How to Not Fail When Predicting the Future
The Birth of the Web 3.0 Rollup
The Economics Underlying Chatbot Mania
Ideas Matter
The Quiet Things That No One Ever Knows

How to Not Fail When Predicting the Future

“In the strict formulation of the causal law – if we know the present, we can calculate the future – it not the conclusion that is wrong, but the premise.” – An overview of the “Heisenberg Uncertainty Principle” from Uncertainty: The Life and Science of Werner Heisenberg

It’s common parlance to suggest that venture capitalists are in the business of “predicting the future.” Investments are referred to “bets,” and there’s a material uncertainty about the future. Case in point – a VC primer from The New Yorker’s 2015 profile of Marc Andresseen, “Tomorrow’s Advance Man”:

In venture, it’s not batting average that matters; it’s slugging average. Boldness is all. When Google Glass appeared, a16z joined a collective to seek out investments, and Andreessen declared that, without the face shield, “people are going to find they feel, basically, naked and lonely.” Google withdrew the product in January. But, he would argue, so what? His thesis is that such a16z failures as Fab and Rockmelt and Digg and Kno are not merely a tolerable by-product of the risk algorithm but a vital indicator of it. It’s fine to have a lousy record of predicting the future, most of the time, as long as when you’re right you’re really right. Between 2004 and 2013, a mere 0.4 per cent of all venture investments returned at least 50x. The real mistakes aren’t the errors of commission, the companies that crash—all you can lose is your investment—but those of omission. There were good reasons that a16z passed on buying twelve per cent of Uber in 2011, including a deadline of just hours to make a decision. But the firm missed a profit, on paper, of more than three billion dollars.

At Chicago Ventures, we are often discussing and brainstorming which factors or precedents we can identify that are predictive of future success. But over the years I’ve come to believe that venture investing isn’t the act of betting on the future; on the contrary, investments are a test of how well we truly understand the present.


Over the weekend I was re-reading William Duggan’s Strategic Intuition, a book I first read in business school, and which goes to lengths to demystify the unplanned creative sparks that underpin legendary strategic insights. Early on, Duggan takes a deep dive into understanding the scientific method, using Thomas Kuhn’s The Structure of Scientific Revolutions as a guide; and while I expected to mostly skim that section, it set off lightbulbs for me as regards our challenge as investors:

Kuhn goes on to explain that a breakthrough is part of both the past it came from and the future it starts, in the same way that a bend in the road serves as the end of one direction and the beginning of another…The common idea of how a lap of progress happens is a leap of imagination. Kuhn gives us an alternative to imagination that we can apply to realms of achievement other than science. He shows us in detail how the bend in the road happens: a selective combination of elements from the past makes something new. The elements themselves are not new.

This sequence for paradigm shift – achievement, than theory – is exactly backward from common ideas on how progress happens. In the typical notion of the scientific method, first you posit a theory, and then you conduct an experiment to test it. If your experiment works, you have an achievement. But this is the experimental method, not the scientific method. The experimental method is part of the scientific method, but it’s not the first step.

Scientific advance does not come about by a leap of thought to a new theory, but rather from combining specific achievements that lead to a theory, which explains them. It’s an act of combination, not imagination. Specifically, it’s the selective recombination of previous elements into a new whole. Pieces of the past come together to make a new future.

I think as entrepreneurs and investors consider the future, they should be singularly focused on the question: what has actually been achieved?

For example, Webvan – one of the public failures of the dot com era – was predicated on the assumption that the web (html/desktop) was a sufficient enough technological achievement to enable on-demand last mile delivery. It turned out that while the internet did facilitate real-time transfer of information, the opex of managing trucks and warehouses was prohibitively expensive (communication was not yet ubiquitous enough to enable crowdsourcing). The working assumption for this decade’s last mile players (Deliv, Postmates, Doordash) is that the GPS-empowered smartphone is a strong enough achievement to enable cost-effective crowdsourced delivery.

This take on experimentation versus achievement touches on broader themes I’ve been struggling with such as bitcoin and virtual reality. Is Facebook’s acquisition of Oculus an achievement for VR or merely an incremental step in its experimentation period? I am of the belief that VR is still deeply experimental and that technological progress, especially for consumer facing technologies, are rarely “achievements” until their presence has become ubiquitous through a native or modular platform layer (web, and more recently mobile are the two ultimate examples of this).

Bitcoin, on the other hand, may actually be somewhat silently transitioning into its achievement phase. Were historical increases (or recent surges) in bitcoin’s market price “achievements?” No, of course not – bitcoin is a commodity – the price is utterly irrelevant. But I’ve begun encountering increasing number of companies that have integrated the blockchain for accounting, certification, escrow, location or identification, often without advertising themselves as “blockchain companies,” but simply because blockchain tech enables them to optimize a process they couldn’t without it. While price is largely irrelevant, broad adoption of platform is not.

As entrepreneurs and investors, it’s a heck of a lot of fun to imagine the future. But bets on the future need to be predicated on a discerned analysis of the present. This approach was a new, nuanced insight for me into the question of “why now?” and I hope its helpful to readers as well.


The Birth of the Web 3.0 Rollup

Many of the first generation of large internet properties – often termed Web 1.0 (in my mind roughly 1998-2007) – were holding companies employing roll-up strategies, acquiring and aggregating multiple brands. Everything from IAC’s (NSDQ: IAC) entry into digital media, to GSI Commerce, Liberty Media Group, even XOXO Group (NYSE: XOXO). The purpose, in many cases, was to become a category killer – not simply operating a single product, or targeting a specific demographic – but owning an entire category: fitness, makeup, weddings, holidays, etc.

The years that followed brought a host of unbundling. Entrepreneurs recognized the underleveraged value that could be unlocked by catering to specific groups of customers within platforms such as Craigslist or eBay. This trend brought us Airbnb and Etsy but also lots of single purpose or vertical/niche specific apps that failed to reach venture scale. I wrote about this recently in The Market Has Spoken: Go Horizontal not Vertical. But it was unclear to me at the time what those horizontal extensions might look like.

The Return of the Rollup

Earlier this week, Victorious, a publisher of more than 100 apps targeted to micro-communities of superfans, announced a $25M Series B round of funding. Six months prior, Massdrop, a platform that hosts dozens of transactional micro-communities around certain hobbies and products raised a $40M Series A from August Capital. Around the same time, Amino Apps, a creator of a wide range of interest specific forums raised its Series A from USV and Venrock. ReplyYes, a fast growing SMS-based curator for niche products recently expanded from targeting just vinyl enthusiasts to comic books fans, with more to come.

While this studio model of launches is commonplace in the gaming world, it’s a new development in the social category, and a reversion to an older model in commerce. It also appears to be a compromise of sorts (not a rejection) around the emergence of single purpose apps or vertical specific commerce – an affirmation that they do provide a benefit to end users, but an admission that on an individual basis their reward did not justify venture investment.

Around 2.5 years ago, I met a successful entrepreneur who had built a $200M revenue “category killer” business in the mid 2000s and believed he could build the same in what was otherwise a terribly noisy category. I was skeptical about the market but passionate about the entrepreneur and had him spend time with the entirety of our team. In selling the deal to my partnership, I focused on selling his operational excellence and impressive cash efficiency in spite of the headwinds in the space. But we passed, partially because of the market, but also partially because of the multiple business lines he was growing simultaneously – all interconnected, but each with unique marketing and operational challenges – which suggested to us a lack of clarity and focus.

That company is now doing extremely well, and our reason for missing the investment is because I failed to comprehend (and subsequently sell) his founding insight: that his niche specific competitors would quickly stagnate on growth and face increasing acquisition costs. That a category killer or roll-up strategy would likely win: enabling him to keep growth consistent & cash efficient and cross-market different brands to his existing, tangentially related customers.

I won’t make that mistake again.

Now that I’ve seen the roll-up/studio/category killer model begin to play out again in certain social and commerce categories, I’m still left wondering if or how it plays out in b2b SaaS, especially given the constraints around scaling up sales teams. I’ll leave that as an open question for the future.

The Economics Underlying Chatbot Mania

Over the last several weeks, we’ve reached peak AI/Bot mania. Most of the conversation has centered around Chatbots and the potential emergence of a new platform/distribution layer. If you’ve been mostly ignoring the press, some good reads are:

Tl;dr – the major takeaways are as follows: given that consumers don’t really download apps anymore, brands & retailers have a new access point to end consumers, sitting on top of existing messaging platforms and leveraging chatbots to ensure mass scale. The truth is that the chatbot platform conversation is really just an extension of the one we had about a year ago during the emergence of Magic/Operator and SMS as the new platform, which we discussed in Are We Already Rebundling Mobile. An important extension given that such bots have been democratized and can now be spun up not just by tech companies, but by traditional retailers (on their own or within Messenger) or even by individuals such as you and me.


All @TayTweets joking aside, the more intriguing aspect this time around is that Artifical Intelligence and Machine Learning have improved by leaps and bounds. A few articles that reflect this point are: Why AlphaGo Is Really Such a Big Deal, The Current State of Machine Intelligence, Can Machine Learning Predict a Hit or Miss on Estimated Earnings, and The Humans Hiding Behind the Chatbots

But again, the vast vast majority of analysis has focused on bots living within the worlds we frequent (messaging & SMS) and the platform implications. SO – I wanted to spend a few paragraphs to quantify and explore the effects of these advancements from a unit economic or business perspective. The big ones are twofold in my mind: (1) Properly executed AI can transform certain human capital marketplaces from operating as take-rate businesses and transition them into high gross margin software businesses. (2) Chatbots in their current function as customer service agents can make a material impact on contribution margin & overall EBITDA if they can successfully remove the customer service expense line.

20% Take Rate —-> 90% Gross Margins?!?!?

First, some context. About four months ago, writing in “The Middleman Strikes Back” I suggested:

“It’s clear why a hybrid AI/machine learning model is the holy grail for several verticals – replicating [a high] level of personalized service while minimizing overhead labor costs and maintaining extraordinary software level gross margins.”

But in fact, I was wrong. Dennis Mortenson, founder of in “The Humans Behind the Chatbots” believes that the hybrid in “hybrid AI /ML” should be minimized even further…to zero:

“The two scheduling e-mail bot companies have divergent plans for expansion. Clara, which is slowly letting people off its waitlist and said it currently serves hundreds of companies, charges $199 per month per user., on the other hand, plans to move from limited beta to a public release later this year and wants to charge about $9 per month. Dennis Mortensen, its founder, wrote in an e-mail that “only a machine-powered agent can take on the 10 billion formal meetings that U.S. knowledge workers schedule every year.” Mortensen said the service will start asking e-mail senders to clarify when the computer can’t interpret an message—“Did you mean Monday, April 4?”—instead of having an employee read it and infer. “We want to give the job away for free, or for $9, which you can only do if it’s software,” he said.”

Executing on Dennis’ vision, by removing human labor in the middle, you have effectively transformed a take-rate marketplace into a high margin software business, while managing to provide a similar product. Here are examples of some of the service/agent businesses* that could see their economics transform towards 80-90% gross margins when fully leveraging AI:

The Unit Economics Underpinning the AI

Additionally, the company’s variable costs (ie the cost to provide each incremental production or engagement) will transition from significant (paying humans) to immaterial (software cloud hosting fees) enabling a much lower cost of servicing demand, should these companies choose to lower prices. Doing so could expand the audience for such products tremendously.

While I was initially a big skeptic of, the combination of a product that increasingly works with minimal human intervention and a product priced to undercut the market tremendously at $9/mo is a proof that this transition from human marketplace to pure software is already underway.

Further, there’s even an even bigger opportunity in play. If Matt Turck’s suggested “data network effects” take hold many of these service marketplaces – few of which are operating in actual winner take all markets – could be replaced by a software layer, leveraging data network effects so strong that those markets actually become winner take all.

$$$ Massive Value Creation in Public Markets

Again, this is mostly a theoretical exercise, but let’s imagine for a moment the value creation consequences of fully automated chatbots successfully managing 100% of a company’s customer service interactions.

Here are a handful of examples of some companies you might be familiar with showing their current enterprise value, approximately how much they spend on human capital customer service and the effective sensitivity in their valuation if they could maintain their current level of customer service via no-cost** chatbots:

The Unit Economics Underpinning the AI2

It’s not clear to me this is the optimal way to assess the economic value of chatbots – I kind of doubt anyone knows yet – but what is clear is that the effect to profitability would be tremendous. Massive. And that’s just by automating customer support alone.

Importantly, and intentionally, this exercise ignores the real, global cost of losing so many service level jobs from the economy. I’m not qualified to assess that cost nor its effect. There are many opinions in the market for what an AI economy might look like; Roy Bahat at Bloomberg Beta recently offered an intriguing one to The Twenty Minute VC – a futuristic marketplace actually placing a premium on any human manufactured products or human assisted services because of its rarity. There are others as well. Much discussion around AI is conjecture, but its economic effects are a very serious business.

* Certain of these companies do not publicize take-rate or offer non take-rate subscription models. In these cases, gross margins have been assessed from public interviews, or estimated based on reported hourly worker wages and expected throughput.

** This assumption is implausible as there would still be some associated costs related to the software itself and managing/customizing the software on a daily basis.

Special thanks to Dan Abelon for his feedback on this piece.

Ideas Matter

The debate amongst venture capitalists over whether to prioritize markets or people in investment decision making is as old as the industry itself.

Fred Wilson, famously authored an oft quoted 2004 blog – Execution Matters, Ideas Don’t – which referenced USV’s failed incubation of FaveMail leading to the following conclusion:

“The lesson i take away from the whole thing is great ideas don’t make great investments – great entrepreneurs do.”

Three years later, Marc Andreessen, co-founder of Andreessen-Horowitz, opined differently in “On product/market fit for startups,” noting:

In a great market — a market with lots of real potential customers — the market pulls product out of the startup.

The market needs to be fulfilled and the market will be fulfilled, by the first viable product that comes along.

The product doesn’t need to be great; it just has to basically work. And, the market doesn’t care how good the team is, as long as the team can produce that viable product.

Most recently, University of Chicago Professor Steven Kaplan in his 2009 study “Should Investors Bet On the Jockey or the Horse” in the Journal of Finance concluded:

The results for both of our samples indicate that firms that go public rarely change or make a huge leap from their initial business idea or line of business. This suggests that it is extremely important that a VC picks a good business. At the same time, firms commonly replace their initial managers with new ones and see their founders depart, yet still are able to go public, suggesting that VCs are regularly able to find management replacements or improvements for good businesses.

In spite of Andreessen’s comments, I would say that the working consensus in the early/seed stage venture world for the duration of my tenure has been to bet on special people and let the rest fall into place. That approach was verbalized in my interview with David Hornik on this blog where he outlined his investment approach as “people, people, markets, people.”

But the world is changing. The vast majority of companies I’m referred to are largely derivative ideas of larger tech/startup competitors. Problematically, this is actually fundamentally different than going after large monolithic incumbent corporates in a given space. For whatever reason I think it’s because entrepreneurs are trying to improve processes/better execute on problems rather than re-imagining the reasons those habits/processes even exist.

My sense is that the market is cycling back to the importance of bold, unique, creative ideas – above and beyond the obvious focus on “big markets” or “founder/market fit.” Of course people still matter, just as a large addressable markets have always mattered, but in my estimation there is a subtle yet undeniable shift of interest away from improved processes/products towards ideas that challenge the fundamental assumptions underlying the existence of those processes or products themselves.

In that vein, I was recently asked to prep some talking points for a firm offsite on the state and challenges of investing in consumer tech. I’ve attached those slides here. The theme was very clearly that we are in the “reimagination” stage of venture and my observation is that companies solving pain points but not re-imagining or re-conceptualizing consumer behavior are out of favor with top tier investors.

[Note: It’s always nerve wracking putting detailed thoughts into the market – I’m sure a lot of people disagree with me – but I do appreciate any feedback and counter-examples of places where I’m wrong.]

The Quiet Things That No One Ever Knows

It’s always been easy to feel invincible.

I’ve been blessed in life: growing up in an upper middle class family, well connected, always a safety net to catch me if I fell. I met my extraordinary wife in a bar: all I had to do was say hello and sparks flew. I found online poker during an era when money literally grew on trees and won enough to basically finance the past decade of my life. I’ve worked hard – and I’ve hustled like hell – but I’ve been invincible: I got what I wanted. Sure there was volatility, but its been largely muted.

And then two months ago my friend Adam London, rising star in the Chicago tech community and all of 27 years old, simply never woke up. If only it were an isolated case. Six months prior, one of my wife’s closest friends woke up to find a lifeless husband, leaving behind a pair of 18-month old twins. Twins who will never have the opportunity to tell their father they love him.

It was a wake up call. We’re not invincible. And when things are stable, it’s so easy to forget and think we are. In reality, we’re constantly being humbled by life – it’s just that most of us are too obtuse to recognize it.

Concurrently, over the past few months I’ve been increasingly humbled in my venture career as well. Starting out, when you’re largely reactive and responding to directives from superiors it’s easy to do a good job – but as you take on more responsibility, mistakes become more frequent, and costly. Look, investors are really good at projecting strength and confidence – and that’s made easier by Twitter and short form content.  But I’ll just be honest: the last few months have been a struggle. I’ve failed myself and my firm on multiple occasions. There was a deal we should have won that we lost – and I was leading the charge. There was a company I should have been hawking over but was too lax – and they let some fundamental issues get out of hand. And there was a company we should have spotted and invested in, but I failed at selling my partnership properly both on upside and urgency and the opportunity slipped away.

Further, as one grows up in the venture business, structural constraints become increasingly relevant and incongruous. A mentor of mine once advised me that “venture is a terrible business for junior people.” The paradoxes are endless: any credible entrepreneur would prefer to work with your senior partner. And yet the goal is to become a senior partner. Any credible LP would prefer to talk to your senior partner. And yet the goal is to be a compelling fundraiser oneself.

Put it all together and it’s not hard to lose a bit of swagger and confidence.

When I was 19 and beginning to play poker at increasingly high stakes, I purchased and read Doyle Brunson’s classic instructional book Super System. Doyle is one of the winningest players in history, owner of multiple WSOP bracelets, and yet for years I always found the following section absolutely ridiculous:

After I’ve won a pot in No Limit … I’m in the next pot regardless of what two cards I pick up. And if I win that one … I’m always in the next one. I keep playing every pot until I lose one. And, in all those pots, I gamble more than I normally would.

If you don’t play that way … you’ll never have much of a rush. I know that scientists don’t believe in rushes … but they make about fifteen hundred a month. I’ve played Poker for almost 25 years now … and I’ve made millions at it. A big part of my winnings came from playing my rushes.

There’s only one world class Poker player that I know of who doesn’t believe in rushes. Well, he’s wrong … and so are the “scientists”. Besides, how many of them can play Poker anyway?

If you want to take the money off … I mean, make a big score … then, you’ve got to play your rushes. It’s that simple.

The section is absurd for all the obvious reasons. It’s absurd because it’s mathematically implausible. It’s absurd because subsequent hands are statisically uncorrelated to the ones prior. It’s absurd because it’s equivalent to the “hot hand fallacy,” in sports – which has, for all intents and purposes, been debunked.

And yet – in spite of those absurdities – it’s true.

Life is a series of streaks. Hot streaks. Cold streaks. Some simply too imperceptible to matter.

What’s underlying these (anecdotal) streaks? In poker, your opponents react to you differently. If you’re winning – and everyone knows it – your mere presence demands/manifests respect. Opponents make suboptimal decisions, allowing emotions to cloud their thinking. They will outlevel themselves, incorrectly assuming you’re operating on a higher plane and lose sight of the reality in front of them.

In venture, success –whether due to innate brilliance or dumb luck – furthers brand which has the effect of enhancing network, reputation and trust. Failure cultivates the opposite. Both have material consequences.

I’ve always believed that the game of poker is a microcosm of life more boradly. And poker, like life, like venture, is a game of variance and swings. They key is remaining emotionally balanced and self-aware while building an authentic internal confidence and positivity. Never easy, always humbling – anything but invincible – but real.

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