Speiser is rare in now only doing incubations.

At a first glance this appears odd—after all, there are many great companies that he didn’t incubate that his reputation could help him get access to investing in. Isn’t he sub-optimizing returns by not doing traditional investing?

The question is whether the loops of incubation and venture investing are additive to each other. In broad strokes they clearly are. After all, many of the same people who could be good founders to incubate companies with would be good founders to invest in separately, too. Much of the understanding of markets, company progress, and more are generalizable between the two approaches as well.

However, on deeper inspection they are actually not that aligned in many ways. Companies are a sequence of de-risking functional loops. Incubations are focused on the very earliest stages of these. By the time a traditional VC firm invests, companies are already set on many of the aspects that someone incubating companies must be good at.

While there is overlap in networks, in many ways there is also a tradeoff between what’s best for incubations vs. investing. By focusing solely on incubations, Speiser does not need to keep pace with the torrent of potential investments that occupies most investors’ schedules. This frees up significant amounts of time and focus.

Like a gas, the normal flow of investing can expand to fill an entire schedule. There is always a company of the week that must urgently be pursued. It’s very hard to carve out real time for incubations amid this.

While other investors incubate companies, there is little differentiation between how they handle their incubations and their traditional venture investments. Each part of Speiser’s process is tuned specifically towards incubations. From deciding which parts of the company he’ll run for first few years to the talent arbitrage. Or his process for finding founders and spaces to incubate companies around. And from changes he’s made to the incubation process over the last few years, it’s clear that he continues to work refining the process. Going forward it will be interesting to see 1) whether he can scale the number of companies he can incubate and remove himself as the bottleneck to incubations 2) if he can create more sources of value to incubated companies that compound further with every incubation he does.

Optimal firm structure is downstream of the expected value distribution of portfolio companies. If incubations have a different likelihood and magnitude of success than traditional venture portfolios, it’s inevitable the firm structures that best support them will also be different.

Visibility and brand strategy

Speiser is one of the top current VCs by returns, and after the Snowflake IPO he’ll have likely returned more than $12B in returns to Sutter Hill. That will bring the average billion dollar plus outcome in his portfolio to around 20%, and likely to rise with more companies like Sigma Computing, Clumio, and Ghost Locomotion approaching that valuation.

More important, he is one of the few with a unique and clear strategy that has clear compounding loops. Why then is he virtually unknown in Silicon Valley at large? Especially when compared relative to other investors with similar track records.

Many firms and investors rely on broad top-of-funnel brand network effects. Andreessen Horowitz’s model grows stronger as more people are familiar with Andreessen Horowitz. It’s their brand network effect that drives dealflow, name recognition, and improves their portfolios’ cost of recruiting or customer acquisition.

However, Speiser’s model doesn’t rely on these brand network effects optimized for inbound, so he doesn’t have similar pressure to broadcast his strategy and success. In fact, he avoids it.

Firms in this quadrant are most interesting to study. Firms with strategies that require mainstream awareness are much better understood—because their approach is out in the open. But firms that are successful but don’t rely on a broad brand network effect are much less understood—so there is greater potential they have discovered an un-arbitraged source of compounding returns.

This is graphic I made for myself. You can tell because it’s worse than my normal graphics. Bet you didn’t know that was possible

That’s not to say he doesn’t have brand network effects at all. Within the network of people he’d want to start companies with, he is well known—but there’s no benefit to him in being known more widely. The percentage of his meetings that are outbound is likely significantly higher than most VCs, so what matters is that if he reaches out to people they view him as credible.

Speiser’s model is far more interesting than most firms’, because it bucks the current strain of conventional venture thought. It throws our assumptions of how companies must be started into disarray by abolishing the strict Chinese firewall typically held between VCs and the operations of their portfolio companies. It believes venture firms can increase the likelihood of company success much more than people think, which runs counter to the current view of picking and access as the primary frontier of competition.

Most importantly, it understands that the function of venture is more important than its form. And it adapts its structure to best serve its purpose—improving the fundamental derisking of companies.

Future of incubation

For the most part, the venture industry is skeptical of its ability to incubate successes with any consistency. This pessimism can be seen implicitly in discussions of venture success, which revolve primarily around the knowing of and getting access to the top fundraises. And can be seen in the structure of most firms’ portfolios and follow on investments

Speiser’s success incubating, along with a few others, is important because it is an existence proof of venture’s ability to meaningfully impact companies. Over time industries tend to get overly narrow, focusing on approaches that are known to work well. This is natural, but often means that without outliers that take new approaches and perturb the equilibrium, industries can get stuck on local maxima—with no one wanting to be the first to try unproven strategies.

Speiser’s success ratio is far beyond the normal distribution of venture outcomes, and the fact that they are incubated with him as interim CEO indisputably proves his involvement is effective.

Will the magnitude of success of Snowflake’s IPO trigger a re-examination of incubations? It should. With caveats.

Speiser’s model is in stark contrast to modern norms around the optimal level of investor engagement with companies. The Chinese firewall around investor and company engagement is primarily a function of fear of investors harming the companies—whether maliciously or more often unintentionally. However, this should mean that there are high returns where there is trust between founders and investors on greater levels of context and engagement by investors.

And as a market, it’s important to have high returns on increased trust, otherwise trust becomes purely an aesthetic attribute.

More importantly, the power dynamic in tech is shifting very strongly away from investors to founders. As this trend continues and founders increasingly are less afraid of their investors, the field will increasingly bifurcate. Investors will either be less engaged and more passive (primarily contributing capital and brand) or they will engage closer with companies but be held to a higher bar of helping push progress for the companies. Over time as the leverage moves towards founders and the fear of investors being able to damage companies continues to fall, there will be more openness to new formats of investor and company engagements.

Incubations have far more degrees of freedom than investing, since investors are more closely involved in many more aspects of the company—especially at the proto-formation stage before many core decisions have calcified. If you look across the current landscape, there are very different strategies across the taxonomy of firms that incubate. Some of the investors that incubate include Asheem Chandra (Greylock), Aneel Bhusri (Greylock), Jim Goetz (Sequoia), Kevin Ryan, SciFi VC (Max Levchin’s fund), Thrive Capital, BoldStart VC, 8VC, Unusual Ventures, and many others. Just looking at this list there are many axes they all differentiate on in approach, what they think should be centralized by the incubating firm, and where they think value is generated. And the types of companies they incubate and the dynamic range of their outcomes is equally wide.

People often bucket all incubations together as a category. This is wrong as there are more variants of incubations than of traditional venture. VCs and LPs trying to do incubations without a clear viewpoint on how to press the form and structure of their approaches to best incubate will be disappointed.

In the coming year we are likely to see many moves to incubate companies. This will not be due to a change in the efficacy of incubating, but rather the shifts in market valuations—and the sharp rise in demand and valuations for earlier stage companies in certain segments.

When companies at $1M ARR can regularly raise in the hundreds of millions of dollars, the pull to invest earlier grows. Competition to invest in the companies attracting these multiples has grown so fast that investors are moving aggressively earlier and earlier in their lifecycle. In some ways this is rational as the industry has improved at inferring forward revenue predictability of companies, but in large part investors are moving to invest before all the data points on go-to-market have become clear. But as seed investing eventually also becomes very competitive and investments in new startups get marked up at high valuations, investors will increasingly look towards incubating new startups.

At some stage the constraint becomes the number of companies being founded in these areas. Incubating new companies will be one of the few ways for investors to generate proprietary deal flow.

Final thoughts

As a community, it’s easy for tech to become reflexively fixated on returns. Especially in this environment where every day brings a new company raising at astronomical valuations or going public at $75B, it’s easy to get lost in the allure of it. But lost in all of the breathless discussion is what is actually being built, what value is being added, and whether our ecosystem is improving.

Returns untethered from value creation are a temporal anomaly. Over a long enough time period, returns should accrue to where value is created. And we should be most worried when returns don’t have any tie to value created—an ecosystem with no fitness function cascades to the worst disasters. Just as companies are judged on their net present value of future cash flows, so too should we judge organizations on the net present value of their future value added.

Have you ever seen a sadder portrait of the rise and fall of empires than this drawing?

It’s easy to judge VC firms on returns. But value created above replacement is the much more interesting and leading metric, especially from a founder’s perspective. If the Ghosts of Christmas removed a VC firm from existence, how would all of its now former portfolio companies fare? Would they have raised from another VC firm and been in the exact same place? Or would they not even have existed?

At an ecosystem level this same principle applies. Companies and firms push progress by taking novel approaches that others can all learn from. Increasingly firms have converged on structure and strategy—a local maxima. Few firms perturb the equilibria. Often they fail. But it’s the pursuit of new approaches to every aspect of company building that perturb the equilibria and push progress. It’s these aspects of companies that create positive externalities for everyone.

Speiser’s approach breaks from venture convention. And with Snowflake’s recent IPO, it’s getting attention that’s long due.

In many ways, Speiser’s playbook reminds me of TikTok. The most beautiful aspect of TikTok’s business model is how every aspect of it is aligned with its network effect. Its short-form video format decreases friction and maximizes volume to feed into its network effect. Its algorithms favoring of shares and full watch-throughs are core metrics for engagement and distribution. Its duet and reply videos turn every aspect towards the creation of new videos. Its sounds, memes, and dances are all user-generated social capital marketplaces. It’s glorious to see a company willing to rethink everything to best make its form fit its function.

Speiser’s incubation model shares that intentional design and craft. Over time, each aspect of his model has been shaped to better serve originating companies—rather than just repurposing the traditional venture model for a new function.

And it’s not lost on me that similar to the talent arbitrage I discussed Union Square Hospitality Group having in Aligning Business Models to Markets, Speiser also has one in attracting EIRs and CEOs.

Among my friends who admire Speiser’s model, one question hangs over us all. Is Speiser’s model repeatable, or is Speiser unique? Can his playbook be improved and scaled, or is it a craft that is fundamentally artisanal? Have we found a new frontier of Coasean logic to company formation that will become mainstream with time, or a single bloom that will vanish with him?

There’s reason to be optimistic. While Speiser feels like an outlier in venture, there are a number of individual partners that have incubated companies with models that feel very similar to great success. It is the intentional refinement of structure and process that seem unique—rather than the entire thesis.

Over the coming years we’ll hopefully see many investors evolve their own perspectives on Speiser’s approach and execute on it with their own modifications. There are many iterations that I’m particularly interested in seeing executed.

Of course, what excites me most about Speiser is that he is not done. In recent years, it’s clear he’s continued to push experiments on how to further refine the incubation model: how to scale it up while centralizing and compounding the value the firm provides and how to use the unique advantages of his model and financial returns to provide a more compelling package to founders while building in defensibility. And of course, also failed experiments in where the model can be expanded to.

I love what Speiser has done and continues to work toward. The true next dream in my opinion is scaling and systematizing his playbook beyond being constrained primarily by his individual effort. This will be hard—perhaps even impossible if the core of his success turns out to be his connections or some ineffable sense of taste.

But if it’s doable, it would be one of the most important developments in Silicon Valley and tech. His model is a better abstraction layer and structure for creating successful companies and it’s a process that compounds in how effective it is with every company. It’s ultimately a better way to truly drive innovation faster, rather than being merely a tollbooth on innovation.

Acknowledgements

Many thanks to Keila Fong, for the many discussions about this topic and help with this piece.

Additionally, thanks to Max Bulger, Michael Dempsey, Casey Winters, Saam Motamedi, and Zach Brock, for their discussions, edits, and help with this piece.

Why Figma Wins

Companies are a sequencing of loops. While it’s possible to stumble into an initial core loop that works, the companies that are successful in the long term are the ones that can repeatedly find the next loop. However, this evolution is poorly understood relative to its existential impact on a company’s trajectory. Figma is a prime example of sequencing loops. They’re now widely viewed as successful, but the key factors in their success and what bets they must make to get to the next level are less widely understood.

The core insight of Figma is that design is larger than just designers. Design is all of the conversations between designers and PMs about what to build. It is the mocks and prototypes and the feedback on them. It is the handoff of specs and assets to engineers and how easy it is for them to implement them. Building for this entire process doesn’t take away the importance of designers—it gives them a seat at the table for the core decisions a company makes.

Building for everyone in the design process and not just designers is also the foundation of Figma’s core loop, which drives their growth and compounding scale. That network effect is made possible by Figma’s key early choices like:

Architecting Figma to be truly browser-first, instead of just having storage be in the cloud
Their head start in new technologies like WebGL and CRDTs that made this browser-first approach possible
Focusing on a product purpose built for those designing vector based digital products

Figma’s compounding growth is not only due to product market fit, but is also driven by the alignment between their product and distribution. There are limits to Figma’s success if it remains only valued and spread within companies. In order to break through that asymptote, Figma must build a global network effect across the ecosystem. Figma’s value to new users should compound as Figma’s adoption grows, even for solo users outside of organizations. Figma has begun making its bets on how it will become a platform—namely centered around communities and plugins. While it’s still early, these bets can be unpacked and understood.

Many companies are now at this inflection point. They have found success with their core product and are trying to push themselves to the next level of value to customers and scale. Our understanding of building platforms and sequencing towards them is still nascent. From how to shift the allocation of resources between a company’s core business and its potential future expansions to how to structure a platform to catalyze its growth and more. Until the playbook is well understood, it is more art than science. There are many decisions to make, including which layers should be centralized, whether the ecosystem should be driven by open source contributors or profit-seeking enterprises, how broad in scope to allow the ecosystem to grow and where to not let it grow, and more.

Whether they can catalyze compounding productivity in design is a core question for the coming years. Engineering is one field that has exhibited significant compounding progress over time. Whether Figma’s bets in building out global network effects will be able to push this level of progress in design will be an important question to watch.

The Arc of Collaboration

When Figma first launched, its value proposition was primarily around making design collaborative. If design could happen in the browser, then designers could work together on the same projects. In fact, they could even work on a design at the same time.

In 2014 as I helped diligence Figma (disclaimer: I worked on Greylock’s investment in Figma, but I don’t have a personal stake in Figma. sadly.), I used to sit with designers at startups and watch them work. The top right corner of their screens were always a nonstop cycle of Dropbox notifications. Because design teams saved all their files in a shared folder like Dropbox, every time a coworker made a revision they would get a notification. And often there were complex naming conventions to make sure that people were using the right versions.

Figma solved this problem. Designs in Figma are not just stored in the cloud; they are edited in the cloud, too. This means that Figma users are always working on the same design. With Dropbox, this isn’t true. The files may be stored in the cloud, but the editing happens locally—imagine the difference between sharing Word files in Dropbox vs. editing in Google Docs.

Any time a user edits a design, they are in effect checking out a temporary copy. This is why two users editing a file simultaneously creates issues. When designs are edited with Figma, there are no conflicts. And since revisions are a first party feature of each design in Figma, there’s no need to have complex files with names like “profile_design_v23_final_draft2”. Similarly, designers can comment directly in each other’s files, instead of sending emails with feedback.

the one with less loops is better. I know, it’s the first time I’ve ever said that

I used to be confused by the Figma’s team consistent framing of Figma as browser-first. What was the distinction between this and cloud-first? Over time I’ve come to see how important this distinction has been. When many creative tools companies talk about the cloud, they seem to view it as an amorphous place that they store files. But the fundamental user experience of creating in their products is done via a standalone app on the desktop. Figma is browser-first, which was made possible (and more importantly performant) by their understanding and usage of new technologies like WebGL, Operational Transforms, and CRDTs.

From a user’s perspective, there are no files and no syncing that needs to be done with others editing a design. The actual *experience* of designing in Figma is native to the internet. Even today, competitors often talk about cloud, but are torn over how *much* of the experience to port over to the internet. Hint: “all of it” is the correct answer that they all eventually will converge on.

Designers loved Figma and this drew initial distribution. And with features like team libraries, designers have incentive to pull in other designers on their team into Figma. But a tool designers love, while a prerequisite for success, does not fully capture the root of Figma’s traction so far. While Figma has been building the ideal tool for designers, they’ve actually built something more important: a way for non-designers to be involved in the design process.

Tools for Design, not just Designers

While Figma was building better tools for designers, their browser-first approach had a much more radical and significant impact on non-designers.

We often forget that the purpose of the tools we use at work isn’t to increase our individual productivity, but the entire team’s productivity. Companies themselves often forget this.

The best tool for individuals may also be the best for the entire team. But it’s the latter that matters. And beyond some level of enhancing individual productivity, it’s the team aspects that increasingly matter. Word processing is a good example of this. There used to be lots of experimentation and customization around individual features like typesetting. But once this was good enough, the focus has shifted towards collaboration. And for most use cases, few care about typesetting today. Increasingly our tools must understand and align with how we collaborate. This was less important when collaboration was logistically difficult and prohibitively costly, but as collaboration becomes easier its importance has risen. People’s work is less siloed—and their tools must reflect this.

The best tools enable collaboration that was previously unthinkable.

Historically it has been very difficult for non-designers to be involved *during* the design process. If PMs, engineers, or even the CEO wanted to be involved, there were many logistical frictions. If they wanted the full designs, the designer would need to send them the current file. They’d then need to not only download it, but also make sure they had the right Adobe product or Sketch installed on their computer—costly tools that were hard to justify for those who didn’t design regularly. And these tools were large, slow, specialized programs that were unwieldy for those not familiar with using them. It was hard to navigate a project without a designer to walk you through it. Comments were done out of band in separate emails. Even worse, if a designer made an update before viewers had finished looking at the file, the file would be out of date—without the viewer being aware.

The experience was just as bad for designers. Even if they wanted their PMs and engineers to give feedback, they’d need to handhold them through the process. Designers would have to export the designs into images, send the screenshot, and later figure out how to translate the feedback into changes in the actual design. The feedback loop was so slow that they’d need to pause their process while waiting for feedback.

Of course, what really happened is that most of the time non-designers just didn’t engage as much with the design process. The pain of reviewing designs wasn’t the primary problem, but there was enough friction that reviewing often never happened.

Figma fixed this.

Sharing designs with Figma is as easy as sending a link. Anyone can open it directly in their browser. It’s as easy as going to a website, because…well…it literally is going to a website.

Once they have the link, non-designers will always have the latest design. They can comment directly in the designs, without disrupting the flow of designers. And with collaborative editing, they can talk through changes in a meeting and watch as they are implemented in real time.

These technical changes are kindling for a far more important social norm shift. Figma makes it possible for non-designers to be part of the process earlier and throughout it all.

Figma made companies realize that non-designers should and could be more involved in the design process and how crazy it is that other design tools aren’t built with the experience of and interactions with non-designers in mind.

Tightening the Feedback Loop

Figma allows closer collaboration between designers and non-designers, but the second order impact of this on the social norms of teams is far more impactful. Historically, there has been so much friction in the design process that design is brought in after most decisions have been made. And conversely, there is a limited set of changes non-designers can push for once the design is set.
Tightening the feedback loop of collaboration allows for non-linear returns on the process. Design can be drafted simultaneously with the product, allowing feedback to flow in both directions throughout the process. Aligning the assets used by design and engineering allow more seamless handoffs, and allows for more lossless and iterative exchange.

Designers can get feedback continuously from engineers or PMs on their team. Some will have sensitivities about non-designers sticking their nose too deep into the design process. In some cases they might be right, but this cost pales in comparison to the benefit to designers.

Bringing non-designers into the process is what gives designers a seat at the table of product and business decisions.

For too long we’ve systematically siloed functional areas like design, sales, and customer service. But modern companies are internalizing that if their core loops are truly an iterative process, then functions like design must themselves be part of the feedback loop of the company. Design decisions cannot be entirely abstracted from the rest of a business—they are as intertwined as the decisions made in product and engineering.

Historically, design has been opaque as a business unit due to the logistical and technical difficulties of making the design process legible to others. But as these hurdles are increasingly solved by companies like Figma, we’re seeing teams navigate how to best integrate design with the rest of a company’s processes. This should be no surprise, as we have seen the same arc play out in engineering over the last few decades.

Means of Ascent

Much of Figma’s current success is driven by its ability to spread within companies. Figma becomes more useful as more people within a company use it, driving advantaged speed and scale of penetration within companies.

Figma was quick to recognize that the constraints on design at companies is often not a problem of pixels, but of people.

Many of Figma’s competitors are great tools for designers. But that’s who they are for—designers.

Figma is a tool for teams to design. Not for designers alone.

now you’re all stuck inside the orange box with me

By bringing both designers and non-designers alike into Figma, they create a cross-side network effect. In a direct network effect, a homogenous group gets more value from a product as more of them join. In contrast, a cross-side network effect involves two (or more) distinct groups that grow in size and value as the other group does, too.

Figma’s cross-side network effect between designers and non-designers is one of the primary and under-appreciated sources of their compounding success over the last few years.

As more designers use Figma, they pull in the non-designers they work with. Similarly, as these non-designers use Figma, they encourage the other designers they work with to use Figma. It’s a virtuous circle and a powerful compounding loop.

Cross-side Network Effects

Cross-side network effects often get less attention than direct network effects—partially because our vocabulary around network effects is less robust than it should be, but primarily because they are typically viewed as being specific to marketplaces. While it’s true that cross-side network effects are most commonly seen in marketplaces, it’s wrong to think that they only exist in marketplaces. Supply and demand is the most famous of cross-side network effects, but not the sole source.

Figma’s direct network effect among designers helped it grow early on, but has limits on its ability to help Figma spread. The set of designers that a designer works with directly is a limited set—and unlikely to change often. While they may spread it via word of mouth and social referral to many other designers, the network effect has an asymptote on distribution.

Figma’s cross-side network effect offers an additional vector. Designers who use Figma share their designs with engineers and PMs, introducing them to Figma. As these non-designers learn to appreciate Figma, they then evangelize it to other teams of designers they work with on different projects. These cross-side network effects jump across teams and help Figma metastasize throughout entire organizations.

more loops is better. Phew, all is right with the world

This impacts monetization and purchasing at companies. Paying for a new design tool because it has new features for designers may not be a top priority. But if product managers, engineers, or even the CEO herself think it matters for the business as a whole—that has much higher priority and pricing leverage.

Product-Distribution Fit

It’s this cross-side network effect that has most shaped Figma’s growth. Like planting seeds, Figma can start small with a scattering of individuals using it at each company—but each of them can lead to serious expansion throughout their companies. Once Figma is adopted by a few designers, all their colleagues across departments get exposed to the product and especially its collaborative features and see how much better the experience is for them. They then encourage its usage and adoption across their other teams and projects and so on.

This is what gives Figma the compounding growth rate it has. While it took Figma some time to ramp up its growth, there is now a predictability and compounding that is impressive.

The core of Figma’s product is the core of its distribution loop. That is incredibly rare and powerful. True alignment between product and core distribution loops is what’s needed to catalyze outlier ability to compound in growth.

Like many of their peers in this generation of companies, Figma has developed a two-step sales motion: landing and expanding via a bottom-up, product-driven approach, then doing top-down sales once usage of the product has metastasized. Preparing for this entails a whole slew of enterprise features, building up the sales machine, and much more. Over the last few years Figma has begun to build these out, though there is still a lot of work for them to do here.

This new type of enterprise companies is a mix of both product and growth-driven consumer and sales-driven enterprise. We are still early when it comes to companies understanding how to re-architect their org structure, GTM motion, pricing model, and more to best fit this model. And like SaaS before it, this bottom-up to top-down model will mature from art to science over the coming years. Though Figma has significant work to do to make their traditional enterprise sales process robust, it would be disappointing if they stopped at simply recreating the traditional enterprise sales process. Practitioners are starting to understand that these sales loops can be understood in the same way growth loops have been and that there are ways to drive significantly advantaged sales velocity and scale via product and ops. Figma is one of the companies on the frontier of this, and there is an entirely different series of essays to be written about the maturation of this nascent field.*

Building an Ecosystem

Where does Figma go next?

There is always more to improve in the core product. But there comes a time when companies need to start thinking about the next sequence of their growth.

The competitive landscape has also matured. Competitors are beginning to copy and encroach on Figma’s core strengths. As they converge on Figma’s views on the space, they pressure Figma to push forward. Sketch, for example, has shifted its pricing model to subscription and oriented around teams and has focused on moving more of the product into Sketch Cloud. In the last year, they’ve gone from being entirely bootstrapped to raising a venture round from Benchmark. This capital and support has fueled a more aggressive move in Figma’s direction with a strong focus on collaboration, especially bringing Sketch to the browser and building team collaboration.

Adobe has made similar moves. Though powerful and widely used, Photoshop and Illustrator were not particularly specialized products for designers of digital products. In 2019, they launched Adobe XD as a direct competitor to Sketch and Figma.

Figma has been focused for the last few years on its value and spread within companies. Figma’s next challenge is to improve its value and spread across companies in the ecosystem at large.

Global Network Effects

There are cross-side network effects that drive compounding value within companies using Figma. But how does it spread to new companies?

Given Figma’s growth rate, it clearly does, but in less consciously compounding ways. Many people work across companies, especially agencies, and spread Figma to their clients. Similarly, when people leave their jobs and join new companies, they bring Figma with them. And of course, word of mouth helps spread it. These are all effective and amplified by Figma’s intrinsic collaborative benefits, but they operate on their own natural cadence.

Thanks to Figma’s hard work, the value in signing up is much higher for someone whose company already uses Figma. There are already teammates to collaborate with. There are assets and design libraries specific to their company. There are components that their team has built that they can reuse.

But for users who don’t have co-workers using Figma, none of this value exists. They have the single-player benefits, but no direct increased value from the hard work Figma has done to add so many new users over the years.

life is just an endless pursuit of trying to draw charts that go up and to the right

Figma’s challenge is to create not just local network effects within companies, but global network effects that make Figma more useful to all users as it grows in scale. There are many directions Figma could expand its scope*, but our focus in this piece will be on the directions they have already begun to take.

say it together now: more loops is better!

In 2019, Figma began to plant the seeds for what their ecosystem loops across companies will be. With their launch of Figma Plugins last August and Communities more recently, they are starting to push the frontier of collaboration and productivity across companies.

this is my best graphic. It’s all down and to the right from here

Empowering Creativity through Communities

Communities push their network orientation further by allowing users and companies to publicly share their designs.

Historically, when sharing designs on sites like Dribbble, it’s often only the output image file that’s shared. It’s hard for others to see and use the full design themselves, including the layers and components that went into it. Even if the actual design is shared, the friction of opening it in whichever program and resolving any dependencies is non-trivial.

Sharing Figma designs removes this friction. Anyone can instantly open a design and start using it for their own projects. This enables users to frictionlessly become creators too, not just consumers.

Some designers have shared their UI kits, components, and design systems using Github. This has the right idea, but Github isn’t meant for design. Forking a repo may be frictionless in engineering—but it is not for designs. The designs must still be downloaded and loaded into your app. For design, Github is more akin to a hosting site for downloading files. In many ways Figma’s Communities are a reflection of Github’s philosophy and intent, but built with design in mind. Duplicate a shared design, and a copy is instantly saved to your workspace and ready to be edited.

This frictionless process makes it easy for people to share and build upon each other’s designs. But perhaps more importantly, including the entire design is a better abstraction layer that allows for new benefits.

Instead of just receiving the final output, recipients can see both the underlying components and assets within a design as well as more complex interactions and animations around designs. A great example is this design shared by Figma’s design director, Noah Levin, of smart animations within Figma. By sharing the entire design with the community, others can not just see the animations—but play with and tweak the actual designs and animations directly. This makes it easier for them to learn how to use smart animations, or even just copy parts of Levin’s demo into their designs.

The Promise of Plugins

I’ve been using Figma to create very niche memes to send friends. Figma is great, but as I’ve gotten more particular I’ve begun to add colored rectangles (with rounded corners because I’m not a barbarian) behind any text to make it more readable. This is a small but annoyingly rote series of tasks to do. Recently, I discovered the plugin Substrate for Text which simplifies this exact process. Now I can just select the text, activate the plugin and this text background gets auto-generated instantly. This plugin is made by a designer, Andreslav Kozlov, who lives in Russia. On the other side of the world, Andreslav had felt the same problem I had, built a plugin to solve it, and shared it with the internet. And that improved the power of Figma for me, making it far easier for me to immediately spin up memes mid-conversation with friends.

though maybe he should also be blamed for the increased speed with which I can troll my friends now

this isn’t how this meme is meant to be used either

This is a small example of the promise of plugins. Figma’s plugins make it extensible so designers can augment their workflows, be empowered with new abilities otherwise impossible, and easily share them with others.

i hope you appreciate the precision of my squiggles in the productivity curve

Most Figma plugins today simplify tasks that would otherwise be very repetitive or painstaking. Today, companies can build private plugins to address their specific needs. For example, companies have built plugins that automatically generate dark mode designs, pull in external data and lint for common design errors, or make it easy to generate design assets in the right orientation. These plugins level up their entire teams.

The real power of plugins, however, is in making them publicly available across the ecosystem. Plugins are collective progress available to all users. Whether creating charts, custom maps, pulling data into your designs, redlining for engineering handoff, or random blobs, plugins leverage up designers’ productivity. And plugins like Figma Chat show that the frontier can be pushed out even further by enabling entirely new abilities for designers.

As companies scale, it becomes harder to sustain consistently increasing value to customers. With more customers, it becomes harder for one company to address all the unique use cases and needs. Companies must also increasingly service less ideal customers as they expand beyond their core audience. This is especially true for users who don’t have all the compounding benefits of working with a team that all use Figma.

your local and global utility curves should look like synchronized divers. That’s what everyone says right?

Plugins help Figma fight this drag. As they scale in users, more plugins will be created, making the product better for new users and spurring more designs to be created.

Architecting a Platform

Companies like Sketch have shown the importance of a robust plugin ecosystem (and of course, Adobe before them has a long track record of encouraging plugins). It’s impossible for a single company to build all the features and tools needed by each user. Platforms are needed most when the diversity and scale of use cases is larger than can be built—or often even understood—by the company. Sketch’s plugins allow the value to Sketch customers to grow at a faster pace than Sketch can ship new features. Figma will also likely stay focused on a generalized and core set of features, leaving a huge surface area of user workflows for plugins to address.

this graphic will probably get its own essay

Companies are still early on in understanding the nuances of becoming platforms. More and more companies are reaching the scale and dynamic range where this is a priority, but for the most part, they are individually recreating the wheel. In a decade, there will be clear frameworks, metrics, and supporting ecosystems for building platforms; today there are few. This is a natural maturation, and one that all business models go through, such as SaaS and subscription over the last decade.

Because of our lack of shared vocabulary around building platforms, it’s hard for many to understand the subtle but important delineations between the approaches of different companies. It’s easy to assume, for example, that all plugin systems are built the same, but this impulse is often incorrect.

Sketch: a Case Study

Take Sketch’s plugin ecosystem. The API is well documented and the plugin coverage is high. However, plugins are outside the scope of the core product. Users installing a plugin are most commonly directed to the Github page of the plugin, which they must manually download and install. This is another example of the gradients of what it means to put your product in the cloud. Even if Sketch is in the cloud, its plugins are local files. There is friction in downloading and installing them. This is compounded in a work setting as teams must manually make sure employees are using the same plugins if needed.

Because plugins are not first-class citizens in the Sketch product and handled out of its scope, plugin management is very decentralized. Plugins can register to be listed on Sketch’s site and enable automatic updates, but otherwise Sketch is very hands-off. There is no official Sketch source for how popular a plugin is, nor do they have to approve plugins. Instead, users must rely on Github stars or reviews on third party sites. There is no oversight on plugins causing performance or stability issues.

This shouldn’t be mistaken for criticism. The weaknesses in Sketch’s plugin architecture show only because of how successful they have been at encouraging a robust community of plugins around Sketch.

Platforms are emergent ecosystems, more akin to building a consumer social network than a traditional enterprise sales company. This is one of the core dilemmas of building platforms. They are complex organic systems that have to be carefully cultivated (gardening vs. engineering mindset) and it’s hard to predict beforehand what direction they will go and the scale they will achieve.

Sketch cannot be faulted. To this day it’s still unclear how ambitious plugins can and should be. By defaulting to a relative hands-off approach, they allowed the community to flourish unhindered by them. And it’s by seeing Sketch’s success with plugins, as well as their struggles, that others like Figma and Adobe XD have been able to have increased confidence in the importance, potential, and levers of a plugin ecosystem.

But while Sketch cannot be faulted, their choices have stunted the full potential of plugins built around them. Sketch understands this. They are rearchitecting their plugin system right now to be fully in the cloud, which is a necessary step in the right direction.

Complex systems do not absolve companies of their need to make clear choices about the architecture, policy, and norms of their platforms. If anything, they magnify its importance. The structural choices made by companies ripple forward as the ecosystem emerges around them. And the direction and scale of the resulting platform are a function of the physics set by their initial conditions. We shape our abstractions, and thereafter they shape us.

Figma: Forming Foundations

Figma’s plugins are very early, but promising. Natively built in and browser-first, when you click to install a plugin, it’s available instantly. There’s nothing to install after all, just access privileges to activate. That’s magical.

Like all good magic, the work to make this feel effortless is quite arduous. Figma’s plugins must be secure, performant, and stable, especially since they are creating a plugin ecosystem for a browser-first system. Users should be able to trust that using plugins won’t expose them to security risk or hurt Figma’s performance. And both developers and users should feel confident that the APIs the plugins depend on won’t be suddenly deprecated or broken. Without these preconditions, plugins will always be at best a small aspect of Figma. This trust and stability is the bedrock of a strong ecosystem.

i think the takeaway is that plugins are like rainbow cake?

This difficulty is best seen in Figma’s engineering blog posts on building their plugin system. Within a month of their post on how they decided the architecture for plugins, they learned of vulnerabilities in their approach and had to switch it out.

Ensuring the platform can be trusted is not just a matter of technical architecture. Figma doesn’t just host plugins, they also have a centralized approval process, more similar to Apple’s app store than to Sketch’s approach. Plugins that want to be listed must pass Figma’s policies around safety, business, usability, and legal.

The Path to Platforms

The business policies are worth noting in particular. While safety, usability, and legal are about maintaining the integrity and trust of the platform, Figma’s business policies are about shaping what they think the plugin ecosystem should look like. For example, they allow monetization but prefer plugins be accessible to all users. Choices like how much to encourage an ecosystem of plugin businesses vs. a more open source community are important ones. There is no obviously correct answer, and it can and often needs to change over the life of a platform. One need only look at the makeup of Uber drivers, WordPress plugins, or Airbnb hosts over time to see this.

Building platforms requires many hard choices like these. How should you balance encouraging growth today and building towards the ideal long term vision? To what degree should platforms influence which plugins get built or even which they should build themselves in the early days? How should you decide which features actually should be part of the platform itself vs. standalone plugins? How wide is the scope for what plugins can build? These are only a few of the core questions.

Perhaps Figma’s most interesting choice is the heavy focus on ease of plugin creation. Figma’s plugin system is explicitly designed to turn designers into developers by enabling them to create plugins for their own workflows. In most platforms and marketplaces, the ecosystem tends to split and professionalize over time. This is the natural gravitational pull of ecosystems*. To bet that a significant amount of the platform’s leverage will come from individuals improving their own workflows is a bold one. It’s a bet on induced demand, which is always the most interesting type of bet.

Figma’s plugin ecosystem is very nascent. From their list of supported features and what’s to come, it’s clear that there is still a long way to go to open up their platform to more advanced plugins. And choosing the right abstraction layers for the plugin ecosystem is crucial. So far they have adopted a very strong stance on what the core technical decisions and promises around safety, performance, and stability need to be, but have been very hands-off on what plugins are built. This is to be expected early on. When it’s unclear what should be built, it’s not a bad policy to see where the creativity of the community leads. It often brings really amazing stuff, like this demo of a plugin. Over time, however, we should expect to see more focus. Right now their plugin page is unsorted other than popularity and some that are featured. This works when the number of plugins is low enough, but eventually they will have to decide how to categorize plugins and what they want discovery to look like.

As plugin categories begin to crystallize, Figma will need to form crisp views on which areas should be absorbed into their core product, what they want each category to look like as it matures, what essential plugins don’t yet exist that they must help catalyze, and what new APIs they should allow plugins to address. The choice of how much to encourage monetization of the plugin ecosystem, as discussed above, is a perfect example of the kind of key decisions Figma will need to make (repeatedly) as they build out their plugin platform. Perhaps most importantly, Figma must decide the meta-framework by which to make these decisions intentionally rather than capriciously.

Pushing Progress

Has design been improving? Are we getting better at designing, not as an art, but as a functional practice?

The answer is certainly yes. We have tools that would have been unfathomable a decade ago, much less pre-computer. It is easier to design. It is easier to begin designing. Design is more scalable.

But how does design’s rate of improvement compare to the rate at which engineering has been improving as a process? Design as a meta-process is less impressive by this benchmark.

Engineering is almost unparalleled in the rate at which it commoditizes itself and pushes the frontier of progress out. The best practices in frameworks, languages, and infrastructure are always rapidly—and sometimes tumultuously—evolving. What used to take entire teams to build before, requires fewer and fewer people every year.

this is the true up and to the right curve. and the one that needs to be figured out for every discipline

As disciplines evolve, they figure out the social norms needed to operate better, build tools that can be shared across the industry, and invent abstractions that allow offloading more and more of the workload. They learn how to collaborate better, not just with each other but with all the other functions as well. Disciplines are not an end to themselves; the degree to which they contribute to the larger organizations and ecosystems they are part of is the final measure of their progress.

Design appears to be inflecting in the direction of engineering. Figma is in pole position to drive this evolution. As a tool, it makes designers both more efficient and more collaborative by breaking down the walls between design and the other teams they work with.

But Figma’s true potential is if it can make the transition to becoming a platform. If Figma can, they’ll push progress in design as a discipline.

Which companies are successful in a field is decided by many factors, not the least of which is a good measure of luck. But when disciplines undergo tectonic shifts, the companies that thrive have outsized influence. The choices they make in abstraction layers, social norms, architecture, and more have large ripples for a generation. This is even more true for platforms, whose loops become core to their ecosystems. Like wet clay, the choices they make eventually set and become the underlying substrate that defines how the entire ecosystem grows. That is both a tremendous opportunity and responsibility for the companies, like Figma, that take on this mantle.

Acknowledgements

Many thanks to Keila Fong, for the many discussions about this topic and help with this piece. As well as unceasing pressure to publish it.

Additionally, thanks to Max Bulger, Michael Dempsey, Kane Hsieh, Boris Jabes, Dave Petersen, Ryan Petersen, Kevin Simler, and Eugene Wei for their discussions, edits, and help with this piece. John Lilly, who led Greylock’s investment in Figma, deserves most of the credit for seeding all my views on productivity and collaboration. His investment memo from 2014 is still prescient in ways that took me years to fully appreciate.

Thanks also to Casey Winters and Brian Balfour. Building the Advanced Growth Strategy course was the origin of many conversations about Figma’s loops. And I still teach the Figma case study every semester.

All graphics in this piece were created with Procreate and Figma. Procreate is a fantastic drawing app for iPad. If you have made it all the way through this essay and don’t know what Figma is then I don’t know what to tell you. An integration between these two might have a target audience of only me. But I would love it.

* Further pieces to be written on these subjects

Underutilized Fixed Assets

Every marketplace is unique. But every successful marketplace is unique in the same ways. Historically it’s very hard to find a successful marketplace that wasn’t built on an underutilized fixed asset.

Airbnb is a canonical example of this. Someone has a guest bedroom. It’s always there, a fixed asset. But it’s unused and they make no money from it. Until Airbnb, they may not even have considered that they could make money from it. And then suddenly, with Airbnb it generates hundreds of dollars for them. It’s like an ATM they didn’t know existed in their guest room.

What Exactly are Underutilized Fixed Assets

To understand underutilized fixed assets (UFA) is to understand each component of the term.

Fixed vs Variable Assets

Fixed assets are those where the cost of them is constant, and independent of usage. If you buy a pan, it is a fixed asset. You have already paid for the pan. Whether you use it once or a hundred times, the total cost does not change. Having gasoline for your car on the other hand is a variable asset. If you don’t drive at all, you will not spend any money on gasoline. But the more you drive, the more you will have to refill your tank and by extension pay for gas.

This difference between fixed and variable assets is flipped when you look at their costs on a per usage basis. Variable assets are relatively constant in their costs per usage. While the more fixed assets are used, the cheaper their cost per usage falls. Usage of fixed assets is amortized across all usage of them, so they have a natural economy of scale.

There’s a third and more important way to view fixed assets. They can be viewed as assets that are already paid off. Any incremental uses of them are functionally free. Unlike variable assets, where incremental usage always still carries a cost.

Underutilized vs Fully utilized assets

Assets have a maximum amount they can be used. Both in terms of the frequency with which they can be used as well as the total number of times they can be used. There can be significant variance between two similar assets on their maximum utilization or even disagreement about what is a reasonable benchmark to use for max utilization, but those are in-the-weeds details. Underutilized assets are those that are not used very much, while fully utilized assets are those whose usage is close to the maximum possible.

Why are Underutilized Fixed Assets important?

Underutilized fixed assets are things with fixed costs that are not being used as much as they could be. They are important because they *can* be used more, and from their owner’s perspective all additional usage is free.

Unlocking early markets

The cross-side network effect of marketplaces is incredibly strong, but equally difficult to create. Convincing both sides of a market to to join on the promise of the other side being there is a constant struggle. And simultaneously building both sides is significantly more difficult than being able to focus on just one side of the market.

In the early days, many marketplaces have found an underutilized fixed asset to be an incredible boost to expedite building the supply side of their market.

The best way to think of underutilized fixed assets is as pure potential energy sitting in people’s homes, cars, and random tchotkes. Marketplaces take a tremendous amount of energy to get their flywheel spinning. But it’s easier when there is an external supply of potential energy that can be put to work.

Preferred Pricing

Unlike businesses, most people with underutilized fixed assets baseline their value at zero. Any money these assets turn out to be worth is money they found lying on the floor that they’re happy to receive.

This lets a marketplace bring on supply at a much lower cost of acquisition than expected. And these savings can be passed along to consumers as well.

Latent supply

Once a new underutilized fixed asset is identified, a startup can grow rapidly because there is so much latent supply of the asset initially sitting unused.

This supply tends to be mostly retail. It’s more fragmented sources of supply, with people who are less price sensitive since it’s not a business to them.

One downside with more fragmented supply is the complexity in bringing them online, making them legible, and handling logistics. This tends to work in marketplaces’ favor, however, as it’s the exact type of complexity that software is particularly good at, and allows them to unlock the underutilized fixed asset.

One way to look at marketplaces is a series of supply and demand acquisition elasticity curves. Finding a good underutilized fixed asset is a surefire way to bend the acquisition elasticity curve for a while.

Burn the Bridges

One under-appreciated advantage of underutilized fixed assets is that because they are a finite and arbitragable source of supply, it’s hard for new competitors to replicate once they’ve been discovered and tapped. It’s hard for a new Airbnb to emerge because people with empty bedrooms no longer are unsure what to do with their empty room–they use Airbnb. So a new competitor can try to go after these same potential listers, but they have to compete against Airbnb, rather than on the hosts doing nothing with the bedroom.

Case Studies

For many marketplaces the early patterns are the same.

Airbnb

Before Airbnb, hotels were the primary option available to consumers. Yes, there were some short term house rentals or communities like Couchsurfing, but these alternatives had low liquidity and trust.

This wasn’t due to lack of empty bedrooms available in cities. Every day thousands upon thousands of empty bedrooms and homes are unused by their owners. But these travelers and home owners never used to connect. There was no way for them to find each other, and even if they did, they would not trust each other.

Airbnb bridged this divide. They brought both sides online. They built trust into the platform by handling payment, customer service, and discovery, as well as by building up reviews and photos for each listing.

All of this made it possible to list people’s empty bedrooms and other underutilized fixed assets like this. These listings were often cheaper than hotel rooms since listers had already assumed the costs of their empty bedroom. They are also better positioned, neighborhood-wise, compared to hotel rooms.

Uber

Before Uber and Lyft, taxis existed to varying degrees in each city. Mobile ordering and dispatch made UberBlack originally possible. But it wasn’t until Lyft that driving was opened up to anyone with a car. Lyft’s main insight was that mapping and routing apps like Google Maps and Waze commoditized local knowledge in drivers. This made it possible for anyone to become a driver, massively expanding the pool of drivers to anyone with a car.

Ebay

Ebay was the first of the online marketplaces, and perhaps the most successful. They raised a total of $7M before going public as an already profitable company. Before Ebay, if you had random stuff in your house you either tried to sell it locally, perhaps at a garage sale, or just let it pile up in your house. By making it easy to list, review and discover items online, Ebay brought liquidity to their marketplace and made it possible for people to sell all the random unused things in their house.

New Startups

For every marketplace, in their early days they should be thinking about potential underutilized fixed assets that make sense for their platform.

For example, Hipcamp helps consumers find campsites to rent. Hipcamp adds supply by getting private landowners to list their land, and for many of these people their land was literally sitting fallow before Hipcamp made it possible for them to share it with others.

The Sequencing of Marketplace Supply

There’s something beautiful about retail marketplaces: two sided markets where both sides are just random average users.

They never last. So there’s a wistfulness to watching them, frozen for just a moment.

Look at all marketplaces as they scale, and you eventually find that the percentage of their revenue that comes from retail users on the supply side falls. Power sellers dominate Ebay. Uber is increasingly full time drivers doing it as their full-time job. Airbnb is increasingly full of individuals and now companies that buy properties and convert them into ideal Airbnb listings.

I’ve learned to draw area charts. This changes everything.

A constant debate persists around whether to embrace the professionalization of one’s platform or to stave it off as long as possible. There are deliberate product choices that can tilt the platform in either direction. These decisions are a subset of a burgeoning understanding of the life cycles of a platform, which I’ll cover more in a future essay. But few marketplaces are able to avoid this transition.

Things can only be underutilized for so long

Empty houses, random stuff in your house, and idle cars.

There is a natural invisible asymptote to these. Eventually, the growth of marketplaces built on top of these underutilized assets slows. They are a great playbook to enter a market, but there is a finite amount of unused assets–and eventually there are no more idling assets to utilize.

Profit drives new behavior

At the beginning, people with underutilized fixed assets dominate the supply side of a marketplace. These people will always have their place on a platform, because they have such a cost advantage. But eventually, others, or even some of the same people, realize that using the marketplace is so profitable that it’s worth it even if they have to bring new supply online.

There are natural economies of scale to this. Someone creating supply on a platform full-time will be better at many things like:

Understanding the intricacies of how the platform works
Understanding how to best make money on the platform
How to best get listed, seen, and discovered on the platform
The most attractive ways to invest capital for a return on the platform

Increasingly the best hosts, sellers, and drivers on the platform professionalize. They are already making the majority of their income on the platform. And whether or not they are the majority of sellers on the platform, given their professional usage they are very likely to be the majority of transactions on the platform.

Scaling is a business

To scale beyond a certain rate is difficult using underutilized fixed assets. Besides a finite amount of them, there is a natural limit to the rate at which they grow on your platform.

Professionalized sellers are very different. As long as it’s profitable for them, they’ll do everything they can to expand to meet demand. Underutilized fixed assets are nice because they have a low effective cost basis.

Professionalized sellers are nice because they are variable and will scale as far as can be supported. Large marketplaces gradually shift to professional sellers as they try to maintain their pace of growth.

The consequences of sequencing

Companies consciously or unconsciously make decisions that embrace or reject this professionalization of sellers to differing degrees and speeds. These choices have large impacts on the growth rate and unit economics of the business.

They also have large impacts on the relationship with supply on the platform and regulation by the government. For example, if you think of Airbnb’s regulatory hurdle at a local level, local landowners renting out their bedroom is far more attractive politically than fast growing companies buying up for sale properties to convert them into short term hotel rooms.

Appendix A: Crypto

Crypto is no different. When Satoshi created Bitcoin, they identified an underutilized fixed asset that could be used to bootstrap the cryptocurrency’s security. Bitcoin was set up so that individuals could have their CPUs, which were often left idle, mine Bitcoin when not being used. For early adopters, this was great. They hadn’t realized their unused CPU cycles were valuable–but now it was like they found free money they hadn’t even known about. And in today’s dollars, those who had their CPUs mining made substantial fortunes. And for Bitcoin, the deal was even better. Security requires something computationally hard. The Proof of Work system got others to provide the compute needed to scale security for Bitcoin without Satoshi needing to personally pay significant sums of money for their own compute. These underutilized CPUs were distributed among a very fragmented and large set of early adopters. Diminishing fears of miner centralization and having the very users be the source of security as well.

However, Bitcoin is also a good example of the limits of underutilized fixed assets. Though underutilized CPUs may be the cheapest source of compute (free). Bitcoin mining is a zero sum game. One’s return is roughly proportional to what percentage of mining you are. This is ideal for Bitcoin, because it makes a competitive red queen equilibrium, where miners must keep working to bring more compute to bear at as low costs as they can in order to keep pace, much less gain ground on their competitors. What matters is not having the cheapest compute, but having the most scalable compute that is still profitable. Underutilized CPUs were cheap, but they weren’t scalable.

Enterprising miners soon realized that they could utilize their GPUs for Bitcoin mining. And machine learning startups renting GPUs from AWS soon learned about this the hard way, as miners realized there was a perfect arbitrage by renting GPUs from AWS to mine Bitcoin–and quickly tied up all of Amazon’s GPUs until their prices rose to make it not profitable anymore. Though Satoshi had intended for a distributed user base mining with their personal computers’ CPUs, there were better places along the SLA-Price curve.

Since then mining has become even more specialized and economy of scale, with mining hashrate centralized around a small number of large companies that specialize on mining. These companies create their own ASICs, which are purpose built for mining Bitcoin, and allow for significantly more mining power at lower costs than even GPUs. This has made CPU and GPU mining prohibitively expensive and, even for those with spare CPU cycles, not very profitable.

Appendix B: Food Delivery

Underutilized fixed assets are the topic of this essay. But all the other variants, such as underutilized variable assets, are important to understand as well. Food delivery is a great example of this. Many people often express disbelief that food delivery startups, have been able to get as many restaurants to sign up for them while charging large take rates (sometimes north of 30% now) from the merchants. “How do these restaurants afford it?” these skeptics ask. What these skeptics fail to understand, is that restaurants do not view deliveries the same way they view customers dining in. There are many factors that restaurants are constrained on including, ingredients, labor, kitchen capacity, and dining space.

For walk-in diners, the primary constraint is dining space. There is an immovable cap on how many tables a restaurant has, and thus how many turns they can do a night [1]. This real estate space is a fully utilized fixed asset. So a startup bringing new diners to a restaurant is entering a zero sum game, especially during peak hours when a restaurant knows they can likely fill all their tables. If a restaurant accepts a diner from a startup and pays them a take rate, this replaces a diner that would have walked in for free. This is the reason why restaurants often don’t list their prime hours on sites like OpenTable. They know they can fill their limited number of tables, so why pay OpenTable a fee for it?

But delivery is different. Real estate space is not relevant to delivery orders. Instead the two main constraints for restaurant delivery are labor and kitchen capacity. Kitchen capacity is an underutilized fixed asset. Most kitchens can handle more orders than they handle each day, but never need to because there’s not enough space in the restaurant for more diners. Like all underutilized fixed assets, restaurant owners are very happy to have their kitchens handle more orders if makes sense.

The other constraint is labor. Restaurants may have some underutilized labor, depending on how busy they are. However, if they have any significant number of delivery orders, they likely would need to have their workers do more shifts, or hire new workers. So labor is an underutilized fixed asset up to some point, but then primarily a variable asset for restaurants.

So when a startup brings new delivery orders to a restaurant, their main question is whether the delivery will be profitable net of the variable costs like ingredients and labor of the restaurant. Other factors like real estate costs are already fixed and so not factored in by restaurants. If these delivery orders are profitable, restaurants are happy to do any and all incremental orders–and will happily pay a higher take rate in return for bringing them the customer. And if the startup brings more customers than they have workers to handle–they’re overjoyed to hire new workers, as long as the economics make sense [2].

Variable assets are great because they can scale well. However, they are far less preferable to underutilized fixed assets for a number of reasons. Their primary weakness is that they can be copied by competitors. Underutilized fixed assets when discovered are have a huge amount of stored value. The first company to properly use them can increase their value significantly. However, after they’ve burned through this arbitrage, future competitors must find a new way to get advantaged distribution fast. This isn’t true for variable assets as we can see in food delivery. The field is increasingly competitive with Grubhub, Uber Eats, and Doordash all competing in increasingly costly battles.

Endnotes

[1] In the US, most restaurants seem to view the number of turns possible per night as being fixed. Many Chinese restaurants, however, do not. Instead they view it as one part of the restaurant trilemma. Food quality, price, and service/speed are all in tradeoff with each other. Chinese restaurants often optimize for great food at low prices, opting to make it up by turning over tables as fast as possible. Often that’s why these places will get your orders before you’re seated, give you your check before you’ve finished eating, and rush you out the door the second you finish the last bite of food. While Westerners often complain about this bad service, it’s the ultimate service: all to maintain quality food at low prices.

It’s surprising that this is not common in the US. Perhaps it’s due to social norms around dining. However, perhaps this is beginning to change. One of the few reliably profitable areas of the restaurant industry, Quick Service Restaurants (QSRs) are all about improving how much throughput each location can handle.

This chart is the only reason I published this entire essay

[2] This is why delivery companies are not winner-take-all on supply. Restaurants are rarely exclusive with delivery companies, because they want any and all incremental demand they can get. In fact, it’s not enough to charge a lower take rate than your competitors, because restaurants will still accept all incremental orders they’ll make money off of–since they’re almost never over capacity. This is why delivery companies must compete to corner the market on demand if they want to be winner take all.

Acknowledgements

Thanks to Keila Fong for making sure that Kwokchain: Year 2 is not an entirely empty book.

Notes on Superhuman’s Acquisition Loops

Recently sent some notes on aspects of Superhuman’s acquisition loops and business model that interested me to some friends. Since I often ask people to write and send me more memos, thought I’d clean up and share this example.

This essay is heavily focused on growth loops, and assumes familiarity with them. If you are not familiar with them recommend reading “Growth Loops are the New Funnels” on the Reforge blog.

Companies in the productivity and collaboration space have been raising at high revenue multiples. Examples include Superhuman, Figma, Airtable, Notion, Slack, Zoom, and others. While valuation multiples don’t necessarily mean these companies will be successful–it is a sign of both excitement about their characteristics as well as the demand pull seen by the companies.

And these companies have overall had impressive traction that looks to compound. There is a longer post to write about them, but that’s not the focus of this essay.

Superhuman is an outlier among these, in that its users do not require or benefit from others also being Superhuman customers. Email clients are interoperable with any other email clients.

A large component of what’s driven the belief in these other companies is the belief that they will compound due to the network effects of collaboration. These companies have a mix of inter and intra-company network effects that will drive increasingly cheap and compounding acquisition as they scale. Users can only use the product together if others also use it. This makes starting the flywheel and hitting minimum viable scope of the network effect hard, but it makes it more scalable and defensible once it’s established.

Superhuman is not like this. It does not have network effects. Users can email with others regardless of what email client they use. However, its distribution *is* still spread by users vs by the company doing enterprise sales. It’s not personal utility that drives new adoption, but word of mouth. Social capital–not personal utility–is what drives Superhuman’s acquisition loop. Users don’t share Superhuman because they need others to use it for it to work; they share it because they want to. This change has many downstream impacts on Superhuman.

Business in the back. Party in the front.

Overview of Superhuman acquisition loop

Superhuman’s CEO has a great essay on finding product market fit in the First Round Review. It’s about focusing on what percentage of your users would be very disappointed to no longer be able to use your product as a core metric.

But part of what makes the path work so well for Superhuman is that the company is structured around a very unique loop.

Most users find Superhuman via word of mouth, social referral, or from it being shared and discussed on platforms like Twitter. They come in to the sales process already primed to want to join–instead of requiring a lengthy sales process. After a pre-qualification process, they’re onboarded to Superhuman.

They pay for Superhuman and use it. But perhaps most importantly, they invite their friends and talk about it on Twitter, thus closing the loop and continuing the cycle.

A simplified example of Superhuman’s acquisition loop is shown below. There are many additions that can and should be added to this, and in later sections we’ll dive into some of them. But this is the essence of Superhuman’s acquisition loop.

Superhuman doesn’t have the traditional sales process of an enterprise company, but many productivity startups with bottoms up adoption don’t. More atypical, is that Superhuman’s acquisition loop is that of a consumer social product.

As long as Superhuman can sustain these channels, it has no marketing expenses and instead of traditional sales can hire people focused on the onboarding and customer service experience. Letting the product and community drive customer education and sales lead to much better unit economics. More importantly, it shifts the center of gravity of the company.

When acquisition is mainly via referral or social, growth is driven by users who are delighted and proselytize the product. All companies want to delight the users who would be most disappointed to lose the product, but this often comes at tradeoffs with other priorities. Superhuman is very good at focusing on delighting its most fervent users, because these users drive new user acquisition for the product.

Social Capital Driven Acquisition: Aligning small details with big picture

Social referral

Social referral is not an unusual channel for a productivity app. The perceived status of being accepted as an early user of Superhuman has helped Superhuman’s acquisition and conversion, though it’s unclear how long that will persist as it scales its number of customers.

Referring a friend to Superhuman or posting about it on Twitter has real friction. There is logistical friction that Superhuman has focused on minimizing. There’s no separate flow for referrals. Instead it’s as easy as introing your friend via email. And for added simplicity there’s a CMD-K shortcut for it. Similarly, when emailing a friend, the sidebar will have a refer button if they aren’t a Superhuman user. Building the referral mechanism seamlessly into the app increases their referral rate, which is core to their growth.

But logistics aside, there is a very real cognitive friction to social referral. It’s not top of mind or high priority for most people. Social capital driven sharing doesn’t *need* to happen, so it’s often the hardest to predict and increase.

If logistical friction is why most potential proselytizers give up on referring friends, delightful moments are what reminds them how much they want to share your company.

Delight Driven Development

Delightful moments are what increase the rate at which users remember and want to share Superhuman. Delightful moments are those moments where reality exceeds your expectations.

Superhuman has many small details that delight users. CMD-Shift-I automatically constructs intros. For most actions that could have been done with a keyboard shortcut, Superhuman will show you the shortcut for next time. Their copy function will make footnotes of links if you paste to plain text. Of course, there are also bigger ones as well like their onboarding flow (discussed below). For most companies, it’d be nice but of indeterminate value to prioritize these small details; for Superhuman they are crucial for driving acquisition.

Brand as Social Capital

Social capital acquisition loops are not just about getting users to invite others more often. They are also about getting those invited to want to join.

The importance of invitees wanting to join is obvious at a surface level, the conversion rate is a function of how many are interested. But even among those who sign up, their level of enthusiasm matters. The cost of onboarding, the level of churn, and even their level of engagement will be a function of how excited users are to use Superhuman.

The social capital that Superhuman has with a potential user is incredibly important in a social capital driven loop. Your friend inviting you matters, but just as important is how primed you are to want to use Superhuman when you’re invited. If you’ve heard many good things about it, it feels like many friends of yours use and enjoy it, and you admire Rahul and the team’s product sensibilities, then you are more likely to be a good customer, both in using Superhuman as the team intended it to be used and in willingness to pay $30/month for it.

This is why it’s important for Superhuman to be consistently discussed, and in social capital positive ways. This aligns Superhuman with opening up more about their internal processes, whether how they built their dark theme or celebrating the importance of onboardings and those who do them. It’s also why I’m bemused about the debates around whether or not Rahul should have a small venture fund. My personal views aside (and to be clear I’m strongly pro-experimentation in the form of venture and especially in founder led venture) anything that builds Rahul’s social capital is *definitely* in the interest of Superhuman’s acquisition loop.

Onboarding

Superhuman’s onboarding flow is also notable.

Superhuman has an aggressive onboarding flow compared to other productivity tools. Everyone who joins is onboarded by someone on their team. This can range from a video call to an in person meeting with the CEO. While common for enterprise sales, it’s rare for consumer apps. The frontier of the hybridization of enterprise and consumer continues to expand. Or more precisely, companies increasingly realize it’s not a stark divide, but a gradient. Superhuman’s onboarding flow drives increased new organic distribution, decreased churn, and a tighter feedback loop.

Superhuman’s high touch onboarding may not be right for every company. However, there are likely effective variants that can be implemented cheaply enough and targeted at the right customer segments to be very ROI positive. Even without human driven approaches, there are product-supported white glove experiences without the expected costs that are likely to work.

Here are some notes on some of the advantages:

General productivity tips. They manually observe your workflow and give general productivity pointers (even if not in the Superhuman app). This is useful and establishes credibility with customers.

KK note: That some of you were functional humans without knowing about command-tab is mind blowing to me. I’m barely a functional human*with* command tab letting me instantly and repeatedly switch between all my appli—oh no

Setting up and tailoring Superhuman. They have you go through setting up Superhuman with them on hand. Many companies actually have pretty significant churn between potential customers signaling they’re interested–and actually ever getting set up. By having onboarding in person, Superhuman removes this churn.

Managing the set up also lets them customize the setup to what they think would be ideal for you. For example, looking at your inbox they may suggest certain split inboxes that’d be ideal and set that up on the spot–vs hoping you discover it on your own. The more they help ease you into a new workflow, the less chance you churn.

Strongly prescriptive on workflow. Most apps are happy for customers to use them however they please. In contrast, the Superhuman team has strongly prescriptive views on what your workflow should be. In their early days, they wouldn’t even allow users who didn’t subscribe to the same email ideology. There are questions about how well this scales and its efficacy in getting new more mainstream customers to adopt the workflows. But the benefit is that it helps shift users towards ways of using Superhuman that are more likely to be successful.

The Instant Inbox Zero. They’ll push you to get to inbox zero in the onboarding session. If you don’t have many emails they’ll sit there as you clear them out. If you have many they’ll offer to nuke your inbox and put them all into email bankruptcy. Starting at inbox zero makes adherence to their view of inbox management more likely. And feeling like you left session and cleared out entire inbox is a magical moment for new users.

KK note: I’ve never felt so motivated to process my email efficiently, then with the CEO of Superhuman sitting beside me patiently burning what could have been productive time. And I’m sure immensely judging my email workflow.

Tight Feedback Loops. Having every customer manually onboarded creates a very tight feedback loop on how people use email, what resonates about Superhuman, and where in onboarding or using the product users struggle. This continuous user research allows the team to quickly improve the product, and just as importantly–rapidly refine how they pitch Superhuman’s value proposition.

Segmented Onboarding. Most companies have the same onboarding flow for all their customers. This is often because they think there is little variance in the value of each customer, and even if there was they wouldn’t know ahead of time. Because they benefit from social referral, WOM, and discussion on social platforms like Twitter, there can be people who can drive significantly outlier new user distribution. Similarly, customers who can convert their entire company or may be future investors also drive more value. Superhuman can decide who onboards a new customer based off of their view of the non-immediately financial return on retaining them.

Human Connection. Every onboarding at Superhuman is done by someone on their team. As importantly, because new users are already high intent, it feels far less transactional and sales oriented. And more aligned and focused on setting up and learning the product. Knowing someone at the company I suspect lowers churn. When the CEO onboards you personally, it becomes harder and more personal to unsubscribe.

I also think that associating a human face with the product (and one that isn’t trying to get you to buy more) also changes the dynamic. It makes the product feel more personal. Beyond churn, I think this translates into things like how you feel when emailing them with feedback or complaints, makes you more likely to read their emails, etc.

Baseline Onboarding. All the early onboardings were done by Rahul, the CEO. I highly recommend this, even beyond the benefits of the tight feedback loop. Nobody is going to be able to pitch the product better than the CEO. Not only does he intimately understand the entire product. It’s frankly just more effective when the CEO is walking you through the product. You’re less likely to churn, etc. This alone wouldn’t be that useful because the CEO’s time is the most valuable and not scalable.

However, by having early onboardings done by Rahul, it sets a baseline. As the company progressively scales out its onboarding flow, they have a baseline of what the highest touch, least scalable process looks like to compare against. This makes it easier to figure out when a new lower-touch onboarding process is giving up too much efficacy in return for scale.

Delight Driven Distribution. If Superhuman’s distribution is driven by outlier moments, there’s no better time to drive that then the onboarding process. I’d guess most tweets about Superhuman come from people who’ve just gone through their onboarding.

Good Friction

Superhuman’s onboarding process introduces significant friction. I know multiple people who’ve avoided signing up for Superhuman because they don’t want to go through onerous process. It’s odd in today’s world of self-service to see a product that doesn’t allow it.

For this stage of Superhuman’s growth, this self-qualification of leads is probably net positive. This process is more Good Friction than bad. Good friction looks like it hurts its step of the loop, but it net improves the entire loop. For Superhuman the onboarding process does likely ding the number of people willing to sign up. But as long as they are not demand constrained yet, this is a positive–it helps pre-filter for customers who are more committed.

And there are other significant benefits. As discussed above, the onboarding process improves retention, helps educate customers on how to use the product, and improves referrals. Of course, this would change if and when they do become demand constrained.

Feedback loops

Many companies no longer talk to their customers. It sounds crazy, but it’s true. Enterprise companies have to because talking to their customers *is* their sales motion. But for consumer companies and non-enterprise ones typically very few people have actually talked to a customer. There’s real value in this. And despite everyone nodding along it’s *underweighted*.

A company is just a process that hopefully compounds as it scales and improves in its ability to serve its customers. Closing and making faster feedback loops are how it compounds.

As discussed above, Superhuman’s onboarding flow shortens the feedback loop. They can see immediately hear what makes sense to people and what is confusing. It allows them to immediately test out new changes and ways of framing the product. And as our industry shifts increasing focus to retention, customer service moves from being a support function to a key retention lever.

Superhuman also does this by decreasing the friction (and response time) of feedback when using the app. Besides a top level button for feedback, there’s easy CMD-K shortcut for giving feedback.

It’s no surprise that the two top level commands bundled together are for 1. Giving feedback and 2. Referring new users. These are their core loops: strengthening the product and increasing its reach.

Do Social Loops Scale?

Superhuman’s social capital acquisition loop, onboarding flow, and feedback loops have been great for the company’s growth. Can these same loops scale with Superhuman, or are they strong initial loops that will have to be transitioned out as the company matures.

Manual user onboarding and talking to all customers typically *is* done by companies below a certain size, before being dropped as they become prohibitively costly with scale. Superhuman has been able to keep doing these even as they’ve grown. The real test for Superhuman is how effectively they can maintain it while scaling.

Historically, few onboarding process this high-touch and driven by social capital have scaled. There is typically a tradeoff between scalability and quality. Quality typically gets worse with scale. Some of the best companies are able to minimize this decline even as they scale out. Or in even rarer cases, build compounding loops that actually improve with scale.

Superhuman has already taken steps to make their onboarding more scalable. They know can onboard most customers remotely. Whereas they previously did every onboarding in person, and before that Rahul did most onboardings.

It’s likely that Superhuman will add self-service as they scale further. And sequencing their loops is typically how most companies mature. Or as Eugene Wei would call it moving past their invisible asymptote. However, if their LTV is strong enough, it’s also possible their current approach *is* scalable, which would be even more interesting.

Sequencing Loops

Social capital acquisition loops traditionally don’t scale. The social dynamics they rely on tend to change with scale and they become weaker. Personal capital driven loops, like network effects, typically scale far more effectively.

It’s easy to get someone to use Superhuman, because they can use it even if everyone they talk to uses another email app. But this very same reason that helps Superhuman in its early days hurts it at scale, because there is no need for collaborators to eventually switch on to Superhuman. By contrast, an app like Zoom, Slack, or Figma struggles in its early days, but actually becomes more and more compelling to use as they get liquidity. When everyone you work with uses Zoom, you *have* to use Zoom to video chat with them.

Social capital has historically been fast to rise but cap out sooner

These kinds of network effects have a high minimum scope, and *very* high maximum scope. Take Slack as an example. It is hard to get people to use Slack when no one is on Slack, but very easy when their entire company uses it. When no one uses Slack at an office, it’s hard to convince the first few to join. Why add *another* app that nobody is even using. Slack isn’t useful until at least my team is on it, it has a high bar before its network effects start kicking in. In fact, compared to alternatives it almost has a negative network effect below a certain amount of active users at a company. After all, why send a slack message to a coworker who doesn’t even use it, when they’ll definitely see an email sent. However, Slack also benefits from a high maximum scope. As more people at the company use slack it becomes increasingly essential for everyone else to use Slack. If all the discussions are happening on Slack and you’re the only person not using it, you’ll be missing out. And there’s no way to be part of that conversation without using the app.

KK Note: Astute observers will note that this is a high maximum scope *within* companies. But that may not naturally spread across companies. Some classify this as local vs global network effects, which is a specific framing of the general elasticity of sequencing loops. And a topic that deserves its own post.

Superhuman’s current product lacks these kinds of network effects, making them distinct from many of their peer companies, and possibly without a scaling compounding loop currently.

Sequencing from social capital loops to network effects is #goals

The normal path is to sequence to a better loop. In Superhuman’s case there are many options. It’s likely we’ll see them release collaborative tools for teams using Superhuman or features like shared editing or enterprise management of inboxes. These features would add intra-company network effects. There are also many features that should be uniquely possible when both parties are using Superhuman.

Scaling Things that don’t Scale

But the really intriguing possibility is that Superhuman believes they can scale their existing loops.

While still small in scale, Superhuman has managed to grow while keeping a remarkably high touch onboarding and feedback loops even as they grow.

Without internal data, we don’t know if these are unit economic positive (and their high touch approach is worth doing currently from a product feedback perspective even if they aren’t). But if they *are* profitable, then they are a datapoint that the frontier of high-touch, economic, and scalable can be pushed out further than anticipated. And perhaps they plan to scale a primarily social capital loop.

Presumably Superhuman’s social capital loops and delight driven development have led to high virality, lower churn, and low cost of acquisition. If this persists, it’s very possible this system is stable.

Will it be able to persist? One large factor is top of funnel demand. Currently they are not demand-constrained. Already having too many people who want the product is what allows all of their novel loops to be possible.

Their waitlist has ~200k people on it. Of course, this is not quite right metric since there is high variability in price elasticity among that list. But this is also the point. Currently they have no shortage of potential customers who are willing to pay full freight. This is part of what allows their unit economics to be very strong: they don’t need to pay for customer acquisition, or traditional inside sales reps. If this can’t be maintained then they will need to start building a traditional sales org which will change their economics. But if social referral and WOM can be a growing and stable source of new customers than that would be opening up new approaches for many companies in the future.

Appendix

Appendix A: Extrapolating from the acquisition loop

With this understanding of Superhuman’s acquisition loop, we can look at specific theoretical or real examples to see how it shapes Superhuman’s decisions.

Free

Since customer referrals and word of mouth are Superhuman’s main acquisition channel, Superhuman’s users are the core driver of their new acquisition. So a user is both a customer that pays Superhuman in money, and potentially a distribution channel to new customers.

While customers may pay the same amount, they are not of equal value as distribution. Some categories of customers, like founders or high profile users, are likely larger drivers of new customer referral or increasing brand awareness for Superhuman than others.

Let’s say one of these customers churns due to price. This disproportionately hurts the acquisition loop–since it’s driven by user distributed channels.

I suspect there are categories of users, especially founders, who if they churn Superhuman will offer the product to them for free. Perhaps Superhuman already does this.

Freemium is a far more complex tool with more levers than is appreciated in tech. It’s not a boolean distinction but rather a gradient that we’ll get better at understanding as an industry.

This goes to our recurring conversations about how freemium isn’t a boolean distinction, but a gradient we’ll get better at understanding as an industry.

The deciding factor for freemium should be where the company gets more value from a user than the financial capital of their payment.

Harming the Loop / Social Regulation

Recently Superhuman became embroiled in twitter controversy after Mike Davidson wrote an essay about its pixel tracking. Writing on the debate itself and my view is out of scope for this piece and would need to be its own essay (which I’m unlikely to write). But I want to talk about why it mattered.

If you look at Superhuman’s acquisition loop, Twitter is a real channel for them. And social referrals and WOM are the other big ones. Just as Superhuman being viewed as a cool and enviable product lowers the friction to it being shared and the likelihood of a potential customer signing up. Superhuman being viewed as a creepy or malicious app increases the friction to it being shared. This is even worse on platforms like Twitter where anyone sharing Superhuman may have these accusations posted in reply to their tweet. If it continued unresolved, many would likely not even share on twitter because of wanting to avoid the pushback.

What is your strength is always your weakness too. With twitter as a main channel, these kinds of posts become existential. Which is why they have to be taken very seriously. Which is what we saw.

When companies sell directly to people they become very sensitive to the will of the people. Superhuman *has* to take these claims seriously, because it *can* hurt them if users don’t view them as protecting their privacy. This is how things should work.

Credits

Many thanks to Keila Fong, Dave Petersen, Max Bulger, Boris Jabes, Ryan Petersen, Michael Dempsey, Allen Yang, and Kane Hsieh for the many discussions on Superhuman and their unceasing pressure to publish this. And to Eugene Wei for our many discussions on social capital and how to best understand and model it.

Finally, much thanks to Casey Winters who has spent far too many hours in a room with me formalizing our views on loops and how to model them as we built Reforge’s Advanced Growth Strategy course. Perhaps one day we’ll have written an essay on every variant of loop, not just [social capital, user generated, user distributed].

The Arc of Collaboration

The arc of collaboration is long and it bends in the direction of functional workflows.

Why Slack is an Else Statement, there is no distinction between productivity and collaboration, and why the Slack of Gaming may be Discord but the Discord for Enterprise is not Slack.

Disclaimer: I currently use every product mentioned in this post, and love all of them.* I also used to work at Greylock and helped with the investments in Discord and Figma. There’s lots of opinions I have on both of them as well as their general spaces. But really you should talk to Dylan Field and Jason Citron. And John Lilly and Josh Elman, who led the investments in both. Because all four have shaped my thinking on productivity and collaboration significantly. And compared to the world they are still living decades in the future on how both are merging and where they are going.

*Except Salesforce, because I am not successful enough to need a Salesforce instance for my personal life.

When Slack first started growing, there were many debates over which company would own collaboration, Slack or Dropbox. Dropbox proponents argued that Dropbox already managed all the actual records of a company, and so would be the center of gravity. Slack partisans argued that Dropbox was a transitory product, and eventually companies would stop caring about individual files, and messaging would be the more important live heartbeat of a company.

Messaging, it turned out, appears to be a better center of gravity than documents. And while Dropbox (barring significant traction in its new products) seems to be fading in its centrality, what’s striking is that Slack’s victory seems hollow as well. If anything we’ve seen even *more* new companies building towards owning parts of these workflows and getting traction.

That’s not a statement on its prospects as a company, or its accomplishments. Slack, even with recent dips in its stock, is a $15B company with very impressive underlying metrics. But there’s this feeling that’s hard to shake.

If Slack won the war, and owns collaboration, why doesn’t it feel like the war is over?

Slack was supposed to be the app that became the OS, the end of the cycle on productivity. But that hasn’t happened. How should we understand what’s happening.

Slack is ubiquitous at most companies in tech (and in many other industries as well), but it doesn’t feel like it is becoming the central nervous system undergirding all the apps and workflows of its customers.

A new generation of functional apps have risen, with messaging and collaboration built directly into them as first parties. And with them it becomes increasingly clear that Slack isn’t air traffic control for every app, it’s 911 for when they fail.

Slack is the 911 for whatever isn’t possible natively in a company’s productivity apps. And though it’s improving, there are still many structural cracks. Slack is current best solution for filling these cracks. But it doesn’t fix the cracks themselves, improved processes and productivity apps are needed for that.

As the ecosystem of specialized SaaS apps and workflows continues to mature, messaging becomes a place of last resort. When things are running smoothly, work happens in the apps built to produce them. And collaboration happens within them. Going to slack is increasingly a channel of last resort, for when there’s no established workflow of what to do. And as these functional apps evolve, there are fewer and fewer exceptions that need Slack. In fact, a sign of a maturing company is one that progressively removes the need to use Slack for more and more situations.

What drives these changes in collaboration? And is there room for one app to focus entirely on collaboration–and if so, what should it look like?

To understand this is to understand that there is no distinction between productivity and collaboration. But we’re only now fully appreciating it.

Separated at Birth: Productivity and Collaboration

Productivity and Collaboration are two sides of the same coin for any team with more than one person. Work is just the iterated output of individuals creating and coordinating together.

But the two have been distinct and isolated segments historically, due to how long the feedback loops of both were.

Post-software and Pre-cloud. Collaboration is external to productivity

What really began our modern era of how to think about collaboration began with the shift to software. Digital work has significantly faster feedback loops for productivity. Software, quite simply, can produce and iterate new things at a daily if not hourly or minute basis.

Suddenly, the constraint on work became much more about the speed and lossiness of collaboration. Which remained remarkably analog. The friction of getting people your document, much less keeping correct versioning was non-trivial.

Even with the introduction of email, people could send each other files—but still had huge coordination costs around versioning.

We might as well have been using carrier pigeons

Cloud – Dropbox and Box

As the industry began to transition to cloud, companies like Dropbox and Box rose. Instead of everyone keeping their own local copies of documents, what if everyone had them pooled in the cloud. Then parts of collaboration like versioning and permissioning could be done across the entire team.

Employees could make changes directly in document, and trust it would propagate to their co-workers. In practice, there were still versioning issues to handle. But it was a significant improvement.

syncing files for designers was hilarious chaos

However, this model looks transitory in retrospect. In a pure cloud world, this atomic unit of documents seems increasingly archaic. Documents are more a constraint of a pre-cloud world. And once you assume storing them online is table stakes, the question becomes where is actual collaboration happening that then leads people to wherever they need to do work.

And core Dropbox is not a solution to this. People store their documents in it. But they had to use email and other messaging apps to tell their co-workers which document to check out and what they needed help with.

Dropbox understands this concern. It’s what’s driven their numerous forays into owning the workflows and communication channels themselves. With Carousel, Mailbox, and their new desktop apps all working to own that. However, there are constraints to owning the workflow when your fundamental atomic unit is documents. And they never quite owned the communication channels.

Slack

Slack became the place you messaged your coworkers and sent them links to the work you wanted them to check out. They began to displace Dropbox as the center of gravity for companies.

The dream of Slack is that they become the central nervous system for all of a company’s employees and apps. This is the view of a clean *separation* of productivity and collaboration. Have all your apps for productivity and then have a single app for coordinating everyone, with your apps also feeding notifications into this system.

In this way, Slack would become a star. With every app revolving around it. Employees would work out of Slack, periodically moving to whichever app they were needed in, before returning to Slack.

But productivity *isn’t* separate from collaboration. They are the two parts of the same loop of producing work. And if anything collaboration is in *service* of team productivity.

What is Slack, really?

There has been much pushback to Slack in recent years. Often centered around this feeling that Slack is distracting and not productive. As with any successful app, much of it is the gripes that come with any app that is successful enough to become a significant part of your working life. But there’s an underlying current to these critiques that I think is real but people struggle to pin down precisely.

It’s not that Slack is too distracting and killing individual productivity. It’s that your company’s processes are so dysfunctional you need Slack to be distracting and killing individual productivity.

Slack is not air traffic control that coordinates everything. It’s 911 for when everything falls apart.

Every slack message about a new document your feedback is wanted on or coordinating about what a design should look like is a failing of process or tools. Slack is exception handling. When there’s no other way to make sure someone sees and update, or knows context, Slack is the 911 that can be used.

Slack serves three functions:

Else statement. Slack is the exception handler, when specific productivity apps don’t have a way to handle something. This should decrease in usefulness, as the apps build in handling of these use cases, and the companies build up internal processes.
Watercooler. Slack is a social hub for co-workers. This is very important, and full of gifs.
Meta-coordination. Slack is the best place for meta-levels of strategy and coordination that don’t have specific productivity apps. This is really a type of ‘else statement’, but one that could persist for a while in unstructured format.

There is an entire separate essay to be written about meta-coordination. Which I think can have very different outcomes from functional workflows. We may be very far from formalization of meta-coordination and less concrete strategy planning. Which means unstructured text, meetings, and video calls could be the best current functional workflows for them for a while. But for our purposes of this essay will put that as out of scope.

As a company’s processes mature and the apps they use get more sophisticated, we expect to see the need to go to Slack for exception handling *decrease* over time. (Though of course, the complexity of the overall company may increase at a faster pace than this maturation, leading to a net increase in slack messages).

These three functions are incredibly important. From the perspective of owning the process of doing work, they point at interesting relationship.

Slack’s importance is inversely tethered to the rate at which functional workflows within companies become legible and systematized. Both at an operational level, and long term at the meta-strategic level.

And this makes sense. The platonic flow of productivity should minimize time spent not productive, with collaboration as aligned and unblocking with that flow as possible. By definition, any app that requires you to switch out of your productivity app to collaborate is blocking and cannot be maximally aligned. It’s fine to leave your productivity app for exceptions and breaks. But not ideal when working (and not having issue).

Functional workflows rule everything around me

Slack ironically is more similar to Dropbox than expected. The more time goes by the more it looks like exception handling being needed ubiquitously is a transitory product as we switch off of documents. After all, like Dropbox, Slack makes the most sense as a global communication channel when the workflows themselves don’t have communication and collaboration baked in natively. For documents this is true, but increasingly for modern apps this is false.

As it becomes more clear what are specific functional jobs to be done, we see more specialized apps closely aligned with solving for that specific loop. And increasingly collaboration is built in natively to them. In fact, for many reasons collaboration being natively built into them may be one of the main driving forces behind the venture interest and success in these spaces.

As these apps proliferate, there is less and less need to turn to Slack. And Slack becomes more and more about the edge cases that aren’t yet built in.

Github is a great example of this for the engineering side. Salesforce for Sales. Out of scope of this essay, but there’s lots to write about this and I’d generalize Shopify as being part of this as well.

But for our purposes, let’s use an example, Figma.

yo dawg, I heard you like chat. So I put chat in every app.

Figma

Figma is a collaborative design tool. Unlike Sketch or Photoshop, Figma has collaboration built in natively as a first party. This means the ability to comment on designs. But it means much more too. It means the ability to design together at the same time. To be able to send a live demo to someone frictionlessly and then be able to make live changes as you talk to them. It means being able to build design systems that are reusable and plugins that are shareable.

Figma shows what collaboration means when you understand that collaboration is *intimately* part of productivity. And always has been.

If you are working on a design Figma handles all communication. There’s no more need to send an updated file on Slack. Or type in feedback on Slack. Or make a change and let someone know on Slack. And as Figma increases the scope of their app and adds more team and enterprise features. Even for sharing with non-designers on the team, the need for external communication falls.

And as Figma expands into plugins, the ecosystem will continue to solve for more and more of the needs and exceptions.

Over time, our workflows align with our functional flows. And collaboration is no exception.

And Figma is not alone. More and more apps in all categories understand that collaboration should and must be built in as a first party if they want to best serve their customers. Notion, Airtable, etc all understand this. The feedback loops of collaboration get so short that they become part of the productivity loop.

The future increasingly looks like one where companies use very specific apps to solve their jobs to be done. And collaboration is right where we work. And that makes sense, of course. Collaboration *should* be where you work.

Meta-coordination

It should be noted, that meta-coordination adds nuance to this. Just as we increasingly productize the functional workflows. It allows us to start to be better at the meta-coordination at longer timeframes. Which could have standalone functional apps that specialize in these slower cadence coordination problems. Slack and Zoom are both possible answers in this regard. As are apps working in todos, project management, etc.

The efficient frontier of meta-coordination is fascinating. Over time we see productivity apps eat up the stack. Google docs is a good example of the abstraction layers of coordination.

Google docs is good at line level commenting. So for this low level of coordination it excels. Which when sending word documents was the current state of the art, felt advanced. But increasingly, feels limited for higher abstraction levels of collaboration. As apps like Figma build in deeper collaboration.

Can there be a meta-layer?

This isn’t to say that there cannot be a horizontal collaboration app that is core to the productivity workflows. But it likely cannot be blocking to productivity. It can’t be a peer level app that is standalone. Instead it must work across and within each productivity app.

Standalone messaging is not what ties all apps together. It is a peer level product that’s used where the others fall through.

However, there is a need for a layer across all the applications. A layer for things that should be shared across the apps as well collaborative functionality across them.

Slack in its current form cannot be this. If you have to switch out of a product to use Slack, then it is not the layer tying them altogether. Instead, the layer needs to exist a layer above. If everything was in browser it’d be a browser extension. But since most apps are not, it needs to be at the OS layer.

There is some mix of presence, collaboration, coordination, and identity that should be ubiquitous across whatever apps are being used. A layer more attached to the people doing work and what they’re trying to accomplish—than which specific app they’re in.

Perhaps one of the closest to this we’ve seen was Screenhero. After all, the idea of screen sharing is inherently about collaboration while working within productivity apps.

But it made the decision to be downstream of Slack, not upstream. It assumed Slack would be the central nervous system for people at work, and people would switch over to Screenhero from Slack. It traded scope for distribution. And got neither.

KK note: It was acquired by Slack in an all equity acquisition. So to be clear it was hugely successful

But there *is* a non-enterprise example of what this layer might look like.

That company is Discord.

Discord

Discord is the best analog for what should exist. For a while Slack and Discord were compared to each other as competitors. As Discord has focused squarely in gaming, and Slack in companies this comparison has been used less and less.

But this misses the main distinction between Slack and Discord.

Discord is actually two products bundled into one. It *is* a messaging app that looks akin to Slack. But it is *also* a meta-layer that runs across all games.

This is the Slack for Gaming for Enterprise. The new startup meme should be X for Y for X

Beyond its Slack-like functionality, Discord has functionality like a social graph, seeing what games your friends are playing, voice chat, etc. These have been misunderstood by the market. They aren’t random small features. They are the backbone of a central nervous system.

Active users of Discord have it on all the time, even when they are not playing games. It’s a passive way to have presence with your friends. And when your friends start playing games it makes it easy to with one click go join them in the game. Bringing your actual social graph across all games. Finally, voice chat makes it possible to talk with your friends across all games, even when you are playing the game. Like when working in a google doc, having to switch out of your game to message is a negative experience. Instead Discord adds functionality to your games even while you are focused solely on them.

We will see more companies understand and begin to work on this area.

Final Thoughts

Abstracting out of productivity and collaboration apps into the processes themselves, there’s something beautiful at how much we’ve improved and continue to improve at the process of working together with other humans.

In Making Uncommon Knowledge Common I said “One way the tech industry can be viewed, is a process by which we collectively push forward our understanding of industries and new business models.”

And perhaps a company is just a process that hopefully compounds and improves in its ability to serve its customers.

But underlying both of these is the most beautiful loop of them all. Progress is a process by which humans compound and improve on our ability to work together better for the things we care about.

Like distributed computing, it has turned out that for most of human history coordinating among humans has been a slow, intractable, sisyphean effort. In the last few decades we have seen tremendous technical breakthroughs in the latencies and tooling possible to remove these constraints. Across the world, whether in productivity apps or in national governance, there will be a transition period as our norms and processes adapt to this tightening of the collaboration feedback loop. But perhaps I remain incredibly bullish on what it means for our alignment and output as we increasingly systematize and make sense of these.

Our ability to compound together at compounding together is our most beautiful trait.

Appendix

Appendix: Distribution

Of course, an approach like Discord for enterprise will need novel acquisition loops. This type of collaboration has strong intra-company network effects at scale. But lacks trivially obvious inter-company network effects or pre-liquidity loops.

Out of scope for this essay. And don’t quite want to get into the tactics I think would be effective here. That said will note a general framework here.

If you look at most collaboration companies’ loops there are a few dimensions to categorize most of the tactics and loop sequences by:

Single player vs multiplayer
Intra-company vs Inter-company
App required vs no app required
Synchronous vs Asynchronous
Personal capital vs Social capital driven

A company working in this space has significant surface area for novel growth loops at each combinatorial set of these.

Appendix: Fortnite and Epic

Discord is also useful for understanding what comes after this stage as well. If you look at Discord. One potential TAM constraint is if gaming becomes 1) power law with low ecosystem churn and 2) not monetized via purchase.

Fortnite and Epic is the best example of this potential. And Epic’s playbook in launching their app store vs Steam is a case study in how a dominant enough app can move up the stack if it has enough sway over end consumer attention.

There are similar lessons for companies selling to other companies. And we’ve already seen examples of these in specific industries. So always, something to watch for.

Credits

Many thanks to Keila Fong, Saam Motamedi, Dave Petersen, and Eugene Wei for the many discussions about this topic, their help with this post, and their unceasing pressure to publish it.

Furthermore, even more thanks to Keila without whom these super professional quality stick figure drawings would not have been possible.

Though they didn’t pre-read this post, also want in particular to thank John Lilly, Josh Elman, Dylan Field, and Jason Citron. All of whom have heavily influenced my thoughts on productivity and collaboration. And compared to the world they are still living decades in the future on how both are merging and where they are going.

Making Uncommon Knowledge Common

The Rich Barton Playbook for winning markets through Data Content Loops

Preface: This is part of a longer private memo analyzing Zillow and its recent shift towards Opendoor’s model. May publish rest of memo at some later point. But wanted to share first part, on Rich Barton and Zillow’s initial rise.

Have had many recent conversations with people in tech who didn’t know who Rich Barton is. So wanted to share this both as primer on him and on the cornerstone of his repeated successes.

When Michael Jordan returned to basketball from retirement—the first time, in his prime, not the second time of which we do not speak—the whole world watched in awe. Meanwhile, the tech world just saw the return of arguably the GOAT of consumer tech, the founder of three household names in Expedia, Glassdoor, and Zillow. And hardly anyone, even inside Silicon Valley itself, paid it any mind.

Rich Barton is hardly a household name. Perhaps this is because he’s not based here, and makes relatively few investments. However, while there are more visible founders (like Bezos and Zuckerberg) who’ve built bigger businesses, market cap and notoriety aren’t the only measures of a founder. And Barton is a strong contender for the title of best consumer tech founder because of his repeated success. He’s founded three consumer companies each worth over a billion dollars with Expedia ($18.6B), Zillow ($8.8B), and Glassdoor (Said to have been acquired for $1.6B).

And he’s back, having returned to the helm of Zillow as it pivots to respond to a new wave of fast rising competitors like Opendoor.

Repeatable success is key, especially in Consumer tech which is one of the hardest areas to succeed in. Companies that sell to large Enterprise customers are relatively well understood now, and even our understanding of SaaS metrics and business model decisions has matured a lot over the last decade. The Consumer tech sector, however, remains dark magic. The playbooks are far less developed—and no one’s playbook has demonstrated the repeatability of Rich Barton’s.

There are a few consumer investors who have multiple multi-billion dollar wins. But it’s hard to name people who have founded three consumer companies worth over a billion dollars each.

To reliably successfully invest in consumer is a rare feat; to repeatedly found successful companies is virtually unheard of. Doing so suggests a founder has hit upon an underlying structural playbook that isn’t yet commonly known, or successfully replicated. And while some of Rich Barton’s techniques are commonly understood, his core strategy to catalyze his compounding loops is not.

So What’s the Playbook?

If you’re reading this, you’ve likely used Zillow, Glassdoor, and Expedia before. It’s hard to look on the internet for anything related to real estate, jobs, or travel and NOT see one of Rich Barton’s companies. Their ubiquity is stunning.

But it’s not coincidental.

Rich Barton’s companies all became household names by following a common playbook.

The Rich Barton Playbook is building Data Content Loops to disintermediate incumbents and dominate Search. And then using this traction to own demand in their industries.

Or as he puts it “Power to the People”

Much of what Rich Barton pioneered has now become mainstream. SEO/search is well saturated, and the importance of owning demand has been popularized by Ben Thompson’s many essays on (Demand) Aggregation Theory. But the cornerstone of Rich Barton’s playbook, Data Content Loops, are still underappreciated and rarely used.

Owning demand gives companies a compounding advantage, but needs to be bootstrapped. When a company is just starting out, it not only doesn’t own demand, it has all the disadvantages of competing against others that do.

In order to grow their demand high enough to become a beneficial flywheel, Barton’s companies use a Data Content Loop to bootstrap their demand and create unique content and index an industry online (homes for Zillow, hotels and flights for Expedia, companies for Glassdoor).

Expedia: Prices for flights and hotels that before you’d have to get from travel agent
Zillow: Zestimate of what your house is likely worth that before you’d have to get from broker
Glassdoor: Reviews from employees about what a company is like that before you’d have to get from a recruiter or the company itself

These Data Content Loops help the companies reach the scale where other loops like SEO, brand, and network effects can kick in.

Barton’s companies then use this content to own search for their market. This gives them a durable and strong source of free user acquisition, which enables them to own demand.

Power to the People: Disintermediating Industries with Data Content Loops

Barton career can be summed up by his mantra “Power to the People”. His companies take power from the incumbents and give it to consumers. Instead of trying to hoard information, they are on the side of consumers and giving them more data transparency.

Glassdoor revealed how employees really felt about companies. Zillow shed light on what any house was worth. Expedia let people see the prices and availability of flights and hotels without talking to an agent. These were knowable things that people have always talked about with each other. There are few topics adults love gossiping about more than work, real estate, or travel. And few categories as core to their net worth.

Rich Barton took these whispered conversations and made them public for everyone to see. Afterwards, everyone wondered why they were ever private.

Part of the reason was that companies benefited from this credibility through obscurity. Real estate brokers have access to significantly more data about the specific houses and the general market via a set of data sources called the MLS. Historically, only brokers had access to MLS data, which gave them leverage over their customers and entrenched their importance as market makers. Similarly, lack of visibility into companies allowed bad ones to put on a good face until prospective employees had already joined. And only large companies could pay for data from compensation research providers, giving them advantage over the potential hires they negotiated with. Many incumbents are able to intermediate their markets and unfairly gain an edge from people’s lack of knowledge. And it’s scary to be the first to buck this trend on your own.

Plus it is logistically difficult. Job applicants are unlikely to know a current employee at companies they are considering joining. And even if they did, it’s unlikely they could trust them to tell them the unvarnished truth. Employees have little incentive to say negative things about their employer, unless very close with the person asking.

This sparse commons is a classic case of natural market failure. While some incumbents take advantage of the information asymmetry, most benefit from a third party that will handle the logistics things like:

Verifying legitimacy of information being shared
Maintaining privacy of participants
Aligning incentives to get people to participate in contributing to the commons
Finding, ingesting, and curating third party data into the commons

Rich Barton’s companies became public Schelling Points. They create common knowledge in their industries from information only middlemen had access to before, from public-but-hard to aggregate data, or from information collected from users themselves. These intermediaries, whether brokers or travel agents were misaligned. They controlled what information was shared with the public, but has an interest in withholding it. Instead of pushing increasingly more and higher quality information to the public, they maintained the status quo.

Creating common knowledge creates a network effect. All companies in Silicon Valley want to build network effects, but few have followed Barton’s path despite its effectiveness. The more people use and trust Glassdoor, the more companies must take it seriously. And as users see more people contributing to Glassdoor, they can be more confident they’ll stay anonymous when they add their review. There are virtuous loops in common knowledge.

Demand Rules Everything Around Me

Search

All of Rich Barton’s companies have primarily used Search (and word of mouth) as their acquisition channel. Search is a great channel, since it can drive significant demand at low cost. Few companies can generate enough high quality web pages about their industries to fully capitalize on it, however.

The Data Content Loops of Barton’s companies let them be the authoritative public source on a subject at scale and low cost. By having super relevant information about every hotel, home, or company someone might be interested in, Barton’s companies become the ideal destination for consumers.

Over the years, he’s refined this model. Expedia aggregated all the various hotel and travel options, but others had done that as well. However, Expedia and Booking.com were among the most aggressive to understand the importance of search. If you had the top spot in search, the next best thing was to acquire more sites so you owned the next top result, and so on. Use Travelocity, Orbitz, CheapTickets, or Hotels.com? All of them are owned by Expedia. And any site not owned by Expedia is probably owned by Booking.com. This approach, coupled with dominating the paid acquisition side as well, helped them dominate.

With Zillow and Glassdoor, Barton took this a step further.

Before Zillow and Glassdoor, if you wanted to look up information about a specific home or company, there wasn’t a webpage for it. Barton’s companies created the definitive page for each house and company. Using a combination of data from authoritative sources (like all the various MLS systems) and user-generated data, they created high quality content unique to each company or listing. Being among the first to do this let them do a huge SEO land grab, which has been hard to displace since.

If you look at the sources of traffic for Barton’s companies, the vast majority of their traffic comes from search or direct. This makes their user acquisition far cheaper than any company that relies primarily on paid acquisition. It’s this ability to get free acquisition at scale that made it possible to build companies in these otherwise difficult, low-frequency markets.

Becoming a Trusted Brand

The ultimate purpose of the “Data Content Loops + SEO” strategy of Barton’s companies is to own the demand side of an industry. Expedia wants to be the first place you go when you travel. Glassdoor wants to be the destination when you’re thinking about companies to work for. And Zillow wants to be the place you go to look at real estate.

Barton’s companies take industries that are low frequency and use their Data Content Loops and SEO to acquire users for free and engage them more frequently. While most companies in real estate have super high customer acquisition costs, Zillow is able to get potential sellers even before they are ready to sell, so Zillow is already there when the sellers are ready.

Owning demand ultimately becomes its own compounding loop since becoming a trusted brand builds its own network effects. Consistently building this reputation increases people’s trust in them and makes them a go to destination.

Saturation and Sequencing

The Rich Barton playbook was particularly strong because it both understood how to find a wedge into a new market and how to transition that to a durable long term advantage at scale.

Data content loops are surprisingly underutilized by tech companies compared to how effective they’ve been. They have a natural invisible asymptote–and often diminishing returns on more data over time. Like Underutilized Fixed Assets for marketplaces, they can be used as kindling to catalyze demand and hit the minimum viable scope of more scalable demand loops.

Zillow as Case Study of the Barton Playbook

Zillow is a perfect example of the Barton Playbook. The data for estimating the price of houses had existed, and many brokers used the MLS systems to estimate it, but nobody had made that available to the masses.

Zillow changed that with their Zestimate.

By combining data from various MLS systems and other sources with their pricing algorithms, suddenly everyone could look up the value of their home. Even better, they could look up the value of their friends’ homes. Within the first day of launching, Zillow had a million people trying to check out the Zestimate. That’s an incredible feat that even today few have matched.

Envy is the best rocket fuel.

This data content loop lets them estimate the value of 100M+ houses. Driving anyone interested in the price of their home (or a home they’re thinking of buying) to Zillow. And they continued to come back. Most users might not be selling their home, but they could all check the prices of their homes, and any home they saw. But the Zestimate didn’t just drive users, it gave Zillow something far more durable.

The Zestimate became the kernel that Zillow used to create a webpage for every house. Zillow used its data content loop to become dominant at SEO for real estate. Try searching for your house on Google. I bet the first result is Zillow. And if not, it’s certainly in the top 5.

Nobody had yet indexed all the homes in the US and brought them online. While sites like Apartments.com had started to do so for rentals, it wasn’t until Zillow (and Trulia) that this was done for homes. There was fertile search real estate to grab and Zillow rushed out to claim it all using the Zestimate as its spearhead.

The Zestimate also had the network effect of becoming public common knowledge. It gave power to the people, and offered leverage against brokers. Armed with the Zestimate, sellers could push back on brokers who tried to pressure them to lower their prices. The Zestimate wasn’t backed by anything so it wasn’t secured, but it forced brokers to justify why the pricing they suggested deviated from the Zestimate. In many ways, Zillow became for homes what the Kelley Blue Book is for cars. And the more people used Zillow, the more powerful it became as an anchor in conversations with brokers. If you told your broker your friend told you the value of your house should be $1 million dollars, your broker would laugh it off. But if tens of millions of people are using Zillow and it tells you your house is worth $1 million dollars, the broker may still disagree but they have to take it seriously. Thus this data content loop has a demand side network effect that strengthens with scale.

Zillow used the advantages to own the demand side of real estate. Even before they decided to buy or sell, consumers went to Zillow. And when they were ready to become buyers or sellers, Zillow was there to help direct them to brokers.

Final Thoughts

One way the tech industry can be viewed, is a process by which we collectively push forward our understanding of industries and new business models.

Consumer will eventually be understood in many of the ways we’ve come to understand other business models like Enterprise and SaaS. Until then, founders like Barton with repeated successes are an early sign of some of the patterns and contours that can lead to repeatability.

While many of Barton’s ideas–like owning demand–have become mainstream, his use of data content loops to catalyze demand for his companies is still underappreciated.

Core to building a scaled consumer business is the unpredictable path of bootstrapping initial demand. Data content loops are one of a few strategies we’ve seen work very well for this phase of companies. And as the world increasingly shifts from supply constrained to demand driven, strategies like data content loops that empower consumers are likely to continue to be very effective.

While the focus of this essay, data content loops and Power to the People, aren’t the only beliefs Barton has advocated for.

Barton has been an early and loud proponent of the importance of:

Unconstraining talent in society
Raising the bar on ambition in companies

Both of which are very core beliefs among many of the people I respect the most. And also warrant much more discussion.

Aside: Ben Thompson has interview with Rich Barton. Which you should totally go read. And in general should go listen to Rich Barton whether he’s giving a talk, being interviewed or on podcasts.

The End of History?

Of course, Zillow’s story didn’t end there. It’s now the incumbent with a new startup fast on its heels. To understand how this happened and why Zillow is moving aggressively to match them, we have to look at the the strengths and weaknesses of the original Barton Playbook and how Opendoor and new competitors’ map to them.

Acknowledgements

Special thanks to Keila Fong and Dennis Tang for help with editing this and without whom it would definitely not be public consumption ready.

Also special thanks to Casey Winters, for discussing through the loops of Zillow’s business model. I’m sure it gave him PTSD to the many days we spent in a room discussing companies while building the Advanced Growth Strategy class for Reforge.
Also thanks to Sam Hinkie and Eugene Wei for discussing this topic and splitting it out for public sharing.