Month: March 2018

 

It’s easier than ever to build software, which makes it harder than ever to build a defensible software business. So it’s no wonder investors and entrepreneurs are optimistic about the potential of data to form a new competitive advantage. Some have even hailed data as “the new oil.” We invest exclusively in startups leveraging data and AI to solve business problems, so we certainly see the appeal — but the oil analogy is flawed.

In all the enthusiasm for big data, it’s easy to lose sight of the fact that all data is not created equal. Startups and large corporations alike boast about the volume of data they’ve amassed, ranging from terabytes of data to quantities surpassing all of the information contained in the Library of Congress. Quantity alone does not make a “data moat.”

Firstly, raw data is not nearly as valuable as data employed to solve a problem. We see this in the public markets: companies that serve as aggregators and merchants of data, such as Nielsen and Acxiom, sustain much lower valuation multiples than companies that build products powered by data in combination with algorithms and ML, such as Netflix or Facebook. The current generation of AI startups recognize this difference and apply machine learning models to extract value from the data they collect.

Even when data is put to work powering ML-based solutions, the size of the data set is only one part of the story. The value of a data set, the strength of a data moat, comes from context. Some applications require models to be trained to a high degree of accuracy before they can provide any value to a customer, while others need little or no data at all. Some data sets are truly proprietary, others are readily duplicated. Some data decays in value over time, while other data sets are evergreen. The application determines the value of the data.

Defining the “data appetite”

Machine learning applications can require widely different amounts of data to provide valuable features to the end user.

MAP threshold

In the cloud era, the idea of the minimum viable product (or MVP) has taken hold — that collection of software features which has just enough value to seek initial customers. In the intelligence era, we see the analog emerging for data and models: the minimum level of accurate intelligence required to justify adoption. We call this the minimum algorithmic performance (MAP).

Most applications don’t require 100 percent accuracy to create value. For example, a productivity tool for doctors might initially streamline data entry into electronic health record systems, but over time could automate data entry by learning from what doctors enter in the system. In this case, the MAP is zero, because the application has value from day one based on software features alone. Intelligence can be added later. However, solutions where AI is central to the product (for example, a tool to identify strokes from CT scans), would likely need to equal the accuracy of status quo (human-based) solutions. In this case the MAP is to match the performance of human radiologists, and an immense volume of data might be needed before a commercial launch is viable.

Performance threshold

Not every problem can be solved with near 100 percent accuracy. Some problems are too complex to fully model given the current state of the art; in that case, volume of data won’t be a silver bullet. Adding data might incrementally improve the model’s performance, but quickly hit diminishing marginal returns.

At the other extreme, some problems can be solved with near 100 percent accuracy with a very small training set, because the problem being modeled is relatively simple, with few dimensions to track and few variations in outcome.

In short, the amount of data you need to effectively solve a problem varies widely. We call the amount of training data needed to reach viable levels of accuracy the performance threshold.

AI-powered contract processing is a good example of an application with a low performance threshold. There are thousands of contract types, but most of them share key fields: the parties involved, the items of value being exchanged, time frame, etc. Specific document types like mortgage applications or rental agreements are highly standardized in order to comply with regulation. Across multiple startups, we’ve seen algorithms that automatically process documents needing only a few hundred examples to train to an acceptable degree of accuracy.

Entrepreneurs need to thread a needle. If the performance threshold is high, you’ll have a bootstrap problem acquiring enough data to create a product to drive customer usage and more data collection. Too low, and you haven’t built much of a data moat!

Stability threshold

Machine learning models train on examples taken from the real-world environment they represent. If conditions change over time, gradually or suddenly, and the model doesn’t change with it, the model will decay. In other words, the model’s predictions will no longer be reliable.

For example, Constructor.io is a startup that uses machine learning to rank search results for e-commerce websites. The system observes customer clicks on search results and uses that data to predict the best order for future search results. But e-commerce product catalogs are constantly changing. A model that weighs all clicks equally, or trained only on a data set from one period of time, risks overvaluing older products at the expense of newly introduced and currently popular products.

Keeping the model stable requires ingesting fresh training data at the same rate that the environment changes. We call this rate of data acquisition the stability threshold.

Perishable data doesn’t make for a very good data moat. On the other hand, ongoing access to abundant fresh data can be a formidable barrier to entry when the stability threshold is low.

Identifying opportunities with long-term defensibility

The MAP, performance threshold and stability threshold are all central elements to identifying strong data moats.

First-movers may have a low MAP to enter a new category, but once they have created a category and lead it, the minimum bar for future entrants is to equal or exceed the first mover.

Domains requiring less data to reach the performance threshold and less data to maintain that performance (the stability threshold) are not very defensible. New entrants can readily amass enough data and match or leapfrog your solution. On the other hand, companies attacking problems with low performance threshold (don’t require too much data) and a low stability threshold (data decays rapidly) could still build a moat by acquiring new data faster than the competition.

More elements of a strong data moat

AI investors talk enthusiastically about “public data” versus “proprietary data” to classify data sets, but the strength of a data moat has more dimensions, including:

• Accessibility
• Time — how quickly can the data be amassed and used in the model? Can the data be accessed instantly, or does it take a significant amount of time to obtain and process?
• Cost — how much money is needed to acquire this data? Does the user of the data need to pay for licensing rights or pay humans to label the data?
• Uniqueness — is similar data widely available to others who could then build a model and achieve the same result? Such so-called proprietary data might better be termed “commodity data” — for example: job listings, widely available document types (like NDAs or loan applications), images of human faces.
• Dimensionality — how many different attributes are described in a data set? Are many of them relevant to solving the problem?
• Breadth — how widely do the values of attributes vary? Does the data set account for edge cases and rare exceptions? Can data or learnings be pooled across customers to provide greater breadth of coverage than data from just one customer?
• Perishability — how broadly applicable over time is this data? Is a model trained from this data durable over a long time period, or does it need regular updates?
• Virtuous loop — can outcomes such as performance feedback or predictive accuracy be used as inputs to improve the algorithm? Can performance compound over time?

Software is now a commodity, making data moats more important than ever for companies to build a long-term competitive advantage. With tech titans democratizing access to AI toolkits to attract cloud computing customers, data sets are one of the most important ways to differentiate. A truly defensible data moat doesn’t come from just amassing the largest volume of data. The best data moats are tied to a particular problem domain, in which unique, fresh, data compounds in value as it solves problems for customers.

Source: Tech Crunch

There’s currently a shortage of Nvidia GPUs and Nvidia’s CEO pointed to Ethereum distributed ledgers as the cause. Today at Nvidia’s GTC conference he spoke to a group of journalists following his keynote address and addressed the shortage.

Huang simply stated that Nvidia is not in the business of cryptocurrency or distributed ledgers. As such, he stated he preferred if his company’s GPUs were used the areas Nvidia is targeting though explained why Nvidia’s products are used for crypto mining.

“[Cryptocurrency] is not our business,” he said. “Gaming is growing and workstation is growing because of ray tracing.” He noted that Nvidia’s high performance business is also growing and these are the areas he wished Nvidia could allocate units for.

Huang explained why crypto miners are using Nvidia’s products echoing what he told me in an interview last week.

“We’re sold out of many of our high-end SKUs, and so it’s a real challenge keeping [graphic cards] in the marketplace for games,” he said, adding “At the highest level the way to think about that is because of the philosophy of cryptocurrency — which is really about taking advantage of distributed high-performance computing — there are supercomputers in the hands of almost everybody in the world so that no singular force or entity that can control the currency.”

So what is he going to do about it? “We have to build a whole lot more,” he told TechCrunch last week. “The video supply chain is working really hard, and you know all of our partners are working around the clock. We’ve got to come closer to the demand of the market. And right now, we’re not anywhere near close to that and so we’re just going to have to keep running.”

Source: Tech Crunch

Just a day after General Catalyst, the 18-year-old venture firm, revealed plans in an SEC filing to raise a record $1.375 billion in capital to shower on startups, another firm that we’d said was likely to file any second has done just that.

According to a fresh SEC filing, Lightspeed Venture Partners, also 18 years old at this point, is raising a record $1.8 billion in new capital commitments from its investors, just two years after raising what was then a record for the firm: $1.2 billion in funding across two funds (one early stage and the other for “select” companies in its portfolio that had garnered traction).

Still on our watch list: news of bigger-and-better-than-ever funds from other firms that announced their latest funds roughly two years ago, including Founders Fund, Andreessen Horowitz, and Accel Partners.

The supersizing of venture firms isn’t a shock, as we wrote yesterday — though it’s also not necessarily good for returns, as we also noted. Right now, venture firms are reacting in part to the $100 billion SoftBank Vision Fund, which SoftBank has hinted is merely the first of more gigantic funds it plans to raise, including from investors in the Middle East who’d like to plug more money into Silicon Valley than they’ve been able to do historically.

The game, as ever, has also changed, these firms could argue. For one thing, the size of rounds has soared in recent years, making it easy for venture firms to convince themselves that to “stay in the game,” they need to have more cash at their disposal.

Further, so-called limited partners from universities, pension funds and elsewhere, want to plug more money into venture capital, given the lackluster performance some other asset classes have produced.

When they want to write bigger checks to the funds in which they are already investors, the funds often try accommodating them out of loyalty. (We’re guessing the greater management fees they receive, which are tied to the amount of assets they manage, are also persuasive.)

What’s neglected in this race is the fact that the biggest outcomes can usually be traced to the earlier rounds in which VCs participate. Look at Sequoia’s early investment in Dropbox, for example, or Lightspeed’s early check to Snapchat. No matter the outcome of these companies, short of total failure, both venture firms will have made a mint, unlike later investors that might not be able to say the same.

There is also ample evidence that it’s far harder to produce meaningful returns to investors when managing a giant fund. (This Kaufmann study from 2012 is among the mostly highly cited, if you’re curious.)

Whether raising so much will prove wise for Lightspeed is an open question. What is not in doubt: Lightspeed is right now among the best-performing venture firms in Silicon Valley.

In addition to being the first institutional investor in now publicly traded Snap, the company wrote early checks to MuleSoft, which staged a successful IPO in 2018; in StitchFix, which staged a successful IPO in 2018; in AppDynamics, which sold to Cisco for $3.7 billion last year. It was an early investor in Nimble Storage, which sold to Hewlett Packard Enterprise for just north of $1 billion in cash last March. And just two weeks ago, another of its portfolio companies, Zscaler, also staged a successful IPO.

At a StrictlyVC event hosted last year by this editor, firm cofounders Ravi Mhatre and Barry Eggers talked about their very long “overnight” success story, and about the importance of funding companies early to help them set up durable businesses.

It will be interesting to see whether this new capital is invested in more early-stage deals, or the firm sees growing opportunity to compete at the growth stage. Probably both? Stay tuned.

Pictured, left to right: investors Semil Shah, Ravi Mhatre, and Barry Eggers.

Source: Tech Crunch

The smash dockless scooter rental startup, Bird, is expanding beyond its Southern California nest with a new rollout in San Francisco, San Jose, Calif. and Washington, DC, the company said today.

And as his company makes its migration across the country, Bird chief executive Travis VanderZanden is determined not to make the same mistakes that bedeviled his former bosses at Uber .

As part of the rollout, Bird is offering to remit $1 daily for each of its scooters deployed in every city it’s operating in. That’s all part of an outreach effort that Bird is framing as a commitment to “Save Our Sidewalks.”

The initiative, which Bird is encouraging other scooter sharing services like LimeBike, Mobike, Ofo, and Spin to join, includes a commitment to collect vehicles every night; reposition them to meet demand in the mornings; provide regular maintenance; and only add capacity when every vehicle in a fleet is used three times per day.

The dollar per day commitment is a nice attempt by Bird to get in front of tariffs or fees that may be imposed by local jurisdictions which could be far higher. For instance, cities would make far more money charging bird a smaller fee per ride rather than per day.

Bird prices its rides at $1 to rent the scooter and then 15 cents per minute traveled.

The company’s services are already available in Los Angeles, San Diego, and Santa Monica, Calif.

Source: Tech Crunch

Dropbox’s surge on the stock market has continued, with the company going up another 7% on its second day on the stock market.

The company saw its shares close at $30.45, giving the company above a $13 billion market cap, fully diluted.

When it priced its IPO, there was a question as to whether Dropbox would surpass the $10 billion valuation it achieved in its last private round. It eliminated those concerns overnight.

The first few days have been a strong indicator of investor demand for the cloud storage company.

To recap, Dropbox initially hoped to price its IPO between $16 and $18, then raised it from $18 to $20. Then it ultimately priced its IPO at $21, closing the day above $28. And it still continues to go up.

Investors like Dropbox’s improving financials.

It brought in $1.1 billion in revenue in its most recent year. This is up from $845 million in revenue the year before and $604 million for 2015.

Yet while it’s been cash flow positive since 2016, it is not profitable. Dropbox lost nearly $112 million last year. But its margins are looking better when compared with losses of $210 million for 2016 and $326 million for 2015.

Although Dropbox is very different than Spotify which intends to list next week, investors will view this favorable debut as a sign that the IPO window is “open,” meaning that there is strong demand for newly public tech companies.

Source: Tech Crunch

Lerer Hippeau Ventures, the New York-based early-stage venture firm, is taking over the $125 million debut fund created by Binary Capital, a young San Francisco-based venture firm whose cofounder’s misdeeds became the talk of Silicon Valley last summer.

Axios reported the development earlier this morning. Lerer Hippeau tells us it’s not commenting on the news.

The portfolio includes 25 startups, including Bellhops, a five-year-old, Chattanooga, Tn.-based local moving services startup, and Unikrn, a three-year-old, Bellevue, Wa.-based e-sports wagering service that raised $31.4 million via an initial coin offering last fall. (Billionaire Mark Cuban is also an investor.)

What happens to Binary’s second fund is apparently an open question. Jonathan Teo, a cofounder of Binary, didn’t respond to our requests for more information this morning, but Recode reports that firm has “bogged down in various legal matters, including an attempt by Teo to have his fate decided in arbitration.”

Teo’s cofounder, Justin Caldbeck, had brought the firm to the brink of ruin. Last summer, an in expose published by The Information, Caldbeck, who’d previously been an investor with Lightspeed Venture Partners, was accused of making unwanted sexual advances toward six women who said they were groped and propositioned during their professional relationship with him.

Caldbeck initially denied the claims, telling The Information’s reporter, “Go f— yourself.” A day later, he was apologizing for his behavior and, within short order, was forced to resign under pressure.

Teo had apparently hoped to hang on to the firm, which he’s created with Caldbeck in early 2014. Judging by Recode’s report, he’s still fighting to stay involved.

Caldbeck meanwhile showed up at his alma mater, Duke University, last fall to discuss the male-dominated world of finance. “If we’re going to make change, men need to behave better,” Caldbeck told the school newspaper afterward. “Part of what needs to happen is more education around these issues.”

Caldbeck separately told Bloomberg that he planned to release a website dedicated to the topic of “bro culture” and how to address it.

To the relief of his many critics, he appears not to have moved forward with those plans.

Pictured above, left to right: Teo and Caldbeck.

Source: Tech Crunch

Snapchat has ephemeral messages, and now Facebook has ephemeral friend requests. The big blue social network feeds off your social graph, and every time you expand it, it has more content to show you. But if you leave a questionable friend request in limbo for too long, you’ll probably never confirm or delete it. So Facebook is betting that by making those friend requests into exploding offers, you’ll be more likely to accept than lose the opportunity to connect. And if you didn’t want that friend request in the first place, it will self-destruct even if you don’t bother to manually reject it.

On Friday, TechCrunch reader Christine Hudler provided screenshots of a new expiring friend requests feature that gives you a 14 day countdown to make a decision. Now a Facebook spokesperson has confirmed the feature to TechCrunch, writing “I can confirm that this is a test to help surface the most recent requests.” Facebook tells me it’s a way to assist people with managing unwanted friend requests by eventually deleting those people saw but didn’t accept. It’s currently only appearing to a subset of users, not to everyone.

Those in the test group will see a “14 days to respond” countdown on their friend requests. A ‘Learn More’ link leads to this Help Center article we’ve screenshotted here, as it only shows details about expirations to those in the test.

Keeping people’s friend request queue clean is critical to the company because if you can’t find the legitimate ones from people you know amongst all the randos and spam, you might stop growing your graph. Expiring friend requests could also solve a problem for social media stars and other public figures on Facebook. The app only lets you have up to 5000 friends, and a limited number of pending requests that seems to be 5000 minus your friend count (Facebook wouldn’t say). After that, you won’t receive inbound friend requests any more. The expiration date makes it much less likely that you’ll ever hit the pending friend request maximum.

The “limited time offer” trick has been around in shopping forever as way to boost your sense of urgency. Humans love optionality but hate to miss out. People buy things off of infomercials they don’t actually want because if they “ACT NOW!” they’ll get a discount before it disappears. This same approach compels people to open Snapchat so they don’t miss their friends’ Stories that delete themselves after 24 hours.

The feature comes at a time when Facebook is especially sensitive about appearing respectful of your data, following the Cambridge Analytica scandal. Friend requests from total strangers can make users feel like they’re already sharing too much public information, and that one wrong click could expose their friends-only photos and posts. Keeping these requests from piling up could make users feel safer while ensuring they can keep adding real friends.

For more on what’s up with Facebook, read our feature pieces:

Source: Tech Crunch

Smartsheet is the latest company to file to go public, now that the IPO window is open. 

The Bellevue, Washington-based company offers enterprise software for communication and collaboration.

It describes itself as the “leading cloud-based platform for work execution, enabling teams and organizations to plan, capture, manage, automate, and report on work at scale, resulting in more efficient processes and better business outcomes. ”

Smartsheet says it has 3.6 million users and its products are utilized at 90% of the Fortune 100 companies around the world.

It touts clients like Cisco and Starbucks. Smartsheet says Cisco uses it to keep tabs on spending and Starbucks uses it send product and business updates to its thousands of stores.

The company brought in $111.3 million in revenue for its fiscal 2018 year. It’s a big jump from $67 million for 2017 and $40.8 million for 2016.

But losses are also growing, totaling $49.1 million for 2018, up from negative $15.2 million and $14.3 million in prior years.

“We have a history of cumulative losses and we cannot assure you that we will achieve profitability in the foreseeable future,” the company warned in its prospectus.

Smartsheet acknowledges that it competes with Microsoft and Google on spreadsheets and other productivity tools. Its products also compete with Asana, Atlassian, Planview and Workfront.

“The market in which we participate is highly competitive, and if we do not compete effectively, our operating results could be harmed,” reads the “risk factors” section of the filing.

The largest shareholder is Insight Venture Partners, which owned a sizeable 32.1% of the company prior to the IPO. Madrona Ventures owned 28.4% of the company and Sutter Hill Ventures owned 5.4%.

Smartsheet had raised at least $106 million in venture funding, dating back to 2010, according to Crunchbase data. Last year, TechCrunch reported that it had an $800 million valuation.

The company plans to list on the New York Stock Exchange, under the ticker “SMAR.”

Morgan Stanley and J.P. Morgan are managing the offering. Fenwick & West and Wilson Sonsini served as counsel.

The floodgates have opened for enterprise tech IPOs. Last week we saw Dropbox debut and now we’ve seen filings for Zuora and Pivotal. DocuSign is also expected to file in the coming months.

Many of last year’s enterprise tech IPOs performed well, giving pipeline companies confidence in their debuts.

Spring also tends to be an active time for IPOs, with companies looking to debut before the summer slowdown.

And while consumer tech IPOs have been slow for several years now, one of the more anticipated companies looking to debut is Spotify, which is expected to go public next week via a “direct listing.”

 

Source: Tech Crunch