CEO Blog: You Can’t Copy Commitment

When I was a startup entrepreneur I was often asked, “Why won’t Microsoft or Google just copy what you are doing and crush you?”


It’s a good question. So good that Aspire asks it when we invest. And there is an answer: What can’t be copied is culture and commitment.

Just look at companies that have created great processes like Toyota, who adopted Six Sigma to create the Toyota Way, or Ikea which completely rethought how furniture was going to be developed, shipped, packed, and showcased in a store. It is the culture of the organization and the commitment to that culture which made the revolutionary process possible.

Culture is a slippery concept, but its importance isn’t. Just look at Six Sigma, the highly structured method for removing defects and minimizing variables in business processes. The process is far from secret—you can take a class to become a certified practitioner, and many do. Yet most companies that try to adopt it fail.

Satya S. Chakravoty—a Six Sigma expert who has advised companies such as Beech-Nut, Lockheed Martin, and Heinz—conducted a study of why companies fail at the practice so often. He found that firms fail to change the company culture to the point where they can effortlessly execute the process without having a consultant to look over their shoulders. Without a consultant on site, employees often abandon Six Sigma because they think it’s slowing them down.

And so thousands of little decisions that aren’t part of the Six Sigma process go differently than they would in a company like Toyota where culture and commitment automatically lead to better decisions.

I don’t mean to imply it’s easy—building a culture and adhering to a level of commitment isn’t. Look at Amazon’s dedication to plowing money into R&D. The market treated Jeff Bezos like a discount piñata because Amazon, though cash rich, wouldn’t declare dividends. But Bezos thought dividends were tax inefficient. That waste of money would take away from funds that could be used for R&D and growth.

I think any other CEO without that commitment to freeing every possible dollar for R&D would have caved in to market pressure. Imagine what he had to put up with. But the results are hits like the Echo Alexa voice control, which has sold even more briskly than Amazon anticipated, and makes it easier for Amazon to provide products and services to customers.

Which brings us back to Aspire and our question – “Why won’t Microsoft or Google just copy what you are doing and crush you?”

As an example, I’d say take a look at our commitment to changing healthcare, an industry that is so resistant to change.

We have a culture that puts prediction powered by AI as a pillar of our approach. That is why we’ve been dedicated to efforts like predicting future blood sugar levels of diabetics. It’s a critical number for them to know, and the ability to accurately forecast future blood glucose levels is crucial to avoiding deadly comas that come from unexpected blood sugar dips. The rest of the industry has been trapped in a deluge of charts and graphs they want users to look at to manage their blood sugar—instead of the hard thing which is actually creating an algorithm that accurately makes the prediction. We are up against giants like the partnership between Medtronic and IBM, which have thrown unknown millions at the problem.

But in less time, with a smaller team, and at a faction of the cost, we have achieved amazing results with our predictive AI. 

It’s because it is baked into our culture that we make predictions, not charts. Often it means going against the grain and even ignoring end users who ask for the charts! Even with prediction and AI as part of our culture, when you look at the team working on the problem, from the marketing copy writer to the lead programmer, we have 1000 opportunities a day to make little decisions that would turn the product into a charting product instead of a prediction product.

Our commitment to predictions over charts is just one component of our culture that keeps us on track. To bring transformative products to market, it also takes a lean approach and discipline to not get caught up in adding cool but unnecessary features that distract from a product’s core. And that’s where other groups without a committed culture often make the wrong decision.

So the answer to why we can’t be copied and crushed? Our culture can’t be copied, and a long string of right decisions is hard to crush.

CEO Blog: The Unicorn Apocalypse

In the past year the creation of “unicorns”—startups valued at $1 billion or better—doubled from 4 to 8. It raises a question: Are we really minting twice as many great companies as we were just a year ago?

The short answer is no.

What has increased are the deals where big brand VCs overpay for shares to drive a company’s value to Unicorn levels—on paper, anyway. It’s a strategy that can pay off for the big brand VCs, who’s returns are guaranteed, but at the expense of the other investors.

It is a legal form of market rigging, and unless you are a big brand you can prepare for the wins to come off of your high-flying unicorn investment, and for it’s value to come crashing back to earth. In fact, it’s already beginning—but I’ll get to that in a minute.

To see how this works, take a look at Square, which makes a mobile phone credit card reader and processes card payments for tiny businesses.

Helmed by Twitter CEO Jack Dorsey, Square was a VC darling nearly from the start. In 2012, after it’s D series round of funding, Square was valued at $3.25 billion. Two years later, after its E series round of funding, it was up to $6 billion. It’s a remarkable jump when you look at how investing traditionally worked. 

In the past, investors from early stage angels to later stage VCs, argued for the lowest price possible on shares in a company it invested in to get a greater percentage of ownership for their money.

Now, when a late stage VC comes along, it may seek to overvalue a company—paying more than it may really be worth—basically agreeing to say the company is worth billions, even if it isn’t.

Why the turn around, offering top dollar for shares, instead of trying to strike a bargain? 

At Square, their E Series investors were a pretty canny lot, including the Government of Singapore Investment Corporation (GIC), Goldman Sachs, and Rizvi Traverse Management.

When big players like those pay a higher valuation, it makes other investors think the big players know something. After all, these guys are insiders—they must know something no one else does, right? That perception magically drives the share price up. The company’s profile rises, it gets even more press, and it becomes easier to bring in new investors. Or to be less charitable, to lure in suckers.

But wait, won’t GIC, Goldman Sachs, and Rizvi Traverse Management take it in the gut if the shares are overpriced?

Nope. Because those Series E investors were protected by a mechanism called a ratchet, which guarantees their returns.

TechCrunch did a good job unpacking Square’s S-1 filing, which showed that Square Series E investors paid $15.46 per share for Preferred Stock. The ratchet guaranteed the Series E investors an IPO price of at least $18.56, roughly a 20 percent profit.

If the IPO went out at less than $18.56 per share, Square had to give Series E investors more shares, enough to reach the equivalent of a 20 percent profit.

When a company issues more shares, it means each share is worth less. So investors not covered by the ratchet have their profits funneled to the Series E guys.

The reason I keep harping on big brand VCs, is because they are pretty much the only ones who can use this strategy. They have enough money to make big purchases that get them sweetheart deals, and they have big enough reputations that they can influence what the rest of the market is willing to pay.

Indeed, when Square went public in November of 2015, it’s IPO price was $9. Ryan Mac at Forbes did the math, which had Square issuing 10.3 million additional shares to the Series E guys. When the stock traded up 45 percent in its first day of trading, those shares were worth $135 million. That $135 million came out of the pockets of the other investors.

You might think I’ve chosen a particularly outrageous example, but a study by Fenwick & West LLP, a law firm specializing in technology, found that “Approximately 30 percent of unicorn investors had significant protection against a down round IPO.”

This is a particularly bad time to be on the wrong end of that 30 percent. The number of startups taking major value hits is high and growing, which means even more pain for unprotected investors.

CB Insights, an analytical firm, runs a site called “The Downround Tracker.” It keeps tabs on prominent startups that are losing value, whether through a “downround,” in which a company sold equity for less per share than in a previous round, a drop in share price, or a “down exit,” in which the startup was sold for less money than it had raised from previous investors.

According to CB insights, there have been 35 prominent examples of startups suffering some form of downround so far this year. Ten of those are unicorns. To remind you, there have been fewer than 8 new unicorns each year so far.

Among those on The Downturn Tracker is Zenefits, which acts as an internet-based insurance brokerage for small businesses. It had been valued as high a $4.5 billion, but Downturn Tracker recently put it at $2 billion.

A recent New York Times story dissected the drop in value, driven partially by a CEO resignation and examination by insurance investigators, and partially because of unrealistically pumped up prospects.

The Times came to the conclusion that the Zenefits crash was “a defining scandal of the tech boom.”

I wish. The Unicorn Apocalypse has barely begun.

Moore’s Law is Coming to an End, and that’s OK.

In 1965, Intel co-founder Gordon Moore observed that the number of transistors that could fit on a silicon wafer doubles about every two years, an observation that later became known as Moore’s law. Since then, the tech industry has been able to happily rely on continuously improved processing speeds and power for computers that are getting smaller and smaller. That has resulted in an endless stream of software improvements, as well as new hardware like mobile devices and sensors that have changed the world as we know it. 

But according to recent headlines, computing experts say Moore’s Law could be coming to an end within the next ten years. Silicon-based microchips are reaching the physical limits of how small they can go. Intel’s latest transistor is only about 100 atoms wide, and at its current trajectory, it could reach a width of only 10 atoms by 2020. 

As we get closer and closer to the limits of silicon, some technologists are growing worried. But reaching the boundaries of our current paradigm may not be such a bad thing. 

In a recent Motherboard article, Alasdair Allan argues that we’re finally reaching a mature technological base in computing, where computers have become so cheap and so capable that the number of possible applications for technology we already have are nearly limitless. 

“While we might no longer be expecting computing to become orders of magnitude faster, we may well have reached the point where that doesn’t matter any more,” Allan writes.

Learning to use well the technology we have will mean that the real innovators will be focused on optimizing our current computing systems and integrating disparate technologies to create greater data connectivity. That shift in focus, as Howard Yu argues in a recent Fortune article, will likely disrupt the current pecking order within the tech industry. As innovation becomes increasingly nuanced, new windows of opportunity will open up to change the game. 

“The end of Moore’s Law will not be the end of the IT world, but it will demand new ways to make better machines,” Yu writes. 

Here at Aspire, our chief software architect Thomas Rogers says that reaching the limits of silicon will be a positive for innovation and for computer science as a discipline. 

Firstly, Rogers says, the limits of what we can do are with the current technology and the current mediums. And usually when we reach a limit, Rogers says, we eventually see a radical reinvention

Already scientists are exploring theoretical and even practical implementations of new computing subsystems like quantum computing, which would in effect reset the bar for a new era in computing. But until then, developers, programmers, and engineers might have to change their approach to adapt to a plateau in computing power.

For any of the disciplines that have to use computing horsepower as a resource, Moore’s law has been an unprecedented luxury, Rogers says. 

“If we were building automobiles it would be like we’ve had the luxury of Exxon or Mobile coming up with more efficient, lower cost, higher octane fuels every year, year after year. In computing, we’ve grown complacent about building efficient algorithms because we didn’t have to,” Rogers says.

“Once that performance curve starts to slow or flatten, it’s going to force people back into being craftsman at what they do. You can’t be sloppy any more, and I look at that as a very good thing.”

That’s why at Aspire we’ve been focused on the fundamentals, Rogers says, architecting better technological building blocks that can be used to assemble more efficient systems. And that’s why we’ve been filling our bench with developers who know how to develop effective, efficient, high performance tech. 

Aspire’s artificial intelligence expert and Entrepreneur in Residence Mike Monteiro agrees that we’ll need computers to work smarter, not harder.

“Let’s suppose Moore’s law stops or slows down and we’re just sort of stuck with the computational power that we have now,” Monteiro says. “That means you have to be smarter with each compute cycle, you can’t just lazily rely on brute force.” 

And that means that algorithms need to be smarter about the resources they use, Monteiro says, without relying too heavily on memory or long calculations.

At Aspire, Monteiro says, we’re in the business of making algorithms smarter with our adaptive artificial intelligence platform A2i, a technology that optimizes algorithms to help them arrive at better answers more quickly.

A tool like A2i, built to make algorithms smarter and more efficient, will be a huge advantage in a post-Moore’s-law world. 

A Lesson from HBO’s Silicon Valley: How to Avoid the Mistakes of Pied Piper

For tech insiders who watch the HBO series Silicon Valley, industry culture jokes about Scrum or the imaginary “conjoined triangles of success” usually hit pretty close to home. But the fine attention to detail doesn’t just make for better comedy, the realism also results in some important lessons that many tech startups fail to learn before it’s too late. (Warning: spoilers ahead)

As Matt Weinberger points out in a recent Business Insider article, this season’s penultimate episode nails a very important lesson that many tech startups overlook: designing products without getting real user feedback early on can result in a fundamentally flawed product. In that episode, titled “daily active users”, Pied Piper is flying high on a major wave of industry buzz until they learn that their daily active user numbers are abysmal.

The problem? They only tested their revolutionary compression platform with other engineers and never bothered to gain insights from regular users. Despite the blood, sweat, and tears the Pied Piper crew put into building the platform and overcoming just about every hurdle imaginable, the consumers had the final say on whether Pied Piper’s technology would live or die in the marketplace.

Sadly, many startups develop products that share a similar fate to that of Pied Piper’s file compression platform. Months or even years of development time can be wasted because a startup failed to test their assumptions with consumers first.

So what can companies do to avoid wasting development time on products that no one wants to use? At Aspire Ventures we’ve found that using “Design Sprints”—a 5-day rapid prototyping method that culminates in testing with real consumers—has been an effective tool to gain valuable consumer insights early on in the process. The regimented approach, developed by Google Ventures, is a great way to test your riskiest assumptions and find out whether or not you’re on the right path.

With some tweaks to make the sprint our own, ventures at Aspire are applying the method at the beginning of the development process—prototyping concepts to find out if we should move forward with development.

Most recently, Tempo Health held a Concept Sprint to create an app that would help type 1 Diabetes patients reduce their number of hypoglycemic and hyperglycemic events (when blood sugar levels go too low or too high, respectively). After just five days, a cross-functional team laid down the framework for an app that would alert users at certain times of the day about high and low patterns in their blood glucose levels based on historical data. The app also includes motivational tools like a running clock of the user’s time within normal range of blood sugar levels, and a leaderboard where they can compare their time in range with other users.

At the end of the sprint, we tested the concept with close to twenty type 1 diabetes patients by walking them through several screens of our prototype. The response was overwhelmingly positive, and now our development team is moving forward to create a beta version of the app.

By taking just five days to hash out the design and test it with users, we gained extremely valuable feedback that will inform the rest of our development process. And we can be sure we’re not wasting valuable resources on a product that no one wants to use.

CEO Blog: There is a Reason They Call Them Unicorns

When I started in the technology business, first as an entrepreneur, then as an investor, the common wisdom was that if you had 10 investments, you expected 7 to tank, two to achieve modest success, and one home run that made up for the others, and then some. And this concept is still largely accepted.

Only, it’s wrong. It was wrong then, and it’s more wrong now.

To understand how completely wrong it is, you only have to look at the numbers.

That imagined one-in-ten home run is now known as a unicorn—a start up with a $1 billion or better valuation (whether or not it is actually producing revenue).

And that one-in-ten figure is every bit as mythological as the unicorn itself.

According to research by Aileen Lee, founder of seed capitol firm Cowboy Ventures, there are currently 84 U.S.–based unicorns. On average, eight unicorns have been created each year for the past decade.

Best estimates put the number of companies getting venture money at about 6,000 a year, or 60,000 in that same 10-year period.

That means odds of 84 in 60,000, or 1 in 714, which is 0.14 percent. The earth has a better chance of being hit by an asteroid than investors have of funding a unicorn (in which case you will have wished that you invested in Space-X).

Even one of the biggest, most influential, smartest VC firms, Kleiner, Perkins, Caufield and Byers reportedly had a period from 2000 till after 2008 where none of their funds had positive returns.

And we are not even accounting for angel investors, who put money in an average of 20,000 tech start ups per year. 84 unicorns out 200,000 investments!

VCs will tell you investing is more an art than a science. My point is, if you look at the results, they are really, really shitty. Maybe all investors throw away that whole “more of an art than a science thing” and admit that up to this point there is a big element of dumb luck. Whether you are in Airbnb at the right time is largely luck. Airbnb: it was a dog until it wasn’t. It was at death’s door many many times, then magic happened.

So what do we do about the unicorn problem?

Well, in my case, you start Aspire Universal.

The idea is to see if we can create a system to reliably produce consistent big winners. If this were baseball I’d say the objective is still to hit home runs but with a much higher batting average and fewer strike outs … and the only way to do this is to have a consistent technique.

So, that doesn’t mean we shouldn’t be seeking unicorns – we should help build them!

If you’re still not convinced that “a system for creating great companies” is a better play than an experienced tech who claims to know the “art of picking unicorns” … we’ll discuss more in my next post.