Andrew Yang is running for President to save America from the robots

In the first episode of the new “Interesting People in Interesting Times” podcast, recorded at the March 5th Andrew Yang, tech entrepreneur, founder of Venture for America, and author of The War on Normal People: The Truth About America’s Disappearing Jobs and Why Universal Basic Income Is Our Future, discusses his latest endeavor — vying for the Democratic party nomination to run for President of the United States.

Yang outlines his radical policy agenda, which focuses on Universal Basic Income and includes a “freedom dividend.” He talks about the very real and immediate threat of artificial intelligence, how new technologies are erasing millions of jobs before our eyes, and why we need to put humanity first. He also addresses “the big four” and what he plans to do about Amazon.

During the interview, Yang called out governments inability to address large scale problems and the challenges that technology is creating in modern American society.

“I believe that we need to start owning these realities [of automation and artificial intelligence taking away jobs] and these challenges as a people, as a country, and as a society, and start being honest. I’m running for president to solve the big problems and to show that these things are not beyond us,” Yang says.

Yang’s own plan to address the increasing power tech companies are wielding in the world involves something called a “freedom dividend”, which would paid for by a value-added tax. The revenue from that tax (levied on “gains from the big four”) would be redistributed via the “freedom dividend” to citizens, Yang says.

Yang also discusses his idea for incentivizing social contributions in his vision of a new American society.

“People talk about the things that should be valued, like caring for the elderly, but we don’t pay those people now. Journalism is another example. My plan is to supplement the freedom dividend with a new digital social currency that is meant to map to pro-social activities,” says Yang. “There are many things that the monetary market right now will not value appropriately: raising children, arts and creativity, caring for the elderly, environmental sustainability, even science. Our society does not reward these things appropriately. My plan would be to create a new currency and put the new currency against it. This new currency can be traded in for dollars, but if you [do that] you’d tax it, which no one would do, so you’d hoard them like Amex points. You can trade [these new digital currency points] and have this parallel economy based around things that we know are good.”

Listen to the podcast here:

Here are the top states and cities for startups in the South

The American South may not be the first region that comes to mind when you hear the phrase “hotbed of tech entrepreneurship,” but, slightly misguided perceptions aside, it’s home to a diverse and growing collection of startups.

Here, we’re going to take a deep dive into the startup funding data for the region.

What is “the South?”

Just like it’s a common pastime for many city dwellers to argue about the precise boundaries of neighborhoods, there’s often some disagreement about the exact contours of the U.S.’s various regions. To quash rabble-rousing from the get-go, we’re using the U.S. Census Bureau’s definition of “the South” on its official map of the United States. Below, we display a map of the states we’re going to look at today.

Much like barbecue, the South is not a monolithic concept. So to incorporate some regional flavor into the following analysis, we’re also going to use the same regional divisions that the U.S. Census Bureau uses.

By doing this, we’ll be able to get a better idea of the relative contribution states from each sub-region make to startup activity in the South overall.

The ebb and flow of deal and dollar volume

As is the case with most of the country, the South appears to be experiencing a shift in startup funding as we move toward the latter half of a bull run in entrepreneurial activity. The chart below shows a divergence in overall deal and dollar volume over time.

Much like in the rest of the U.S., reported deal and dollar volume are heading in different directions. Part of this may be due to reporting delays — it can sometimes take a few years for seed and early-stage rounds to get added to databases like Crunchbase’s . Nonetheless, there is a slow and generally upward creep in round sizes at most stages of funding. And that’s not just a Southern thing; it’s a country-wide trend.

Let’s disaggregate these figures a bit. We’ll start with deal counts and move on to dollar volume from there.

A closer look at southern venture deal and dollar volume

In the chart below, you’ll see venture deal volume broken out by sub-region.

Over the past several years, reported venture deal volume has been on the downswing. From a local maximum in 2014 through the end of 2017, it’s down almost 35 percent overall. But that’s not the whole picture. The relative share of deal volume has changed, as well.

Although it’s not immediately clear just by looking at the chart above, startups in the South Atlantic sub-region have accounted for an increasingly large share of the funding rounds. For example, in 2012, South Atlantic startups attracted 54 percent of the deal volume. In 2017, that grows to 64 percent. Startups in the West South Central sub-region have pretty consistently pulled in between 28 and 30 percent of the deals, so where’s the loss coming from? Startups headquartered in Kentucky, Tennessee, Mississippi and Alabama pulled in just 8 percent of deals in 2017, compared to 18 percent in 2012.

It’s a similar story with dollar volume.

In general, dollar volume follows the same pattern, albeit with a bit more variability. Regardless, startups in the South Atlantic sub-region are hoovering up an ever-larger share of venture dollars, and there’s little to indicate that trend will reverse itself any time soon.

Where are the regional hotspots for deal-making in the south?

Let’s see which states accounted for most of the deal volume. The chart below shows the geographic distribution of deal-making activity by startups in each Southern state from the beginning of 2017 through time of writing. It should come as no surprise that much of the activity is concentrated in states with higher populations.

And here’s the distribution of dollar volume among southern states.

Despite some variation in which states are at the top of the ranks, the share of deal and dollar volume raised by startups in the top three states is remarkably similar, coming in at between 52 and 53 percent for both metrics.

The top startup cities in the south

We started by looking at the South as a whole and then drilled into its sub regions and states. But there’s one layer deeper we can go here, and that’s to rank the top startup cities in the South.

In the interest of keeping our rankings fresh and timely, we’re covering activity from the past 15 months or so, from the start of 2017 through mid-March 2018. But before highlighting some of the more notable hubs, let’s take a look at the numbers.

In the chart below, you’ll find the top 10 metropolitan areas where Southern startups closed the most funding rounds.

The chart below shows reported dollar volume over the same period of time.

Much like we saw at the state level, the top five startup cities — ranked by both deal and dollar volume — are the same, although there’s some variation between where each one ranks. In order, the D.C., Austin and Atlanta metro areas rank in the top three for each metric, while Dallas and Raleigh, NC switch off between fourth and fifth place.

Startups capitalize on the nation’s capital

To be frank, Washington, D.C.’s top-shelf ranking was a bit of a surprise. It may be the fact that Austin, TX plays host to South By Southwest, a somewhat more relaxed culture and/or a preponderance of excellent breakfast taco and barbecue joints, but to many — ourselves included — the city feels like it would have a more active startup scene than the nation’s capital. But that’s not exactly the case. The D.C. metro area had more venture deal and dollar volume than Austin for seven out of the last 10 years, and startups based in the nation’s capital have raised more than twice as much money so far in 2018.

D.C.-area startups have recently raised some notable rounds. Just a couple of weeks prior to the time of writing, Viela Bio raised $250 million in a Series A round (in late February 2018) to continue funding research and testing of its treatments for severe inflammation and autoimmune diseases. And on the later-stage end of things, education technology company Everfi raised $190 million in a Series D round that had participation from Amazon founder and CEO Jeff Bezos, former Alphabet executive Eric Schmidt and Medium CEO Ev Williams. Other D.C. companies, including Mapbox,, Afiniti and ThreatQuotient, have all raised late-stage rounds within the past 15 months.

Startup ecosystems in Southern cities may pale in comparison to places like New York and San Francisco, but it wouldn’t be wise to discount the region entirely. A large number of interesting companies call the lower half of the Lower 48 home, and as the cost of living continues to rise on the east and west coasts, don’t be surprised if many current and would-be founders opt to stay down home in the South.

The rise of experiential commerce

“$43 million and the only thing you can buy in it is a coffee.”

So said Samsung’s Senior Director of Store Development Michael Koch about the company’s flagship Manhattan “popup”—Samsung 837—though “popup” is an understated description for a 56,000 square-foot cavern with interactive art, virtual reality, lounge areas, a recording studio, and a three-story 96-screen display wall. The most shocking thing about it isn’t what’s there, but what Koch, who led the project, says about the place:

“I don’t want you to buy anything in it.”

This may seem antithetical to the purpose of a “store,” but it captures a critical understanding – experience is the core to the future of commerce.

Experiences Everywhere

So what is experiential commerce, and what does it look like?

Red Bull really did give this guy wings.

The takeover of experiential commerce is a figure with a thousand faces. It’s in the long-run transformation of stores into showrooms. It’s in Airbnb CEO Brian Chesky’s ambitions that the company’s Experiences platform will stand alongside home rentals as a core part of the business. It’s in Red Bull spending $65 million to drop an Austrian daredevil out of a space balloon and livestream it to millions of viewers on YouTube. It’s in American summer vacation spending rising by $10 billion, or 12.5%, in 2017.

You have to buy tickets to San Francisco’s Color Factory – which markets itself as 12,000 square feet of “color experiences” – months ahead of time, and escape rooms have swept the nation.

This must be the submarine that Ringo was talking about.

Explaining Experiential Commerce’s Rise

It wasn’t always like this. The status quo historically focused on functionality. Marketing and brand-building stressed a product’s uses—this brand works well to clean your clothes or iron out wrinkles, or this cream will reduce age lines if you wear it daily.

A brick-and-mortar store was product testing, warehousing, and distribution rolled into one. You walk into a Payless to try the shoes on; the customer service associate strolls into the back to get that sneaker in your size; you pay for it at the counter and walk out with it. Above all, however, the store was the place you went to buy the thing. You’re meant to go inside and walk out with something or the store and its salesfolk have not done their job properly. Analysts would judge success on metrics such as ‘sales per square foot’ in each store.


Hell hath no fury like a hand wrinkled before its time.

Now Payless is bankrupt, and Allbirds is doubling revenue to $100 million in 2018. The status quo is done. Why? Because technological and logistical advances made it possible for it to change and consumer preferences made it desirable for it to change.

The growth of e-commerce infrastructure (Stripe, AWS, Shopify, etc) and fulfilment networks has lessened the need for distribution and warehousing to take place in a store. E-commerce’s share of industrial real estate increased from 5% to 20% between 2013 and 2017; warehouse space is growing at double the rate of office space. Amazon fulfilled 2 billion orders on behalf of marketplace sellers in 2016. With delivery by drones and other autonomous vehicles still to hit the mainstream, innovation on distribution is hardly finished.

Online reviewing and free shipping/returns has lessened the need for product testing in a store—you know that the sneakers are good sneakers because 238 people reviewed them for an average rating of 4.7/5 stars; even if they turn out to be awful, you know you can send them back with zero cost and minimal inconvenience.

Consumer preferences have changed for a number of reasons. In large part this shift is a generational one, which means, yes, we have to talk about millennials (I’m an ancient borderline millennial at 33).

Millennials aren’t as materialistic as previous generations: an Eventbrite study conducted by Harris Poll in 2014 found that 78% of them would prefer to spend money on a desirable experience or event over a desirable object. Since self-report is an iffy foundation to rest that argument on—I regularly report preferring to spend money on gym visits to lavish desserts—the really eye-catching finding was that U.S. consumer expenditure on live events doubled between 1990 and 2010, when the first millennials turned 30.

It undoubtedly has something to do with social media, which has upended the conspicuous element of consumption. Why spend heaps of money on an expensive watch when you can spend that same heap on multiple photogenic meals and yoga classes that will do more for your Instagram follower and likes count? As my friend Deborah Weinswig puts it, “wellness is the new luxury.” You can only snap an item once, but a worthy lifestyle encapsulates hundreds of shareable moments.

Finally, the arrival of the sharing economy mean people who know how to navigate that space—read tech-savvy youth—don’t actually have to own as many things. When you can outsource your car with Uber and your closet with Rent the Runway, it’s possible to use more stuff while owning less stuff. These forces have combined to result in the experiential commerce boom we see today.

What Experiential Commerce Means for Business

Companies that will thrive in this environment understand that the appeal of a product or a brick-and-mortar spot has to go beyond functionality. The store has to be a place where consumers want to spend time, not just transact. This is not a new insight—Starbucks has spent years successfully charging customers 15-20x what they spend on a homemade coffee on the back of this idea. Starbucks CEO Howard Schultz once said that he wanted to make Starbucks the “third place” in people’s lives, after work and home. Hence the comfy chairs, free Wi-Fi, and effortful decor. Starbucks’ customers are fully aware of that price differential but continue to welcome this extortion because they like spending time there. And did I mention free Wi-Fi? Blue Bottle was also paying attention—add better coffee, subtract free Wi-Fi; and you have a 40-shop company Nestle is willing pay $500 million for.

The lesson is also seeping into the minds of companies that sell physical goods. Apple, which transformed retail with the Apple Store 17 years ago, now wants its locations to be more than just a place to interact with and purchase its products. At its most recent iPhone event, Apple SVP (and retail design demigod) Angela Ahrendts revealed a new retail concept called “Town Squares” that positions Apple locations as gathering places for local communities to attend concerts, workshops and more.

It’s not just giants like Samsung and Apple embracing experiences, however. Casper asks its potential customers to come take a nap in its showroom. Harry’s has set up a barbershop in Soho. b8ta functions as a gallery of tech gadgets that leans into letting you actually try them first. Glossier wants you to stroll by and check out their showroom, which an architectural correspondent described droolingly as “like something of a hybrid of a modern boudoir and a high-fashion funhouse.” One particularly quirky experience requires the customer to push a red button, upon which a gloved hand emerges through a hole and sprays Glossier You perfume on their wrist.

All Casper employees fill their bedroom walls with whimsical hand-drawn cartoons.

Unlike Starbucks, however, the goal is less direct than persuading someone to pay $5 for a cup of coffee. That’s a transaction, after all, which takes place in the same venue that the consumer spends time in. Instead, these new consumer brands want to use great brick-and-mortar experiences to court the consumer—come take a nap in my showroom, and when you need a new mattress two months down the line, you’ll choose Casper over Tuft & Needle. You probably won’t order in store, but you’ll go home and order it online…and that’s precisely the idea.

In such instances, brick and mortar becomes a kind of marketing or brand-building effort more than anything else. One way to think about it is as a very well-thought-out, multidimensional billboard.

Why Experiential Commerce Is Important

This consumer trend has consequences that go beyond Times Square and your mattress choices. Experiential commerce is speeding the decline of retail jobs and malls. It’s not hard for an optimist to find upside in less mall space in the U.S.—the country has 10x as much mall coverage per capita as Germany, and many would be happy to see that gap close if it meant more affordable housing or green space. On the other hand, while New Yorkers get to revel in Samsung 837’s digital opulence, would the company do something similar for Cleveland? If M&Ms can reach a million social-media citizens with a single smart Times Square billboard stunt, there’s no need to replicate it in Minneapolis.

If brands see brick and mortar as marketing expenses that drive affinity through foot traffic and exposure through social media, it might not make sense to set up shops in any but the most dense metropolises. That dynamic risks further driving economic vibrancy to the American coasts and urban centers.

Generally, though, experiential commerce’s moment is good news for the consumer. It has crossed over into goods commerce and imbued it with a services mentality, eliminating the pushy salesperson trying to get their commission. That change in attitude will lead to higher standards for CPG companies and more meaningful consumer-product interactions.

Given analysts’ fascination with the “retail apocalypse,” you’d think the capitalism doomsday clock had been set a few minutes from midnight. While it’s true that many retailers are dying at an accelerating rate, this trend doesn’t mark the end of retail so much as an inflection point in its nature. For retailers and brands that have spent decades perfecting the traditional brick-and-mortar experience, this shift isn’t welcomed with open arms. But embracing experiences is a surefire way to stay relevant—and in business—in today’s competitive retail environment.

VR, presence and the case of the missing killer app

Compelling virtual reality shipped to developers and consumers nearly two years ago. The first flagship headsets arrived from Oculus and HTC back in the spring of 2016, offering enough resolution, frame rate, field of view, latency mitigation and position-tracking to produce believable visual immersion.

But no one seems to know what to do with it. To date, no killer app has extended the promise of VR from a novelty to a sticky experience or utility that reaches beyond enthusiasts to resonate with the consumer center of mass.

This isn’t to say that great experiences don’t exist. Apps like Tilt Brush, Elite: Dangerous and Google Earth VR have earned rave reviews and plaudits from enthusiasts. But we have yet to see a household phenomenon like Halo or Lotus 1-2-3 — applications that single-handedly propelled their respective platforms to wide use. At CES 2018, one industry analyst referred to VR as “drawerware,” referring to the likelihood of headsets to be stuffed in a drawer after a few forays into jejune worlds.

In an attempt to shed some light on the case of the missing VR killer app, I want to offer a few thoughts on why VR matters to users, and what that implies for entrepreneurs and investors interested in building or funding the VR killer app.

Why VR matters: Presence

Why is virtual reality valuable? In a word, presence: Immersion is the heart of the incremental value of VR versus existing platforms. Most forms of expressive media provide a third-person perspective of an experience, or convey sufficient information to help a user imagine a first-person perspective on their own.

When done right (6DoF tracking, room-scale movement, sufficiently high-resolution/FOV/low latency, spatial audio), virtual reality helps a user feel like they are really there. Rather than convey an impression of an experience, VR manipulates our visual and auditory senses (and soon our tactile sense) to transmit experience itself.

Presence is valuable in two ways

The idea that VR is valuable because it generates presence is well understood. But why does presence matter? What need does being there fill for users?

The quality of presence has clear intrinsic value. With few exceptions, subjective immersion is the best way to fully grasp what a certain experience is like. Being at the mountaintop generates the maximum degree of sensory throughput, and is a better way to understand the truth of your relationship to that place than watching a video of the mountain, which is better than seeing a picture of the mountain.

The objective fact of being somewhere matters as much or more than the subjective feeling of being there.

But presence also can have instrumental value, where being there is valuable in an objective sense. Being present at a meeting with a potential business partner sends a positive signal separate from the fidelity of your experience. Actually visiting the mountaintop can impress your friends, mattering beyond the sensation of being there.

Put another way, and borrowing the language of philosophy, it seems like we value presence for its experiential worth — being for the sake of experience — as well as for its ontological worth, or being for the sake of being. Another way to describe the ontological value of presence is authenticity. The philosopher Robert Nozick suggested as much in his refutation of ethical hedonism, employing the notion of the “experience machine” to suggest we care about more than our feelings. What this all means is that for many kinds of experience, the objective fact of being somewhere matters as much or more than the subjective feeling of being there.

VR’s killer app will deliver both types of presence value

How does identifying the two ways that presence drives user value help us imagine the use case that a VR killer app might address?

First, it illuminates why many first-order VR applications may not be suited for adoption by a non-enthusiast audience. When examining some of the typical mass market use cases forwarded by VR aficionados — enterprise or personal telepresence, virtual tourism and travel, virtual attendance at sports and entertainment events, virtual social environments and rec rooms — it seems clear that authenticity matters a great deal to consumers of these experiences, meaning that simply porting them to VR may not be compelling beyond an initial sense of novelty.

I believe that the value of ontological presence is largely driven by social norms. As and when the quality of VR experience converges on metaphysically “real” experience, those norms will evolve. Perhaps our children will label us “substratist” for claiming that hanging out in VR is less satisfying than visiting in person. But with regards to the next generation or two of VR tech and applications, I’m not bullish on social VR experiences that merely replicate the ways we interact in real life. By generating experiential presence without authenticity, they seem to fall into an uncanny valley somewhere between interactive video chat and in-person interaction.

It’s tempting to believe, then, that the VR killer app will skirt the issue of authenticity by solving for problems where the subjective feeling of presence, and not the objective fact of it, matters most — for example, virtual training for a factory worker, touring new construction homes for sale or checking out a car in a virtual showroom. VR is already finding fruitful use in the enterprise and select consumer applications. But when considering potential killer applications, the problem is that arenas of experience where experiential presence matters but authenticity does not usually aren’t important or frequently accessed parts of our life.

Ultimately, I think the first VR blockbuster will deliver both the experiential and ontological value of presence. In other words, VR’s killer app will generate a powerful feeling of being there for a compelling experience, in a way that also feels completely authentic.

Quality, accessibility and ecosystem maturity are probably the biggest practical barriers gating the VR killer app.

I believe that the experience in question will lack an analogue in the real world. In other words, the VR killer app won’t be a multiplayer simulation of New York City in the present day, or a virtual movie theater, or a virtual Giants Stadium where you can kick back in a box and watch the Super Bowl. The application that sells the mass market on virtual reality will be fully native to the platform, such that the only way to know what it is really like will be donning a headset and stepping inside.

An engaging VR experience that isn’t simulating something in the real world, but exists solely in its own right, can immerse a user in both senses of the word: After all, authenticity is implied when the virtual substrate is the only home for a certain experience. The real question is making the experience interesting or fun or cool enough that the feeling of presence is appealing, too.

Concluding thoughts

If it sounds like I’m describing a video game, I think I am, too. But video games are a focal use case for every VR headset in production. What’s missing?

Quality, accessibility and ecosystem maturity are probably the biggest practical barriers gating the VR killer app. The current generation of flagship headsets are cumbersome and expensive to set up and run. Though deep price cuts across flagship wearables powered sales of more than a million VR headsets in Q3 2017, and both Oculus and HTC moved hundreds of thousands of high-end, PC-based units, individual install bases remain low enough to deter AAA studios.

Bootstrapping a two-sided ecosystem — in the case of VR, headsets/users and content, with more of the former increasing the incentive to invest in the latter and vice versa — is never easy. But better technology is on the way: HTC recently announced the Vive Pro, sporting improved resolution, spatial audio and a wireless adapter to do away with clunky wires. Google, Samsung, Lenovo and Oculus are working on standalone headsets that run without a PC or smartphone under the hood. Dozens of startups are developing peripherals and software to improve the VR experience, from haptics that mimic touch to pupil tracking that enables realistic eye contact.

Each new iteration of core VR hardware is a rising tide that makes any VR application more appealing to users on the margin. But killer apps often emerge on imperfect versions of the platforms they bring to life. The charting function of Lotus 1-2-3 strained the limits of the early graphics hardware on x86 PCs, but until 1-2-3, no one knew that programmatic generation of charts and graphs was even possible.

A killer app doesn’t need to be a perfect encapsulation of a new technology’s potential. All it needs to do is hint at the grand vision by providing a single, irresistible demonstration of value over the status quo.

In the case of VR, I’m not certain if that demonstration will occur on this generation of hardware or the next. But I believe it will be an experience that compares in intensity or joy or uniqueness to the best experiences we can access in reality. If you’re working on VR content or applications, consider this advice: Give us the ability to be present in a vision of the past, or a counterfactual world. Give us the feeling of life underwater or in space. Give us the sense of being present for an experience completely native to virtual reality, not merely an emulation of experiences we can already inhabit. Give us something real in its own right. That’s when the mass market will start to believe — and buy.

Late-blooming startups can still thrive

It seems like startup news is full of overnight success stories and sudden failures, like the scooter rental company that went from zero to a $300 million valuation in months or the blood-testing unicorn that went from billions to nearly naught.

But what about those other companies that mature more gradually? Is there such a thing as slow and successful in startup-land?

To contemplate that question, Crunchbase News set out to assemble a data set of top late-blooming startups. We looked at companies that were founded in or before 2010 that raised large amounts of capital after 2015, and we also looked at companies founded a least five years ago that raised large early-stage funds in the last year. (For more details on the rules we used to select the companies, check “Data Methods” at the end of the post.)

The exercise was a counterpoint to a data set we did a couple of weeks ago, looking at characteristics of the fastest growing startups by capital raised. For that list, we found plenty of similarities between members, including a preponderance of companies in a few hot sectors, many famous founders and a lot of cancer drug developers.

For the late bloomers, however, patterns were harder to pinpoint. The breakdown wasn’t too different from venture-backed companies overall. Slower-growing companies could come from major venture hubs as well as cities with smaller startup ecosystems. They could be in biotech, medical devices, mobile gaming or even meditation.

What we did find, however, was an interesting and inspiring collection of stories for those of us who’ve been toiling away at something for a long time, with hopes still of striking it big.

Pivots and patience

Even youthful startups have been known to make a major pivot or two. So it’s not surprising to see a lot of pivots among late bloomers that have had more time to tinker with their business models.

One that fits this mold is Headspace, provider of a popular meditation app. The company, founded in 2010 by a British-born Buddhist monk with a degree in circus arts, started as a meditation-focused events startup. But it turned out people wanted to build on their learning on their own time, so Headspace put together some online lessons. Today, Santa Monica-based Headspace has millions of users and has raised $75 million in venture funding.

For late bloomers, the pivot can mean going from a model with limited scalability to one that can attract a much wider audience. That’s the case with Headspace, which would have been limited in its events business to those who could physically show up. Its online model, with instant, global reach, turns the business into something venture investors can line up behind.

Sometimes your sector becomes hip

They say if you wait long enough, everything comes back in style. That mantra usually works as an excuse for hoarding ’80s clothes in the attic. But it also can apply to entrepreneurial companies, which may have launched years before their industry evolved into something venture investors were competing to back.

Take Vacasa, the vacation rental management provider. The company has been around since 2009, but it began raising VC just a couple of years ago amid a broad expansion of its staff and property portfolio. The Portland-based company has raised more than $140 million to date, all of it after 2016, and most in a $103 million October round led by technology growth investor Riverwood Capital.

CloudCraze, which was acquired by Salesforce earlier this week, also took a long time to take venture funding. The Chicago-based provider of business-to-business e-commerce software launched in 2009, but closed its first VC round in 2015, according to Crunchbase records. Prior to the acquisition, the company raised about $30 million, with most of that coming in just a year ago.

Meanwhile, some late bloomers have always been fashionable, just not necessarily as VC-funded companies. Untuckit, a clothing retailer that specializes in button-down shirts that look good untucked, had been building up its business since 2011, but closed its first venture round, a Series A led by VC firm Kleiner Perkins, last June.

Slow-growing venture-backed startups are still not that common

So yes, there is still capital available for those who wait. However, the truth of the matter is most companies that raise substantial sums of venture capital secure their initial seed rounds within a couple years of founding. Companies that chug along for five-plus years without a round and then scale up are comparatively rare.

That said, our data set, which looks at venture and seed funding, does not come close to capturing the full ecosystem of slow-growing startups. For one, many successful bootstrapped companies could raise venture funding but choose not to. And those who do eventually decide to take investment may look at other sources, like private equity, bank financing or even an IPO.

Additionally, the landscape is full of slow-growing startups that do make it, just not in a venture home run exit kind of way. Many stay local, thriving in the places they know best.

On the flip side, companies that wait a long time to take VC funding have also produced some really big exits.

Take Atlassian, the provider of workplace collaboration tools. Founded in 2002, the Australian company waited eight years to take its first VC financing, despite plentiful offers. It went public two years ago, and currently has a market valuation of nearly $14 billion.

The moral: Those who take it slow can still finish ahead.

Data methods

We primarily looked at companies founded in 2010 or earlier in the U.S. and Canada that raised a seed, Series A or Series B round sometime after the beginning of last year, and included some that first raised rounds in 2015 or later and went on to substantial fundraises. We also looked at companies founded in 2012 or earlier that raised a seed or Series A round after the beginning of last year and have raised $30 million or more to date. The list was culled further from there.

The Third Age of credit

Society is beginning to wake up to a tremendous shift in one of the most fundamental underpinnings to how we live our lives: the credit system. Even though it’s not commonly known, credit infrastructure has existed about as long as civilization itself. In one way or another, credit systems have always formalized the one essential basis for relationships between people: trust.

Over millennia, the way credit looks, feels and is used has changed dramatically. Today, buoyed by a plethora of technologies and a golden age for abundant data, credit is undergoing its most radical change yet. But it is being pulled in many directions by competing forces, each with their own vision for the future.

In the beginning, credit was highly personal and subjective — this persisted for thousands of years. Over the last century, a miracle happened: Driven mostly by statistical modeling, credit became for the first time “objective.” Yet today, the cracks in that system are beginning to show, and we now stand on the brink of another revolution — the “Third Age” of credit.

We are on the verge of an exponential leap. The last year has witnessed a Cambrian explosion in credit innovation, unveiling hundreds of possibilities for the future of credit. Unlike the last two ages, credit of the future will be personal, predictive, self-correcting and universal.

The First Age: credit as trust

Modern anthropologists paint a picture of early agricultural society as a community of unsophisticated barterers, trading goods and services directly. In this picture, there is no room for a credit system: I trade you what I have and you want for what you have and I want. But, as historian David Graeber points out in his excellent etymology of credit, Debt: The First 5,000 Years, this account of early civilization is a myth.

The barter system has one major fault, known as the double coincidence of wants. If I am a chicken farmer, and I want to buy shoes from a cobbler, then my only hope is to find a cobbler who wants some of my chickens. If no cobbler in my town wants chickens, then I have to find out what the cobbler wants and begin bringing third parties into the transaction until all wants are fulfilled.

Today, we have a simple solution to this problem — money. Though it’s not conventionally viewed this way, money is actually a form of credit. The radical innovation of money was to introduce one third-party into every transaction: the government. When the farmer doesn’t have anything that the cobbler wants, he pays the cobbler in dollars; the dollars provide a deferred opportunity for the cobbler to then buy what she wants. All of this is possible because people trust that the value of a dollar will remain the same, and that trust comes from the fact that the government vouches for each dollar’s value. When you accept money as payment, you are giving the government credit for their claim that the money you accept can be redeemed for (about) the same value at a later date.

For the first 10,000 years or so, credit was useful… but imperfect.

People take this feature of money for granted, but even today, it’s not ubiquitous — take the example of the three-tier pricing phenomenon in Zimbabwe: The government released bond notes pegged 1:1 to the U.S. dollar, but shops accepted actual U.S. dollars at a premium to the notes (meaning a purchase would be less expensive in U.S. dollars than bond notes). This is the literal embodiment of Zimbabwe’s citizens not giving its government any credit. (Which also led to weird discrepancies in bitcoin prices in the country.)

Money is an amazing financial instrument for so many reasons. It is a medium of exchange. It is a store of value. It is highly divisible. It is fungible across many uses. It is universally coveted. It is liquid. But early societies didn’t have anything resembling modern money, so instead, they used credit. (See a timeline of payments over the course of civilization here.)

Credit has existed as long as human economies have. Some of the earliest writings discovered by archaeologists are debt records. (Historian John Lanchester profiles the history of credit excellently in When Bitcoin Grows Up.) But credit had a lot of issues: How do you give credit to a stranger or foreigner you don’t trust? Even for those you do trust, how do you guarantee they will pay you back? What is the right amount to charge on a loan?

Early debt systems often answered by formalizing rules such as debtors going into slavery or forfeiting their daughters. These conditions artificially constrained debt, meaning that, for most of human history, economies didn’t grow much, their size being capped by a lack of credit.

So, for the first 10,000 years or so, credit was useful… but imperfect.

The Second Age: credit as algorithm

This all changed in 1956. That year, an engineer and a statistician launched a small tech company from their San Francisco apartment. That company, named Fair, Isaac and Co. after its founders, came to be known as FICO.

As Mara Hvistendahl writes, “Before FICO, credit bureaus relied in part on gossip culled from people’s landlords, neighbors, and local grocers. Applicants’ race could be counted against them, as could messiness, poor morals, and ‘effeminate gestures.’ ” Lenders would employ rules such as, “prudence in large transactions with all Jews should be used,” according to Time. “Algorithmic scoring, Fair and Isaac argued, was a more equitable, scientific alternative to this unfair reality.”

It’s hard to overstate how revolutionary FICO really was. Before multivariate credit scoring, a banker couldn’t tell two neighbors apart when pricing a mortgage. The move to statistical underwriting — a movement that had roots as early as the 1800s in the U.S. — had a snowball effect, inspiring lookalike algorithmic credit systems around the world. Credit is all about risk, but until these systems developed in the mid-century, risk-based pricing was almost entirely absent.

Famously, Capital One founder Richard Fairbank launched IBS, his “information-based strategy.” As he noted, “First, the fact that everyone had the same price for credit cards in a risk-based business was strange. […] Secondly, credit cards were a profoundly rich information business because, with the information revolution, there was a huge amount of information that could be acquired about the customers externally.”

Today, algorithmic credit is ubiquitous. Between 90 percent and 95 percent of all financial institutions in the U.S. use FICO. In the last year alone, FICO released new credit scores in Russia, China and India using novel sources of data like utility bills and mobile phone payment records. Banks around the world now implement risk-based pricing for every kind of credit.

What does a new world of credit look like?

Thousands of startups are all finding new ways to apply this same concept of statistical modeling. WeLab in Hong Kong and Kreditech in Germany, for example, use up to 20,000 points of alternative data to process loans (WeLab has provided $28 billion in credit in four years). mPesa and Branch in Kenya provide developing-world credit using mobile data, Lendable does so using psychographic data and Kora does this on blockchain. Young peer-to-peer lending startups like Funding Circle, Lending Club and Lufax have originated more than $100 billion in loans using algorithmic underwriting.

Yet this global credit infrastructure is not without its significant drawbacks, as Americans found out on September 7, 2017, when the credit bureau Equifax announced a hack that exposed the data of 146 million U.S. consumers.

The fallout from the massive breach sparked conversations on credit, forced us to re-evaluate our current credit system and finally inspired the companies to look beyond the Second Age. White House cybersecurity czar Rob Joyce opined that the time has come to get rid of Social Security numbers, so intimately tied to credit scores, which can’t be changed even after identity theft.

Today, we are held hostage by our data. We become vulnerable by being forced to rely on insecure SSNs and PINs that can be stolen. We have no choice how that information is used (more than 100 billion FICO scores have been sold.)

FICO also doesn’t take into account relevant factors such as income or bills, and in some cases only reflects poor payment history and not on-time payments. And on top of that, 50 percent of a person’s score is dependent on their credit history — inherently biasing the system against the younger borrowers who should be leveraging credit the most.

Lastly, as Frank Pasquale writes in The Black Box Society, credit scoring is opaque. This creates disparate impacts on different groups. Algorithms accidentally incorporate human biases, making loans more expensive for minorities. Building credit often requires adherence to unknown rules, such as rewarding “piggybacking” off of others’ credit — a structure that perpetuates economic inequality.

Maybe the Equifax hack was a good thing. It was a jarring reminder that a credit system reliant on historical statistical modeling, opaque algorithms and insecure identifiers is still far from perfect. Were the hackers really Robin Hood in disguise, freeing us from our hostage-like dependence on an outdated scoring system?

The time has come to move beyond the weaknesses of the modern credit regime, and technology is today taking the first step.

The Third Age: credit as liberation

What does a new world of credit look like?

In the last year there has been a Cambrian explosion of new ideas to drive modern credit forward. It is too early to tell which system(s) will win out, but the early indications are truly mind-blowing. Credit is on the precipice of an exponential leap in innovation, which will reshape the world of financial inclusion. It will become more personal, predictive instead of reactive and instantaneous.

One of the most revolutionary aspects of the future of credit is that it will increasingly come to look like cash (and cash, conversely, like credit). Consumers won’t have to request credit; rather it will be automatically allocated to them in advance based off many factors, such as behavior, age, assets and needs. It will be liquid, rather than dispersed in fixed tranches. And as it becomes increasingly commoditized, in many cases it will be close to free.

Customers will have one form of payment for all purchases that automatically decides on the back-end what the best type of funding is, cash or credit, optimizing for efficiency and low fees. Imagine Venmo, credit cards, checks, PayPal and cash, all rolled into one payment method.

People will no longer have multiple credit lines, such as separate credit cards, student loans and mortgages. People will have a guaranteed “credit plan” available to them, all linked into one master identity or profile.

Physical instruments like dollar bills and plastic cards will be phased out and live only in museums. Biometric identifiers like fingerprints will be all you need to make a purchase. Prices will become infinitesimally divisible, optimized in some cases for fractional cent values. Denominations and different currencies will become background features.

In the future, people will be paid in real time (Walmart is experimenting with this now), instead of waiting for work credit every two weeks. Payday loans as an industry will evaporate. WISH Finance is building an Ethereum-based blockchain for cash flow-based underwriting. It’s easy to see this applied to consumers: get real-time credit based on your regular pay and expenses.

Naturally, talking about the future of credit, we have to talk about blockchains.

In the next phase, credit will revolve around the individual. Right now we live in a world of gatekeepers: Centralized data aggregators, such as credit bureaus, act as intermediaries to credit. This advantage will increasingly be eroded by individually permissioned data (a concept known as self-sovereign identity). This is consistent with trends in cross-border work and globalization: In an atomized world, the individual is the core unit and will need to take her information with her, without reliance on third parties. It could reduce some $15 billion in annual fees paid to access data and make information more secure, eliminating single points of failure.

One-size fits all scores like FICO will become disaggregated. Credit is a relational system: Our credit indicates our standing relative to a wide network. But people shouldn’t be represented by averages. Credit will become more multivariate, using machine learning and breaking apart the contributing factors and weights that make up FICO (the company where I work, Petal, is doing this to democratize credit cards).

It makes little sense to set single credit benchmarks — such as the 350 to 850 score range — irrespective of age, so consumers will be compared to their cohort. Per Experian, youngest people have the lowest credit scores. However, youth is when people should be borrowing the most, both to build credit and because they should be saving cash for their spending later in life.

Credit will become contextual. Your maximum available credit will fluctuate based on ever-changing factors such as payroll and bills. It also will be specific to purchases: You will receive different levels and costs of credit based on the value and type of the asset you’re buying. For instance, credit to buy a crib for your newborn may be cheaper than credit to buy a trip to Vegas. Illiquid assets will be automatically usable to secure credit, as Sweetbridge is doing. (The founders of Kora point out that the problem is not that the poor don’t have wealth, it’s that their capital is locked up.)

Credit will be psychographic and predictive. It won’t be enough to look backwards at your past behavior — your creditworthiness will change dynamically as you move around, make purchases and stay active. It will be dynamically assigned to specific needs (like ink if you buy a printer) before you realize you have them.

Naturally, talking about the future of credit, we have to talk about blockchains. They will have three early uses:

  • Funds dispersal: It will become much cheaper to disperse credit and accept payments using services like Stellar. There will be no latency from banks having to verify transactions against their own accounts.

  • Underwriting: Data will be aggregated into universal profiles (like those being built at uPort and Bloom) from a wide variety of sources, such as credit bureaus, phone bills, academic transcripts and Facebook. As mentioned, these will be self-sovereign, and make it much easier for credit providers to underwrite borrowers.

  • Contract enforcement: Smart contracts will be self-enforcing, automatically collecting debt payments, re-adjusting themselves if someone is credit crunched in the short term and refinancing if customers can consolidate or lower their APRs. The universal ID and contract will keep people from “running to Mexico” with their credit funds.

In the future, credit (and capital) will be automatically allocated to people based off predictive AI. Better risk pricing will continue to drop rates at which consumers can borrow, toward 0 percent. The federal funds rate has been around 1 percent for the last couple of years — in 1980 it was 18 percent! A combination of machine learning and what Bain calls “A world awash in money,” with larger investors hunting for lower returns, will continue to drive these rates down.

At a higher level, blockchain protocols like Dharma will set up smart contracts for the credit economy that allocate capital in the most efficient way. Credit will not rely on active investment managers to lend or borrow: Any capital not currently tied into a contract will be programmed to continuously search for the highest risk-adjusted return — including provision of credit.

Credit providers, at scale, will experience massive network effects. “Network effects” describe the condition in which networks become more valuable to users as more users participate. This doesn’t traditionally apply to credit: Just because other people have the same credit card as you, you don’t accrue any benefits. But in the future it will: More data points within credit networks will provide better underwriting, which will create fairer pricing, creating a virtuous cycle of data. User experiences and pricing will benefit tremendously as a result. Initiatives like the U.K.’s Open Banking will accelerate this trend.

Tom Noyes calls this The Democratization of Data. In a world of smaller, local data sets that collaborate (80-90 percent of all our current behavior is local), bridging disparate data gaps will increase credit participation to 100 percent (currently, only about 71 percent of Americans have credit cards).

And these are just some of the more probable, routine ideas. Futurists like Daniel Jeffries envision currencies with built-in features to incentivize different behaviors — like saving versus spending — and universal basic income tokens, to decentralize financial inclusion. Platforms like Bloom, which now has 100 applications being built on it, are reimagining credit at the protocol level. These systems are tackling first-principles questions, such as can the future be entirely meritocratic, or can people inherently create trust with no data.

We are living in the prologue to the Third Age. It’s hard to tell exactly how the future of credit will play out, but from where we stand, we can see that it will represent the biggest departure from the past in credit’s history, and we’re just today taking the first steps.

Silicon Valley companies are undermining the impact of artificial intelligence

Leveraging machine learning and artificial intelligence to glean information from large data sets is the greatest technology opportunity of a generation. After a decade of acquiring talent from startups and research universities, tech companies like Facebook, Google and Uber have amassed some of the best AI teams in the world.

However, we are not seeing the impact we deserve beyond the tech sector. Unfortunately, progress in other industries has become collateral damage to the tech sector’s race for AI talent, and this issue has received little attention.

Over the last five years, 90 percent of AI startups in Silicon Valley have been acquired by leading tech companies. These acquisitions have been largely unrelated to a successful product: Often, companies are in nascent stages and their products are either shelved by the acquiring company altogether, or the technology is embedded as a feature in another core offering. Outside of a few highly targeted cases, it’s a strategy aimed first at getting the talent in-house, then figuring out what to do with them.

Source: CB Insights

On a micro-level, this is a highly rational strategy across the tech innovation ecosystem. Leading technology companies have the capabilities, cash and scale to leverage this talent and technical expertise into profitable products down the road. For their part, venture capitalists feel safer investing at higher prices in early-stage AI companies because a lucrative technology or team acquisition provides downside protection if they are unable to build a big business. Lastly, management teams may be tempted by early acquisition offers that are priced much higher than non AI-centric companies with equivalent product maturity or market traction.

In the AI arms race, though, the name of the game is not just getting ahead, but depriving competitors of the AI talent that could make them competitive. While tech companies compete for the promise of future AI-based offerings, they are not just depriving their competition of talent, but the rest of the economy, as well.

On a macro-level, this hoarding strategy is undercutting 95 percent of the impact AI could have on the global economy and society at large. Aggregate revenue of the five leading U.S. tech companies (Apple, Alphabet, Microsoft, Amazon, Facebook) represent less than 5 percent of total U.S. GDP. Yet tech giants are buying up companies and directing them to focus on R&D, rather than building AI applications for specific, non-tech industry problems that can have an impact today.

Some argue that tech incumbents are best suited to bring industry-specific solutions to bear. Just look at cloud computing and how many industries have used it to increase their productivity — maybe the same will be done for AI and data services. I don’t believe this is likely to happen quickly, for two reasons: (1) tech companies have plenty of their own purposes in mind, and (2) the best AI solutions are designed around a specific problem and workflow.

You can see this already playing out in a few ways:

Today, your Facebook photos are automatically tagged. This is a core feature enhancement designed to increase customer engagement. Recommendations on everything from Google to Netflix to Amazon are increasingly likely to result in increased customer purchases as a result of leveraging machine learning to scan a broader array of profile information. Both of these represent core needs for major tech companies and are not likely to translate into relevant offerings for other industries. Personally, I think it’s a shame that so many great AI minds are working on comparatively incremental feature enhancements.

There is a huge opportunity for AI-based products and companies targeting applications in industries outside the tech sector.

Second, tech companies are building up AI workforces as part of their moonshots and experimental labs that are focused on reimagining incumbent industries on tech terms and building the core IP and research that could make this possible. History indicates that when tech companies set out to reinvent entire categories, many commonly fail at first (recall Webvan, or Marc Andreesen’s LoudCloud). Incumbents don’t react quickly enough (consider Safeway’s response to Webvan, and IBM or HP’s reaction to LoudCloud).

Finally a new disruptive effort eventually succeeds a decade or two later (to complete this example, consider Amazon regarding groceries, and AWS or Opsware regarding cloud computing). In this arena, consumers and tech companies ultimately win, while major incumbents that should have had the inside track are leapfrogged because of the talent and technology gap accumulated during the initial efforts.

Even when specific projects fail, tech incumbents’ research labs reap a side benefit in recruiting power: they get AI talent in through the door and allow them to continue their research, publicizing it and adding to the narrative that tech companies are the best place to conduct research (you get free lunch and dinner!).

The net result of this situation is that, today, AI talent and technology are largely denied from companies outside of tech. Incumbent industries, like insurance, won’t see improvements to their bottom line because a computer can win at Go. This is unfortunate, because although industry applications may seem less “disruptive,” they could have a far more significant impact on a shorter timescale.

So what can other industry leaders do? Incumbent industries must respond aggressively or risk being cut out of the next decade of innovation, which will be largely driven by AI and data analytics. This means (1) acknowledging what is at stake, (2) creating an environment to attract, retain and focus the type of talent required and (3) aggressively seeking said talent.

We’ve begun to see action in a few areas:

With the prospect of self-driving cars, the automotive industry faces an existential risk.  Jon Lauckner at GM has been at the center of some bold moves forward, including the $1 billion acquisition of Cruise and a $500 million investment in Lyft. Ford and Delphi have also been active with acquisitions like Argo AI and NuTomony.

Source: CB Insights

Agriculture also presents a good example of action in recognition of what’s at stake: Two major AI acquisitions have happened in the last five years. Monsanto acquired Climate Corporation to advance their effort into a data-driven future wherein they can provide customized insights and advice to farmers for planting crops. This past year, John Deere acquired Blue River Technology, which takes this a step further, leveraging computer vision to deliver customized insight and action on every individual plant in real time as a tractor moves through the field.

To be sure, acquiring talent is far from the only means to advance as an incumbent, but building the core talent, technology and business model for future success has proven challenging for entrenched incumbents. Netflix is one of the few examples of success, innovating their way from a DVD-based business to a streaming one. Still, it was a painful transition, taking tremendous vision, cannibalization of their own sales and a 75 percent drop in share price before their fortunes turned skyward.

Right now, there is a huge opportunity for AI-based products and companies targeting applications in industries outside the tech sector, and there is relatively little competition in the short and intermediate term — moonshots at major tech companies have a spotty record and largely target a distant future. In the meantime, incumbents have historically failed to capitalize on major technology transformations, and outside of the few examples mentioned, history appears poised to repeat itself unless companies take proactive measures.