Special Report: New York’s enterprise infrastructure ecosystem

New York City is a marvel of infrastructure planning and engineering. There are the visible landmarks — the Brooklyn Bridge, the Lincoln Tunnel, the Empire State Building — and also the invisible ones that run the city beneath its crowded streets, such as one of the world’s most complex water tunneling and reservoir systems. That infrastructure was built for the economy of the 20th century, a market that emphasized the manufacturing and trading of goods.

Infrastructure though has a very different meaning in the 21st century. The digital economy means we no longer measure the movement of products simply as tonnage on freight ships and trucks, but rather as bits and bytes flowing from data centers to devices. The shipping container once revolutionized 20th century global trade, and now containerization is revolutionizing the way we think about delivering applications to end users.

While New York has more Fortune 500 companies than any other state, to date it hasn’t been a global leader in startups compared to hotspots like the Bay Area, particularly in the sorts of enterprise and data infrastructure startups that undergird the internet revolution.

That situation is rapidly changing. Today, New York City has numerous unicorns targeting the enterprise, and a large number of up-and-coming winners like Datadog that are commanding substantial market share. But what is truly exciting — and different from past prognostications about the success of enterprise in New York — is that we are now seeing the rise of a generation of hundreds of startups that are deeply technical and deeply committed to building the future of enterprise infrastructure and applications.

Today, TechCrunch presents a special report on the state of enterprise startups in New York City. My colleague Ron Miller and I interviewed dozens of people, and we boiled down their thoughts and insights into this series of articles. We purposely brought out focus away from the pure SaaS application world, and instead tried to go deeper into the infrastructure and security startups that are increasingly powering and protecting our internet services.

This article provides an overview of the changing exit environment for startups in NYC, the rise of a set of mafias which are incubating startups, and the changing culture of customers and how that is assisting NYC startups with their competition out west.

We then have a series of profile pieces on early but burgeoning startups: DNS provider NS1, time series database Timescale, bare metal cloud Packet, data privacy BigID, cloud monitoring Datadog, and a trio of security startups: cybersecurity analytics Security Scorecard, graph-based security ops Uplevel Security, and decentralized authentication HYPR. Finally, we put together a gallery of enterprise startups we think are going to be making waves in the coming years.

No need to search for the exits anymore

One of the on-going criticisms of the New York City startup ecosystem has been its lack of exits. Despite being a technology epicenter and a hub for some of the world’s largest and richest companies, the actual track record of startups in the city has never measured up. That’s a massive problem, since exits aren’t just trophies to put on the wall. Rather, they’re the generators of wealth which can be transformed into the lifeblood for the next generation of startups.

The exit environment in New York has started to look much better in recent years though, particularly in the enterprise space over the past year. Yext, which manages online reputation for brands, debuted on the NYSE last year and now sits at a $1.28 billion market cap. MongoDB went public late last year, and is just shy of a $2 billion valuation. Flatiron Health, which applies data analytics to cancer research, was acquired by Roche for $1.9 billion two months ago. Moat, an ad measurement company, was purchased by Oracle for $850 million last year.

Those are some hefty exits over just a couple of months, but the real depth of the NYC ecosystem can be witnessed in the startups right behind them that are becoming market leaders. Those companies include AppNexus, Datadog, UiPath, Dataminr, Sprinklr, InVision, Digital Ocean, Percolate, Namely, Compass, Infor, Zeta Global, Greenhouse, WeWork and the list continues. Together, these companies have raised billion of dollars in venture capital funding according to Crunchbase.

What’s different for New York than in the past is that the city is no longer relying on one company as the leading light that will prove the worth of the rest of the ecosystem. As we interviewed investors and founders about what companies they thought were going to be the most notable in the years ahead, what was illuminating was just how little overlap there existed between their answers. There is truly a cohort of strong startups coming of age in the city, and that gives the ecosystem much more vitality than it has ever seen before.

These aren’t your Godfather’s mafias

New York is increasingly a mafia town, and that’s a good thing.

One of Silicon Valley’s biggest advantages has been the constant renewal of its startup talent. People join startups, learn the ropes from experienced founders, meet other talented employees, and eventually decide to spin out on their own and build their startup dreams. Some companies have become so well known for this pattern that the networks they have formed are known as mafias. The PayPal mafia is perhaps the most famous example, but there are many other companies in the Valley that have become boot camps for the next generation of founders.

New York may be more notorious for its occasionally violent, often Italian mafias, but today the city is also home to a growing network of startup mafias who are building companies and firms and powering the ecosystem.

Take Voxel. The company, which was formed in New York City in 1999, built enterprise hosting solutions for customers around the world. It was acquired by Internap in 2012, in an all-cash transaction valued at $30 million.

That’s a pretty small exit by startup standards, but despite its small size, it has created an entire generation of NYC enterprise startup founders. Voxel CEO and founder Raj Dutt ended up starting Grafana, an open source time series analytics platform. Voxel COO Zac Smith left to start Packet, and Voxel principal software architect Kris Beevers started NS1.

Another stylized example is Gilt Groupe. Security Scorecard founders Sam Kassoumeh and Aleksandr Yampolskiy met at Gilt when they became the first two hires for the security team there. Yampolskiy had never heard of the company before, but “my wife was apparently a customer, so maybe I would get some clothes discounts.” When Sam showed up at noon in a sweatshirt on his first day, “I was like, I am going to fire this guy,” he said.

In the end, the two got along, and they eventually left to found Security Scorecard, which has raised more than $62 million in venture capital according to Crunchbase from a long list of luminary Valley-based investors.

The examples are endless. Edward Chiu, the founder of Catalyst, learned customer success at Digital Ocean, and ended up realizing that the company’s internal tooling could be externalized as a startup. Liz Maida, the founder of Uplevel Security, learned her trade at internet traffic juggernaut Akamai, and has taken several of the product lessons she learned there to heart. Timber.io founders Zach Sherman and Ben Johnson met at SeatGeek, where they realized that logging could be made significantly better. The networks each of these bought along helped in building their startups.

Of course, all of these are anecdotes, and it is next to impossible to systematically analyze these movements. Yet, these patterns of entrepreneurs and investors have become much more visible in the ecosystem. Startup talent is increasingly begetting startup talent, spinning out and circulating their knowledge.

But beyond these clusters of individuals lie the glue that is holding the ecosystem together: Jonathan Lehr and his team at Work-Bench and Ed Sim and Eliot Durbin at Boldstart. All three of them made the bet years ago that New York City would become an epicenter of the enterprise infrastructure software industry. Now they are reaping the rewards of those bets.

Work-Bench is both a workspace and a fund, but its core value is the community that’s been built around it. Lehr founded the New York Enterprise Tech Meetup, which hosts at Work-Bench a monthly gathering of hundreds of participants in the enterprise space, from founders to customers.

He has also built up a wide network of potential customers across industries to accelerate the early sales of his startups. “We are not just sending intros, we can backchannel which can save a lot of time” for founders, Lehr said. For instance, if a customer can’t deploy an application for another year because of internal politics, Lehr can figure that out and tell his founders that information, saving them time on a sale that might not come to fruition.

For Sim at Boldstart, the message is much the same. When he first launched the seed fund with Durbin in 2010, people thought that “there aren’t going to be enough deals to be done,” he said. “We thought of it as an experiment,” and the two raised only $1 million to get started. Now the fund has raised its third vehicle of $47 million, and plays a convening in engaging West Coast VCs. “On the West Coast, what [founders] really want is access to customers,” Sim explained “and on the East Coast, they want access to West Coast VCs.” Those West Coast VCs are showing up in New York these days more and more. “Every week there are five different firms sitting in our office trying to figure out what is happening in New York.”

Startup ecosystems take off when there is a sufficient density of talent, a strong desire to help one another, and an open ambition to compete. New York City has never lacked the latter, but it has been missing out on a dense network of helpful and experienced startup hands. The rise of mafias centered on some of the city’s leading companies as well as the development of community hubs for support are adding the final ingredients for a world-class ecosystem.

How changing customer tastes rebuilt NYC’s startup ecosystem

In the classic text Regional Advantage, AnnaLee Saxenian analyzed the cultural differences between innovation on the East Coast, epitomized by Boston’s Route 128, and the culture of Silicon Valley. She found that the East Coast was stodgy, hierarchical, and centralized around large corporate behemoths like DEC and EMC. In contrast, the West Coast was nimble, networked, and decentralized, with little social hierarchy.

Silicon Valley was believed to be dead in the early 1990s, outcompeted by Asian tigers like Singapore, Taiwan, and Korea in manufacturing the chips that gave the region its name. The Valley was saved in just the nick of time by the opening of the internet to commercial activity, and the culture of the West Coast would prove perfectly attuned to the frenetic pace of innovation that followed. The Valley swept the internet economy, and many of the world’s most important tech companies are now located in the Bay Area.

That Silicon Valley innovation culture is now been exported around the world, and that is no less true walking around New York City startup neighborhoods like the Flatiron and Union Square. It’s not just the obvious sartorial changes that have made the city more relaxed and creative. It’s also the changing personality of the people who are successful here — the finance major is now the computer science graduate.

New York’s startup culture isn’t just a transplant of the Valley’s however, but rather an evolution of it. The pure excitement of tech that can be found at San Francisco meetups is much more muted here. Instead, there is a greater focus on investing in product design by listening to customers earlier and much more closely.

That’s only possible though because customers actually want to talk. The success of New York City’s enterprise startups rests in large part on the changing nature of purchasing at Fortune 500 companies.

Lehr of Work-Bench should know. Prior to starting the incubator and fund, he evaluated potential technology vendors at Morgan Stanley. “The adage that you don’t get fired for buying IBM had longed passed,” Lehr explained. Companies have vexing problems, and they are increasingly willing to experiment with startup technology if it has the potential to solve those issues.

The West Coast culture of flexible decision-making has entered the corporate world. CIOs used to have a vice grip on technology purchasing, but now leaders across the enterprise increasingly make their own independent decisions. Lehr said that “you now need to know, as a startup, nuanced different people in enterprise, and as a VC, to stay relevant, you don’t just want to know the CIO or CTO, but the 30 other people who have pain points” across a company.

Sim at Boldstart noted “The last thing heads of IT want is salespeople in front of them. You are not selling anything because they don’t want to buy anything.” Instead, “they are willing to work with startups if you have the right … service partnership mentality,” he said.

With customers increasingly engaged, proximity has become a major boon for startups in NYC. “In the early days before you are ready to scale, it is all about relationships in the enterprise,” Lehr explained. He described the thinking of customers today looking at buying from startups. “I can trust these people to get me promoted, and they are in New York, and they can give me feedback.”

I heard this point made from nearly every person I talked to. Roman Chwyl, a sales executive with experience at AWS, Google, and IBM, noted that when it comes to customers, “We can probably do six meetings a day up and down a subway line.” That thinking was mirrored by George Avetisov, the CEO of HYPR, who said that “All of our customers are in a 10 mile radius” because of the company’s focus on financial institutions.

That customer-centric view is what has made Datadog, which is now north of $100 million in annual recurring revenue, so competitive. Olivier Pomel, the CEO and founder, said that “Mostly what is interesting is that we’re not overwhelmed by the 5,000 startups around us” like in the Valley, and “what we hear is more clearly the message from the customers and the market.” He noted that “For most of the people at Datadog, their significant others are not in tech,” and that means reality doesn’t get distorted in the way it can on the West Coast.

While East Coast customers seem to have become more aggressive early-adopters, that view is not held universally. Kris Beevers, the founder and CEO of NS1, said that “the reality of our business through 2014 and 2015 is that I flew to California twice a month for sales meetings, and that is where the bulk of our customers come from.” As major West Coast companies signed on though, they ended up acting as lighthouse customers for more conservative companies on the East Coast.

Intense pain points can solve that hesitation. Ajay Kulkarni, the founder and CEO of time series database Timescale, noted that the company has customers in conservative industries because the database solves a critical production challenge for those businesses, namely the real-time processing of internet of things data. He also noted that selling to the West Coast is not necessarily easier. “I think the Bay Area is great for open source adoption, but a lot of Bay Area companies, they develop their own database tech, or they use an open source project and never pay for it,” he said.

Lehr also pointed to tech for tech’s sake as one of the increasing challenges for Silicon Valley-based enterprise companies. “In Silicon Valley, too many people start with the whiz bang tech, rather than the dirty word of use cases,” he said.

Some technology purists may complain that customers don’t know what they want until they see it. That may be true, and there is something to be said for disruptive innovation like Docker’s containers, which no one wanted for years and now everyone is excited about. But ultimately, customers buy software because it solves their problems, and they know those problems intimately. Mixing the nimble culture of Silicon Valley with a customer focus has allowed New York to start competing far more aggressively in enterprise infrastructure, and create a leading set of successful companies.

The future is still waiting to be built

New York has come a long way, but it does still have challenges. Unlike venture capitalists on the West Coast, VCs in NYC often face significantly less competition for deals, and that means they can take significantly longer to make a decision. Almost all founders I talked to griped that — with a handful of exceptions — local VCs just aren’t willing to write the first check into their companies. In fact, for Sim at Boldstart, that has become a rallying cry. He bought firstcheck.vc, which redirects to Boldstart’s domain.

Another challenge that is a bit more peculiar to the geography of the city is just how many sub-ecosystems exist. There are distinct Manhattan and Brooklyn startup communities that overlap far less than some might expect. While there are exceptions, the fintech, biotech, and adtech worlds also keep much to themselves. University ecosystems around Columbia, NYU, Cornell Tech, and Princeton also similarly stay in their own space. These fractures are not apparent at first glance, but few leaders in the community have been able to blur these demarcations.

Ironically, New York also has a lack of showmanship. To put it frankly, there is no Elon Musk or SpaceX that is a paragon of ambition and aspiration that drives the rest of the ecosystem to (literally) shoot for the stars. The city’s strength in enterprise tech is a strong bedrock for a durable startup ecosystem, but it is hard to turn the success of, say, an advertising analytics platform into a beacon for others to try their own fortunes in the startup world.

That’s a loss for the city today, but also the opening for the enterprising individual who wants to make it big. Sim at Boldstart said that “I feel like Rodney Dangerfield: we get no respect, and over the next few years, we will get the respect we deserve.” Ultimately, that’s the story of New York: scrappiness and hustle, and trying to build the future one piece of infrastructure at a time.

Timescale is leading the next wave of NYC database tech

Data is the lifeblood of the modern corporation, yet acquiring, storing, processing, and analyzing it remains a remarkably challenging and expensive project. Every time data infrastructure finally catches up with the streams of information pouring in, another source and more demanding decision-making makes the existing technology obsolete.

Few cities rely on data the same way as New York City, nor has any other city so shaped the technology that underpins our data infrastructure. Back in the 1960s, banks and accounting firms helped to drive much of the original computation industry with their massive finance applications. Today, that industry has been supplanted by finance and advertising, both of which need to make microsecond decisions based on petabyte datasets and complex statistical models.

Unsurprisingly, the city’s hunger for data has led to waves of database companies finding their home in the city.

As web applications became increasingly popular in the mid-aughts, SQL databases came under increasing strain to scale, while also proving to be inflexible in terms of their data schemas for the fast-moving startups they served. That problem spawned Manhattan-based MongoDB, whose flexible “NoSQL” schemas and horizontal scaling capabilities made it the default choice for a generation of startups. The company would go on to raise $311 million according to Crunchbase, and debuted late last year on NASDAQ, trading today with a market cap of $2 billion.

At the same time that the NoSQL movement was hitting its stride, academic researchers and entrepreneurs were exploring how to evolve SQL to scale like its NoSQL competitors, while retaining the kinds of features (joining tables, transactions) that make SQL so convenient for developers.

One leading company in this next generation of database tech is New York-based Cockroach Labs, which was founded in 2015 by a trio of former Square, Viewfinder, and Google engineers. The company has gone on to raise more than $50 million according to Crunchbase from a luminary list of investors including Peter Fenton at Benchmark, Mike Volpi at Index, and Satish Dharmaraj at Redpoint, along with GV and Sequoia.

While web applications have their own peculiar data needs, the rise of the internet of things (IoT) created a whole new set of data challenges. How can streams of data from potentially millions of devices be stored in an easily analyzable manner? How could companies build real-time systems to respond to that data?

Mike Freedman and Ajay Kulkarni saw that problem increasingly manifesting itself in 2015. The two had been roommates at MIT in the late 90s, and then went on separate paths into academia and industry respectively. Freedman went to Stanford for a PhD in computer science, and nearly joined the spinout of Nicira, which sold to VMware in 2012 for $1.26 billion. Kulkarni joked that “Mike made the financially wise decision of not joining them,” and Freedman eventually went to Princeton as an assistant professor, and was awarded tenure in 2013. Kulkarni founded and worked at a variety of startups including GroupMe, as well as receiving an MBA from MIT.

The two had startup dreams, and tried building an IoT platform. As they started building it though, they realized they would need a real-time database to process the data streams coming in from devices. “There are a lot of time series databases, [so] let’s grab one off the shelf, and then we evaluated a few,” Kulkarni explained. They realized what they needed was a hybrid of SQL and NoSQL, and nothing they could find offered the feature set they required to power their platform. That challenge became the problem to be solved, and Timescale was born.

In many ways, Timescale is how you build a database in 2018. Rather than starting de novo, the team decided to build on top of Postgres, a popular open-source SQL database. “By building on top of Postgres, we became the more reliable option,” Kulkarni said of their thinking. In addition, the company opted to make the database fully open source. “In this day and age, in order to get wide adoption, you have to be an open source database company,” he said.

Since the project’s first public git commit on October 18, 2016, the company’s database has received nearly 4,500 stars on Github, and it has raised $16.1 million from Benchmark and NEA .

Far more important though are their customers, who are definitely not the typical tech startup roster and include companies from oil and gas, mining, and telecommunications. “You don’t think of them as early adopters, but they have a need, and because we built it on top of Postgres, it integrates into an ecosystem that they know,” Freedman explained. Kulkarni continued, “And the problem they have is that they have all of this time series data, and it isn’t sitting in the corner, it is integrated with their core service.”

New York has been a strong home for the two founders. Freedman continues to be a professor at Princeton, where he has built a pipeline of potential grads for the company. More widely, Kulkarni said, “Some of the most experienced people in databases are in the financial industry, and that’s here.” That’s evident in one of their investors, hedge fund Two Sigma. “Two Sigma had been the only venture firm that we talked to that already had built out their own time series database,” Kulkarni noted.

The two also benefit from paying customers. “I think the Bay Area is great for open source adoption, but a lot of Bay Area companies, they develop their own database tech, or they use an open source project and never pay for it,” Kulkarni said. Being in New York has meant closer collaboration with customers, and ultimately more revenues.

Open source plus revenues. It’s the database way, and the next wave of innovation in the NYC enterprise infrastructure ecosystem.

Full-Metal Packet is hosting the future of cloud infrastructure

Cloud computing has been a revolution for the data center. Rather than investing in expensive hardware and managing a data center directly, companies are relying on public cloud providers like AWS, Google Cloud, and Microsoft Azure to provide general-purpose and high-availability compute, storage, and networking resources in a highly flexible way.

Yet as workflows have moved to the cloud, companies are increasingly realizing that those abstracted resources can be enormously expensive compared to the hardware they used to own. Few companies want to go back to managing hardware directly themselves, but they also yearn to have the price-to-performance level they used to enjoy. Plus, they want to take advantage of a whole new ecosystem of customized and specialized hardware to process unique workflows — think Tensor Processing Units for machine learning applications.

That’s where Packet comes in. The New York City-based startup’s platform offers a highly-customizable infrastructure for running bare metal in the cloud. Rather than sharing an instance with other users, Packet’s customers “own” the hardware they select, so they can use all the resources of that hardware.

Even more interesting is that Packet will also deploy custom hardware to its data centers, which currently number eighteen around the world. So, for instance, if you want to deploy a quantum computing box redundantly in half of those centers, Packet will handle the logistics of installing those boxes, setting them up, and managing that infrastructure for you.

The company was founded in 2014 by Zac Smith, Jacob Smith, and Aaron Welch, and it has raised a total of $12 million in venture capital financing according to Crunchbase, with its last round led by Softbank. “I took the usual path, I went to Juilliard,” Zac Smith, who is CEO, said to me at his office, which overlooks the World Trade Center in downtown Manhattan. Double bass was a first love, but he found his way eventually into internet hosting, working as COO of New York-based Voxel.

At Voxel, Smith said that he grew up in hosting just as the cloud started taking off. “We saw this change in the user from essentially a sysadmin who cared about Tom’s Hardware, to a developer who had never opened a computer but who was suddenly orchestrating infrastructure,” he said.

Innovation is the lifeblood of developers, yet, public clouds were increasingly abstracting away any details of the underlying infrastructure from developers. Smith explained that “infrastructure was becoming increasingly proprietary, the land of few companies.” While he once thought about leaving the hosting world post-Voxel, he and his co-founders saw an opportunity to rethink cloud infrastructure from the metal up.

“Our customer is a millennial developer, 32 years old, and they have never opened an ATX case, and how could you possibly give them IT in the same way,” Smith asked. The idea of Packet was to bring back choice in infrastructure to these developers, while abstracting away the actual data center logistics that none of them wanted to work on. “You can choose your own opinion — we are hardware independent,” he said.

Giving developers more bare metal options is an interesting proposition, but it is Packet’s long-term vision that I think is most striking. In short, the company wants to completely change the model of hardware development worldwide.

VCs are increasingly investing in specialized chips and memory to handle unique processing loads, from machine learning to quantum computing applications. In some cases, these chips can process their workloads exponentially faster compared to general purpose chips, which at scale can save companies millions of dollars.

Packet’s mission is to encourage that ecosystem by essentially becoming a marketplace, connecting original equipment manufacturers with end-user developers. “We use the WeWork model a lot,” Smith said. What he means is that Packet allows you to rent space in its global network of data centers and handle all the logistics of installing and monitoring hardware boxes, much as WeWork allows companies to rent real estate while it handles the minutia like resetting the coffee filter.

In this vision, Packet would create more discerning and diverse buyers, allowing manufacturers to start targeting more specialized niches. Gone are the generic x86 processors from Intel driving nearly all cloud purchases, and in their place could be dozens of new hardware vendors who can build up their brands among developers and own segments of the compute and storage workload.

In this way, developers can hack their infrastructure much as an earlier generation may have tricked out their personal computer. They can now test new hardware more easily, and when they find a particular piece of hardware they like, they can get it running in the cloud in short order. Packet becomes not just the infrastructure operator — but the channel connecting buyers and sellers.

That’s Packet’s big vision. Realizing it will require that hardware manufacturers increasingly build differentiated chips. More importantly, companies will have to have unique workflows, be at a scale where optimizing those workflows is imperative, and realize that they can match those workflows to specific hardware to maximize their cost performance.

That may sound like a tall order, but Packet’s dream is to create exactly that kind of marketplace. If successful, it could transform how hardware and cloud vendors work together and ultimately, the innovation of any 32-year-old millennial developer who doesn’t like plugging a box in, but wants to plug in to innovation.

BigID lands in the right place at the right time with GDPR

Every startup needs a little skill and a little luck. BigID, a NYC-based data governance solution has been blessed with both. The company, which helps customers identify sensitive data in big data stores, launched at just about the same time that the EU announced the GDPR data privacy regulations. Today, the company is having trouble keeping up with the business.

While you can’t discount that timing element, you have to have a product that actually solves a problem and BigID appears to meet that criteria. “This how the market is changing by having and demanding more technology-based controls over how data is being used,” company CEO and co-founder Dimitri Sirota told TechCrunch.

Sirota’s company enables customers to identify the most sensitive data from among vast stores of data. In fact, he says some customers have hundreds of millions of users, but their unique advantage is having built the solution more recently. That provides a modern architecture that can scale to meet these big data requirements, while identifying the data that requires your attention in a way that legacy systems just aren’t prepared to do.

“When we first started talking about this [in 2016] people didn’t grok it. They didn’t understand why you would need a privacy-centric approach. Even after 2016 when GDPR passed, most people didn’t see this. [Today] we are seeing a secular change. The assets they collect are valuable, but also incredibly toxic,” he said. It is the responsibility of the data owner to identify and protect the personal data under their purview under the GDPR rules, and that creates a data double-edged sword because you don’t want to be fined for failing to comply.

GDPR is a set of data privacy regulations that are set to take effect in the European Union at the end of May. Companies have to comply with these rules or could face stiff fines. The thing is GDPR could be just the beginning. The company is seeing similar data privacy regulations in Canada, Australia, China and Japan. Something akin go this could also be coming to the United States after Facebook CEO, Mark Zuckerberg appeared before Congress earlier this month. At the very least we could see state-level privacy laws in the US, Sirota said.

Sirota says there are challenges getting funded as a NYC startup because there hadn’t been a strong big enterprise ecosystem in place until recently, but that’s changing. “Starting an enterprise company in New York is challenging. Ed Sim from Boldstart [A New York City early stage VC firm that invests in enterprise startups] has helped educate through investment and partnerships. More challenging, but it’s reaching a new level now,” he said.

The company launched in 2016 and has raised $16.1 million to date. It scored the bulk of that in a $14 million round at the end of January. Just this week at the RSAC Sandbox competition at the RSA Conference in San Francisco, BigID was named the Most Innovative Startup in a big recognition of the work they are doing around GDPR.

NS1 brings domain name services to the enterprise

When you think about critical infrastructure, DNS or domain naming services might not pop into your head, but what is more important than making sure your website opens quickly and efficiently for your users. NS1 is a New York City startup trying to bring software smarts and automation to the DNS space.

“We’re a DNS and [Internet] traffic management technology company. We sit in a critical path. Companies point domains at our platforms,” company CEO and co-founder Kris Beevers told TechCrunch. That means when you type in the domain name like Google.com, you go to Google and you go there fast. It’s basic internet plumbing, but it’s essential.

Beevers cut his teeth as head of engineering at Voxall, a cloud infrastructure company that was acquired by Internap in 2012 for $35 million. He and his NS1 co-founders saw an opening in the DNS space and launched the company in 2013 with a set of software-defined DNS services. The startup was able to take advantage of the New York startup ecosystem early on to drive some business, even before they went looking for funding, but one incident really helped put the company on the map and effectively double its business.

That event occurred in almost exactly two years ago in 2016. One of NS1’s primary competitors, Dyn, a New Hampshire-based DNS company was the victim of a massive DDoS attack that took down the service for hours. When critical infrastructure like your domain name server goes away, you see the consequences pretty starkly and suddenly customers realized they didn’t just need this service, they needed redundancy in case the primary service went down — and with that attack, NS1’s business effectively doubled overnight.

Suddenly everyone who owned one, needed another for redundancy. One competitor’s misfortune turned out to be highly beneficial for NS1, who turned out to be in the right place at the right time with the right solution. Dyn was actually acquired by Oracle later that year.

“DNS had been around since 1983. The first 20 years were very boring with no commercial ecosystem,” Beevers said. Even when it went commercial in the early 2000s, nobody was looking at this as a software problem. “We saw everyone in this space was a hardware or networking vendor. Nobody was a software company. Nobody had thought about automation or how automation fit into the stack. And nobody saw the big infrastructure trends,” Beevers explained.

They got their start in the adtech startup space that was booming in NYC when they launched in 2013. These companies were willing to take a chance with an unknown startup, partly because they were looking for any edge they could get, and partly because they knew Beevers from his days at Voxall so he wasn’t a completely unknown quantity.

“Our ability around dynamic traffic management and performance reliability gave those ad companies [an advantage].They were able to take a chance on us. If we have a bad day, a customer can’t operate. We had limited infrastructure. They placed a bet on us because of the [positive] impact we had on their business.”

Today the company is growing fast, has raised close to $50 million and has close to 100 employees. While the bulk of those folks are in NYC, they have also opened offices in San Francisco, Londonderry, NH, the UK and Singapore.

Beevers says the Dyn incident in many ways brought the industry closer together. While they compete, they still need to cooperate to keep the domain system up and running. “We compete and are comrades in the internet mess. We will all fall apart if we don’t work together,” he said. As it turned out, being part of the whole New York infrastructure community didn’t hurt either.

Through luck and grit, Datadog is fusing the culture of developers and operations

There used to be two cultures in the enterprise around technology. On one side were software engineers, who built out the applications needed by employees to conduct the business of their companies. On the other side were sysadmins, who were territorially protective of their hardware domain — the servers, switches, and storage boxes needed to power all of that software. Many a great comedy routine has been made at the interface of those two cultures, but they remained divergent.

That is, until the cloud changed everything. Suddenly, there was increasing overlap in the skills required for software engineering and operations, as well as a greater need for collaboration between the two sides to effectively deploy applications. Yet, while these two halves eventually became one whole, the software monitoring tools used by them were often entirely separate.

New York City-based Datadog was designed to bring these two cultures together to create a more nimble and collaborative software and operations culture. Founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, the product offers monitoring and analytics for cloud-based workflows, allowing ops team to track and analyze deployments and developers to instrument their applications. Pomel said that “the root of all of this collaboration is to make sure that everyone has the same understanding of the problem.”

The company has had dizzying success. Pomel declined to disclose precise numbers, but says the company had “north of $100 million” of recurring revenue in the past twelve months, and “we have been doubling that every year so far.” The company, headquartered in the New York Times Building in Times Square, employs more than 600 people across its various worldwide offices. The company has raised nearly $150 million of venture capital according to Crunchbase, and is perennially on banker’s short lists for strong IPO prospects.

The real story though is just how much luck and happenstance can help put wind in the sails of a company.

Pomel first met Lê-Quôc while an undergraduate in France. He was working on running the campus network, and helped to discover that Lê-Quôc had hacked the network. Lê-Quôc was eventually disconnected, and Pomel would migrate to IBM’s upstate New York offices after graduation. After IBM, he led technology at Wireless Generation, a K-12 startup, where he ran into Lê-Quôc again, who was heading up ops for the company. The two cultures of develops and ops was glaring at the startup, where “we had developers who hated operations” and there was much “finger-pointing.”

Putting aside any lingering grievances from their undergrad days, the two began to explore how they could ameliorate the cultural differences they witnessed between their respective teams. “Bringing dev and ops together is not a feature, it is core,” Pomel explained. At the same time, they noticed that companies were increasingly talking about building on Amazon Web Services, which in 2009, was still a relatively new concept. They incorporated Datadog in 2010 as a cloud-first monitoring solution, and launched general availability for the product in 2012.

Luck didn’t just bring the founders together twice, it also defined the currents of their market. Datadog was among the first cloud-native monitoring solutions, and the superlative success of cloud infrastructure in penetrating the enterprise the past few years has benefitted the company enormously. We had “exactly the right product at the right time,” Pomel said, and “a lot of it was luck.” He continued, “It’s healthy to recognize that not everything comes from your genius, because what works once doesn’t always work a second time.”

While startups have been a feature in New York for decades, enterprise infrastructure was in many ways in a dark age when the company launched, which made early fundraising difficult. “None of the West Coast investors were listening,” Pomel said, and “East Coast investors didn’t understand the infrastructure space well enough to take risks.” Even when he could get a West Coast VC to chat with him, they “thought it was a form of mental impairment to start an infrastructure startup in New York.”

Those fundraising difficulties ended up proving a boon for Datadog, because it forced the company to connect with customers much earlier and more often than it might have otherwise. Pomel said, “it forced us to spend all of our time with customers and people who were related to the problem” and ultimately, “it grounded us in the customer problem.” Pomel believes that the company’s early DNA of deeply listening to customers has allowed it to continue to outcompete its rivals on the West Coast.

More success is likely to come as companies continue to move their infrastructure onto the cloud. Datadog used to have a roughly even mix of private and public cloud business, and now the balance is moving increasingly toward the public side. Even large financial institutions, which have been reticent in transitioning their infrastructures, have now started to aggressively embrace cloud as the future of computing in the industry, according to Pomel.

Datadog intends to continue to add new modules to its core monitoring toolkit and expand its team. As the company has grown, so has the need to put in place more processes as parts of the company break. Quoting his co-founder, Pomel said the message to employees is “don’t mind the rattling sound — it is a space heater, not an airliner” and “things are going to break and change, and it is normal.”

Much as Datadog has bridged the gap between developers and ops, Pomel hopes to continue to give back to the New York startup ecosystem by bridging the gap between technical startups and venture capital. He has made a series of angel investments into local emerging enterprise and data startups, including Generable, Seva, and Windmill. Hard work and a lot of luck is propelling Datadog into the top echelon of enterprise startups, pulling New York along with it.

In the NYC enterprise startup scene, security is job one

While most people probably would not think of New York as a hotbed for enterprise startups of any kind, it is actually quite active. When you stop to consider that the world’s biggest banks and financial services companies are located there, it would certainly make sense for security startups to concentrate on such a huge potential market — and it turns out, that’s the case.

According to Crunchbase, there are dozens of security startups based in the city with everything from biometrics and messaging security to identity, security scoring and graph-based analysis tools. Some established companies like Symphony, which was originally launched in the city (although it is now on the west coast), has raised almost $300 million. It was actually formed by a consortium of the world’s biggest financial services companies back in 2014 to create a secure unified messaging platform.

There is a reason such a broad-based ecosystem is based in a single place. The companies who want to discuss these kinds of solutions aren’t based in Silicon Valley. This isn’t typically a case of startups selling to other startups. It’s startups who have been established in New York because that’s where their primary customers are most likely to be.

In this article, we are looking at a few promising early-stage security startups based in Manhattan

Hypr: Decentralizing identity

Hypr is looking at decentralizing identity with the goal of making it much more difficult to steal credentials. As company co-founder and CEO George Avetisov puts it, the idea is to get rid of that credentials honeypot sitting on the servers at most large organizations, and moving the identity processing to the device.

Hypr lets organizations remove stored credentials from the logon process. Photo: Hypr

“The goal of these companies in moving to decentralized authentication is to isolate account breaches to one person,” Avetisov explained. When you get rid of that centralized store, and move identity to the devices, you no longer have to worry about an Equifax scenario because the only thing hackers can get is the credentials on a single device — and that’s not typically worth the time and effort.

At its core, Hypr is an SDK. Developers can tap into the technology in their mobile app or website to force the authorization to the device. This could be using the fingerprint sensor on a phone or a security key like a Yubikey. Secondary authentication could include taking a picture. Over time, customers can delete the centralized storage as they shift to the Hypr method.

The company has raised $15 million and has 35 employees based in New York City.

Uplevel Security: Making connections with graph data

Uplevel’s founder Liz Maida began her career at Akamai where she learned about the value of large data sets and correlating that data to events to help customers understand what was going on behind the scenes. She took those lessons with her when she launched Uplevel Security in 2014. She had a vision of using a graph database to help analysts with differing skill sets understand the underlying connections between events.

“Let’s build a system that allows for correlation between machine intelligence and human intelligence,” she said. If the analyst agrees or disagrees, that information gets fed back into the graph, and the system learns over time the security events that most concern a given organization.

“What is exciting about [our approach] is you get a new alert and build a mini graph, then merge that into the historical data, and based on the network topology, you can start to decide if it’s malicious or not,” she said.

Photo: Uplevel

The company hopes that by providing a graphical view of the security data, it can help all levels of security analysts figure out the nature of the problem, select a proper course of action, and further build the understanding and connections for future similar events.

Maida said they took their time creating all aspects of the product, making the front end attractive, the underlying graph database and machine learning algorithms as useful as possible and allowing companies to get up and running quickly. Making it “self serve” was a priority, partly because they wanted customers digging in quickly and partly with only 10 people, they didn’t have the staff to do a lot of hand holding.

Security Scorecard: Offering a way to measure security

The founders of Security Scorecard met while working at the NYC ecommerce site, Gilt. For a time ecommerce and adtech ruled the startup scene in New York, but in recent times enterprise startups have really started to come on. Part of the reason for that is many people started at these foundational startups and when they started their own companies, they were looking to solve the kinds of enterprise problems they had encountered along the way. In the case of Security Scorecard, it was how could a CISO reasonably measure how secure a company they were buying services from was.

Photo: Security Scorecard

“Companies were doing business with third-party partners. If one of those companies gets hacked, you lose. How do you vett the security of companies you do business with” company co-founder and CEO Aleksandr Yampolskiy asked when they were forming the company.

They created a scoring system based on publicly available information, which wouldn’t require the companies being evaluated to participate. Armed with this data, they could apply a letter grade from A-F. As a former CISO at Gilt, it was certainly a paint point he felt personally. They knew some companies did undertake serious vetting, but it was usually via a questionnaire.

Security Scorecard was offering a way to capture security signals in an automated way and see at a glance just how well their vendors were doing. It doesn’t stop with the simple letter grade though, allowing you to dig into the company’s strengths and weaknesses and see how they compare to other companies in their peer groups and how they have performed over time.

It also gives customers the ability to see how they compare to peers in their own industry and use the number to brag about their security position or conversely, they could use it to ask for more budget to improve it.

The company launched in 2013 and has raised over $62 million, according to Crunchbase. Today, they have 130 employees and 400 enterprise customers.

If you’re an enterprise security startup, you need to be where the biggest companies in the world do business. That’s in New York City, and that’s precisely why these three companies, and dozens of others have chosen to call it home.

Pivotal Software closed up 5% following IPO, raised $555 million

Stock market investors showed lukewarm enthusiasm for Pivotal Software’s debut on Friday. After pricing the IPO at $15, the company closed the day at $15.73.

Although it didn’t “pop” for new investors, pricing at the midpoint of its proposed range allowed Pivotal to raise $555 million. Its public company market cap exceeded $3 billion.

The enterprise cloud computing company has been majority-owned by Dell, which came about after its merger with EMC in 2016. It was spun off from Dell, EMC and VMware in April 2013.

After that, it raised $1.7 billion in funding from Microsoft, Ford and General Electric.

Here’s how it describes its business in the S-1 filing:

Pivotal looks to “provide a leading cloud-native platform that makes software development and IT operations a strategic advantage for our customers. Our cloud-native platform, Pivotal  Cloud Foundry (‘PCF’), accelerates and streamlines software development by reducing the complexity of building, deploying and operating new cloud-native applications and modernizing legacy applications.”

According to the filing, Pivotal brought in $509.4 million in revenue for its fiscal year ending in February. This is up from $416.3 million in revenue for 2017 and $280.9 million in revenue the year before.

The company is still losing a lot of money, however. Losses for fiscal 2018 stood at $163.5 million, improved from the than the negative $232.5 million seen in 2017 and $282.5 million in 2016.

“We have incurred substantial losses and may not be able to generate sufficient revenue to achieve and sustain profitability,” the company warned in the requisite “risk factors” section of its IPO filing.

Pivotal also acknowledged that it faces competition from “legacy application infrastructure and middleware form vendors” like IBM and Oracle. The company says it additionally competes with “open-source based offerings supported by vendors” like RedHat. Pivotal also faces challenges from SAP Cloud Platform, Amazon Web Services and Microsoft Azure.

The company says it believes it will stand out from the pack because of its strong security and easy-to-use platform. Pivotal also claims to have strong brand awareness and a good reputation. It has 118 U.S. patents and 73 pending and is betting that it will remain innovative.

Morgan Stanley and Goldman Sachs served as lead underwriters. Davis Polk and Fenwick & West worked as counsel.

The company listed on the New York Stock Exchange under the ticker “PVTL.”

It has been an active spring for tech IPOs, after a slow winter. Dropbox, Spotify and Zuora are amongst the companies that have gone public in recent weeks. DocuSign, Smartsheet, Carbon Black and Pluralsight are all expected to debut within the next month.

Kubernetes and Cloud Foundry grow closer

Containers are eating the software world — and Kubernetes is the king of containers. So if you are working on any major software project, especially in the enterprise, you will run into it sooner or later. Cloud Foundry, which hosted its semi-annual developer conference in Boston this week, is an interesting example for this.

Outside of the world of enterprise developers, Cloud Foundry remains a bit of an unknown entity, despite having users in at least half of the Fortune 500 companies (though in the startup world, it has almost no traction). If you are unfamiliar with Cloud Foundry, you can think of it as somewhat similar to Heroku, but as an open-source project with a large commercial ecosystem and the ability to run it at scale on any cloud or on-premises installation. Developers write their code (following the twelve-factor methodology), define what it needs to run and Cloud Foundry handles all of the underlying infrastructure and — if necessary — scaling. Ideally, that frees up the developer from having to think about where their applications will run and lets them work more efficiently.

To enable all of this, the Cloud Foundry Foundation made a very early bet on containers, even before Docker was a thing. Since Kubernetes wasn’t around at the time, the various companies involved in Cloud Foundry came together to build their own container orchestration system, which still underpins much of the service today. As it took off, though, the pressure to bring support for Kubernetes grew inside of the Cloud Foundry ecosystem. Last year, the Foundation announced its first major move in this direction by launching its Kubernetes-based Container Runtime for managing containers, which sits next to the existing Application Runtime. With this, developers can use Cloud Foundry to run and manage their new (and existing) monolithic apps and run them in parallel with the new services they develop.

But remember how Cloud Foundry also still uses its own container service for the Application Runtime? There is really no reason to do that now that Kubernetes (and the various other projects in its ecosystem) have become the default of handling containers. It’s maybe no surprise then that there is now a Cloud Foundry project that aims to rip out the old container management systems and replace them with Kubernetes. The container management piece isn’t what differentiates Cloud Foundry, after all. Instead, it’s the developer experience — and at the end of the day, the whole point of Cloud Foundry is that developers shouldn’t have to care about the internal plumbing of the infrastructure.

There is another aspect to how the Cloud Foundry ecosystem is embracing Kubernetes, too. Since Cloud Foundry is also just software, there’s nothing stopping you from running it on top of Kubernetes, too. And with that, it’s no surprise that some of the largest Cloud Foundry vendors, including SUSE and IBM, are doing exactly that.

The SUSE Cloud Application Platform, which is a certified Cloud Foundry distribution, can run on any public cloud Kubernetes infrastructure, including the Microsoft Azure Container service. As the SUSE team told me, that means it’s not just easier to deploy, but also far less resource-intensive to run.

Similarly, IBM is now offering Cloud Foundry on top of Kubernetes for its customers, though it’s only calling this an experimental product for now. IBM’s GM of Cloud Developer Services Don Boulia stressed that IBM’s customers were mostly looking for ways to run their workloads in an isolated environment that isn’t shared with other IBM customers.

Boulia also stressed that for most customers, it’s not about Kubernetes versus Cloud Foundry. For most of his customers, using Kubernetes by itself is very much about moving their existing applications to the cloud. And for new applications, those customers are then opting to run Cloud Foundry.

That’s something the SUSE team also stressed. One pattern SUSE has seen is that potential customers come to it with the idea of setting up a container environment and then, over the course of the conversation, decide to implement Cloud Foundry as well.

Indeed, the message of this week’s event was very much that Kubernetes and Cloud Foundry are complementary technologies. That’s something Chen Goldberg, Google’s Director of Engineering for Container Engine and Kubernetes, also stressed during a panel discussion at the event.

Both the Cloud Foundry Foundation and the Cloud Native Computing Foundation (CNCF), the home of Kubernetes, are under the umbrella of the Linux Foundation. They take somewhat different approaches to their communities, with Cloud Foundry stressing enterprise users far more than the CNCF. There are probably some politics at play here, but for the most part, the two organizations seem friendly enough — and they do share a number of members. “We are part of CNCF and part of Cloud Foundry foundation,” Pivotal CEO Rob Mee told our own Ron Miller. “Those communities are increasingly sharing tech back and forth and evolving together. Not entirely independent and not competitive either. Lot of complexity and subtlety. CNCF and Cloud Foundry are part of a larger ecosystem with complimentary and converging tech.”

We’ll likely see more of this technology sharing — and maybe collaboration — between the CNCF and Cloud Foundry going forward. The CNCF is, after all, the home of a number of very interesting projects for building cloud-native applications that do have their fair share of use cases in Cloud Foundry, too.

Stripe debuts Radar anti-fraud AI tools for big businesses, says it has halted $4B in fraud to date

Cybersecurity continues to be a growing focus and problem in the digital world, and now Stripe is launching a new paid product that it hopes will help its customers better battle one of the bigger side-effects of data breaches: online payment fraud. Today, Stripe is announcing Radar for Fraud Teams, an expansion of its free AI-based Radar service that runs alongside Stripe’s core payments API to help identify and block fraudulent transactions.

And there are further efforts that Stripe is planning in coming months. Michael Manapat, Stripe’s engineering manager for Radar and machine learning, said the company is going to soon launch a private beta of a “dynamic authentication” that will bring in two-factor authentication and start to see Stripe’s first forays into considering how to implement biometric factors in payments. Fingerprints and other physical attributes have become increasingly popular ways to identify mobile and other users.

The initial iteration of Radar launched in October 2016, and since then, Manapat tells me that it has prevented $4 billion in fraud for its “hundreds of thousands” of customers.

Considering the wider scope of how much e-commerce is affected by fraud — one study estimates $57.8 billion in e-commerce fraud across eight major verticals in a one-year period between 2016 and 2017 — this is a decent dent, but there is a lot more work to be done. And Stripe’s position of knowing four out of every five payment card numbers globally (on account of the ubiquity of its payments API) gives it a strong position to be able to tackle it.

The new paid product comes alongside an update to the core, free product that Stripe is dubbing Radar 2.0, which Stripe claims will have more advanced machine learning built into it and can therefore up its fraud detection by some 25 percent over the previous version.

New features for the whole product (free and paid) will include being able to detect when a proxy VPN is being used (which fraudsters might use to appear like they are in one country when they are actually in another) and ingesting billions of data points to train its model, which is now being updated on a daily basis automatically — itself an improvement on the slower and more manual system that Manapat said Stripe has been using for the past couple of years.

Meanwhile, the paid product is an interesting development.

At the time of the original launch, Stripe co-founder John Collison hinted that the company would be considering a paid product down the line. Stripe has said multiple times that it’s in no rush to go public — and statement that a spokesperson reiterated this week — but it’s notable that a paid tier is a sign of how Stripe is slowly building up more monetization and revenue generation.

Stripe is valued at around $9.2 billion as of its last big round in 2016. Most recently, it raised $150 million back in that November 2016 round. A $44 million from March of this year, noted in Pitchbook, was actually related to issuing stock related to its quiet acquisition of point-of-sale payments startup Index in that month — incidentally another interesting move for Stripe to expand its position and placement in the payments ecosystem. Stripe has raised around $450 million in total.

The Teams product, aimed at businesses that are big enough to have dedicated fraud detection staff, will be priced at an additional $0.02 per transaction, on top of Stripe’s basic transaction fees of a 2.9 percent commission plus 30 cents per successful card charge in the U.S. (fees vary in other markets).

The chief advantage of taking the paid product will be that teams will be able to customise how Radar works with their own transactions.

This will include a more complete set of data for teams that review transactions, and a more granular set of tools to determine where and when sales are reviewed, for example based on usage patterns or the size of the transaction. There are already a set of flags the work to note when a card is used in frequent succession across disparate geographies; but Manapat said that newer details such as analysing the speed at which payment details are entered and purchases are made will now also factor into how it flags transactions for review.

Similarly, teams will be able to determine the value at which a transaction needs to be flagged. This is the online equivalent of when certain purchases require or waive you to enter a PIN or provide a signature to seal the deal. (And it’s interesting to see that some e-commerce operations are potentially allowing some dodgy sales to happen simply to keep up the user experience for the majority of legitimate transactions.)

Users of the paid product will also be able to now use Radar to help with their overall management of how it handles fraud. This will include being able to keep lists of attributes, names and numbers that are scrutinised, and to check against them with analytics also created by Stripe to help identify trending issues, and to plan anti-fraud activities going forward.

Updated with further detail about Stripe’s funding.

Cloud Foundry Foundation looks east as Alibaba joins as a gold member

Cloud Foundry is among the most successful open source project in the enterprise right now. It’s a cloud-agnostic platform-as-a-service offering that helps businesses develop and run their software more efficiently. In many enterprises, it’s now the standard platform for writing new applications. Indeed, half of the Fortune 500 companies now use it in one form or another.

With the imminent IPO of Pivotal, which helped birth the project and still sits at the core of its ecosystem, Cloud Foundry is about to gets its first major moment in the spotlight outside of its core audience. Over the course of the last few years, though, the project and the foundation that manages it have also received the sponsorship of  companies like Cisco, IBM, SAP, SUSE, Google, Microsoft, Ford, Volkswagen and Huawei.

Today, China’s Alibaba Group is joining the Cloud Foundry Foundation as a gold member. Compared to AWS, Azure and Google Cloud, the Alibaba Cloud gets relatively little press, but it’s among the largest clouds in the world. Starting today, Cloud Foundry is also available on the Alibaba Cloud, with support for both the Cloud Foundry application and container runtimes.

Cloud Foundry CTO Chip Childers told me that he expects Alibaba to become an active participant in the open source community. He also noted that Cloud Foundry is seeing quite a bit of growth in China — a sentiment that I’ve seen echoed by other large open source projects, including the likes of OpenStack.

Open source is being heavily adopted in China and many companies are now trying to figure out how to best contribute to these kind of projects. Joining a foundation is an obvious first step. Childers also noted that many traditional enterprises in China are now starting down the path of digital transformation, which is driving the adoption of both open source tools and cloud in general.

Squarefoot raises $7M to give offices an easier way to find space

While smaller companies are seeing a lot of new options for distributed office space, or can pick up a couple offices in a WeWork, eventually they get big enough and have to find a bigger office — but that can end up as one of the weirdest and most annoying challenges for an early-stage CEO.

Finding that space is a whole other story, outside of just searching on Google and crossing your fingers. It’s why Jonathan Wasserstrum started Squarefoot, which looks to not only create a hub for these vacant offices, but also have the systems in place — including brokers — to help companies eventually land that office space. Eventually companies as they grow have to graduate into increasingly larger and larger spots, but there’s a missing sweet spot for mid-stage companies that are looking for space but don’t necessarily have the relationships with those big office brokers just yet, and instead are just looking through a friend of a friend. The company said today that it has raised $7 million in a new financing round led by Rosecliff Ventures, with RRE Ventures, Triangle Peak Partners, Armory Square Ventures, and others participating.

“If you talk to any CEO and you ask what they think about commercial real estate brokers, they’ll say, ‘oh, the guys that send an email every week,’” co-founder Jonathan Wasserstrum said. “The industry has been slow to adopt because the average person who owns the building is fine. They don’t wake up every morning and say this process sucks. But the people who wake up and say the process sucks are looking for space. That was kind of one fo the early things that we kind of figured out and focused a lot of attention on aggregating that tenant demand.

Squarefoot starts off on the buyer side as an aggregation platform that localizes open office space into one spot. While companies used to have to Google search something along the lines of “Chelsea office space” in New York — especially for early-stage companies that are just starting to outgrow their early offices — the goal is to always have Squarefoot come up as a result for that. It already happens thanks to a lot of efforts on the marketing front, but eventually with enough inventory and demand the hope is that building owners will be coming to Squarefoot in the first place. (That you see an ad for Squarefoot as a result for a lot of these searches already is, for example, no accident.)

Squarefoot is also another company that is adopting a sort of hybrid model that includes both a set of tools and algorithms to aggregate together all that space into one spot, but keep consultants and brokers in the mix in order to actually close those deals. It’s a stance that the venture community seems to be increasingly softening on as more and more companies launch with the idea that the biggest deals need to have an actual human on the other end in order to manage that relationship.

“We’re not trying to remove brokers, we have them on staff, we think there’s a much better way to go through the process,” Wasserstrum said. “When I am buying a ticket to Chicago, I’m fine going to Kayak and I don’t need a travel agent. But when I’m the CEO of a company and about to sign a three-year lease that’s a $1.5 million liability, and I’ve never done this before, shouldn’t I want someone to help me out? I do not see in the near future this e-commerce experience for commercial real estate. You don’t put it in your shopping cart.”

And, to be sure, there are a lot of platforms that already focus on the consumer side, like Redfin for home search. But this is a big market, and there already is some activity — it just hasn’t picked up a ton of traction just yet because it is a slog to get everything all in one place. One of the original examples is 42Floors, but even then that company early on faced a lot of troubles trying to get the model working and in 2015 cut its brokerage team. That’s not a group of people Wasserstrum is looking to leave behind, simply because the end goal is to actually get these companies signing leases and not just serving as a search engine.

Cloud.gov makes Cloud Foundry easier to adopt for government agencies

At the Cloud Foundry Summit in Boston, the team behind the U.S. government’s cloud.gov application platform announced that it is now a certified Cloud Foundry platform that is guaranteed to be compatible with other certified providers like Huawei, IBM, Pivotal, SAP and — also starting today — Suse. With this, cloud.gov becomes the first government agency to become Cloud Foundry certified.

The point behind the certification is to ensure that all of the various platforms that support Cloud Foundry are compatible with each other. In the government context, this means that agencies can easily move their workloads between clouds (assuming they have all the necessary government certifications in place). But what’s maybe even more important is that it also ensures skills portability, which should make hiring and finding contractors easier for these agencies. Given that the open source Cloud Foundry project has seen quite a bit of adoption in the private sector, with half of the Fortune 500 companies using it, that’s often an important factor for deciding which platform to built on.

From the outset, cloud.gov, which was launched by the General Services Administration’s 18F office to improve the U.S. government’s public-facing websites and applications, was built on top of Cloud Foundry. Similar agencies in Australia and the U.K. have made the same decision to standardize on the Cloud Foundry platform. Cloud Foundry launched its certification program a few years ago and last year, it also added another program for certifying the skills of individual developers.

To be able to run government workloads, a cloud platform has to offer a certain set of security requirements. As Cloud Foundry Foundation CTO Chip Childers told me, the work 18F did to get the FedRAMP authorization for cloud.gov helped bring better controls to the upstream project, too, and he stressed that all of the governments that have adopted the platform have contributed to the overall project.

Wonolo picks up $13M to create a way to connect temp workers with companies

AJ Brustein was out spending time with a member of his merchandising team when a nearby store ran out of stock of some goods — but there was no one on staff responsible for that location. Fortunately, the employee he was with had already showed him how to restock the shelves, and he offered to peel off and do it himself.

But that gap in the workforce may have just continued, leading directly to potential lost revenue for companies that sell products in those stores. That’s why Brustein and Yong Kim started Wonolo, a tool to connect companies with temporary workers in order to fill the unexpected demand those companies might face in those same out-of-stock situations. Wonolo employees sign up for the platform, and the companies that partner with the startup have an opportunity to grab the necessary workers they need on a more flexible basis. Wonolo today said it has raised $13 million in a new financing round led by Sequoia Capital, including existing investors PivotNorth and Crunchfund, and new investor Base10. Sequoia Capital’s Jess Lee is joining the company’s board of directors as part of the financing.

“There’s a big opportunity  helping people fill in their schedule with shifts,” Brustein said. “We really found there’s this huge untapped market of people who are looking for work who are underemployed. Let’s say Mary is a great worker and has a great job at the Home Depot, but no matter how good she, is she can only get 29 hours of work. It’s hard to manage schedules between different employers that want you to work the same hours. That’s the market we’ve really focused on, the underemployed market, which is a growing unfortunate trend in the U.S. That’s changed a little bit about the types of jobs we have on the platform.”

Wonolo is essentially looking to replace the typical temp agency experience, which helps workers find positions with companies that need a more limited amount of time. Meanwhile, those workers get an opportunity to fill in extra shifts that they might need for additional income on a more flexible schedule. Once a company posts a job to Wonolo, employees will get notified that it’s available and then get a chance to pick up those shifts, and when the job is approved those workers get paid right away.

While the jobs that Wonolo is suited for are more along the lines of merchandising, events staff, or more general labor, the hope is that the service will also expose those employees to a variety of companies who may actually end up wanting to hire them at some point. It allows them to get a good snapshot of all the work that’s available, and theoretically would help offer them an additional step on a career path that could get them to a direct full-time job with any of the companies from which they might end up accepting jobs.

“We thought we could address [the idea of being able to deal with unpredictability] better than temp staffing, and we realized the antidote was flexibility on the worker side,” Brustein said. “We could match them with these jobs that would unpredictably pop up. When we dug into it, we realized flexibility was something that was just completely lacking for workers. We took a very different approach to the way that people will often recruit talent for staffing agencies or their own employees. We are looking at character traits.”

Wonolo was born out of Brustein and Kim’s experience at Coca-Cola, where they had an opportunity to work with a major brand for a number of years. After a while, they got an opportunity to start working on a more entrepreneurial project, and that’s when that whole merchandising scenario played out and prompted them to start working on Wonolo. That part about character traits is an important part for Wonolo, Brustein said — because as long as someone can complete a job, they don’t have to be an absolute expert, as long as they are there ready and good to go.

There are, of course, companies trying to create platforms for temporary workers, like TrueBlue, and Brustein said Wonolo will inevitably have to compete with more local players as it looks to expand. But the hope is that aiming to tap the same kind of flexibility that made Uber so popular for temporary staffers — and potentially that pathway to a big career opportunity — will be one that attracts them to their service.

Enterprise AI will make the leap — who will reap the benefits?

This year, artificial intelligence will further elevate the enterprise by transforming the way we work, securing digital assets, increasing collaboration and ushering in a new era of AI-powered innovation. Enterprise AI is rapidly moving beyond hype and into reality, and is primed to become one of the most consequential technological segments. Although startups have already realized AI’s power in redefining industries, enterprise executives are still in the process of understanding how it will transform their business and reshape their teams across all departments.

Throughout the past year, early adopting businesses of all sizes and industries began to reap benefits. AI applications with AI-powered capabilities introduced opportunities to change the way the enterprise engaged customers, segmented markets, assessed sales leads and engaged influencers. Enterprises are on the edge of taking this a step further because of the amount of knowledge and tools leveraging the potential of AI within their entire organization.

“New breakthroughs in AI, enabled by new hardware architectures, will create new intelligent business models for enterprises,” says Nigel Toon, co-founder and CEO at U.K.-based Graphcore. “Companies that can build an initial knowledge model and launch an initial intelligent service or product, then use this first product to capture new data and improve the knowledge model on a continuing basis, will quickly create clear class-leading products and services that competitors will struggle to keep up with.”

The category is evolving, and large companies are finding distinct ways to innovate. They can uniquely tap into decades of industry experience to develop horizontal AI, built for specific industries like healthcare, financial services, automotive, retail and more. These implementations, though, require deep industry expertise and industry-specific design, training, monitoring, security and implementation to meet the high-stakes IT requirements of global organizations.

“In 2018, AI is entering the enterprise. I believe we will see many enterprises adopt AI technology, but the (few) leaders will be those that can align AI with their strategic business goals,” says Ronny Fehling, associate director of Gamma Artificial Intelligence at BCG.

2018: AI will start separating the winners from the losers

Early industry successes (and failures) proved AI’s inevitability, but also the reality that wide-scale adoption would come through incremental progress only. This year, we’ll see AI move from influencing product or business functions to an organization-wide AI strategy. Expect the winners to move fast and remain nimble to keep implementing off-the-shelf and proprietary AI.

The companies that win the AI talent war will gain exponential advantages, given the category’s rapid growth.

Hans-Christian Boos, CEO and founder of Germany-based Arago, adds: “2018 will be a make or break year for enterprise and the established economy in general. I believe AI is the only viable path for innovation, new business models and digital disruption in companies from the industrial era. General AI can enable these enterprises to finally make use of the only advantage they have in the battle against new business models and giants from the Silicon Valley, or rather giants from the new age of knowledge based business models.”

The AI talent challenge

A boon in enterprise AI will also mean a further shortage of talent. Industries like telecommunications, financial services and manufacturing will feel the talent squeeze the most. The companies that win the AI talent war will gain exponential advantages, given the category’s rapid growth.

Hence, enterprises will try to attract talent by offering a powerful vision, a track record of product success, a bench of early client implementations and the potential to impact the masses. It’s about developing high-functioning and reliable solutions that become a new foundation for clients.

Developers and data scientists, however, are only the beginning. Winning enterprises must adopt their organizational structures that attract a new generation of product managers, sales, marketing, communications and other delivery teams that understand AI. This requires an informed, passionate and forward-thinking group of professionals that will help customers understand the future of work and customer engagement powered by AI.

AI adoption and employee training

Digital transformation, powered in large part by new AI capabilities, requires enterprises to understand how to extract data and utilize data-driven intelligence. Data is one of the greatest assets and essentials in maximizing the value in an AI application, yet data is often underutilized and misunderstood. Executives must establish teams and hold individuals across departments accountable for the successful and ongoing implementation of digital tools that extract full value from available internal and external data.

This transformation into an AI-native organization requires it to hire, train and re-skill all levels of employees, and provide the resources for individuals to adopt AI-powered disciplines that enhance their performance. Most workforce, from top to bottom, should be encouraged to rethink and evolve their role by incorporating new digital tools, often enabled by AI itself.

Expect AI and other digital technologies to become more prevalent in all business disciplines, not only at the application layer, as Vishal Chatrath, co-founder and CEO of U.K.-based Prowler.io emphasises. “Decision-making in enterprise is dominated by expert-systems that are born obsolete. The AI tools available till now that rely on deep-neural nets which are great for classification problems (identifying cats, dogs, words etc.) are not really fit for purpose for decision-making in large, complex and dynamic environments, because they are very data inefficient (needs millions of data points) and effectively act like black-boxes. 2018 will see Enterprise AI move beyond classification to decision-making.”

What’s next

However, the spotlight will shine on data governance as businesses adjust entire departments and workflows around data. In turn, data management and integrity will be an essential component of success as consumers and enterprises gain greater awareness about how companies use customers’ data. This opens a large field of opportunities, but also will require transparency in how companies are using, sharing and building applications on top of customer data to ensure trust.

“Every single industry will be enhanced with AI in the coming years. In the last years there was a lot of foundation work on gathering standardized data and now we can start to use some of the advanced AI techniques to bring huge efficiency and quality gains to enterprise companies,” says Rasmus Rothe, co-founder and CTO of Germany-based research lab and venture builder Merantix. “Enterprises should therefore thoroughly analyze their business units to understand how AI can help them to improve. Partnering with external AI experts instead of trying to build everything yourself is often more capital efficient and also leads to better results.”

The shift toward AI-native enterprises is in a defining phase. The pie of the AI-enabled market will continue to grow and everyone has an opportunity to take a slice. Enterprises need to quickly leverage their assets and extract the value of their data as AI algorithms themselves will become the most valuable part when data has become a commodity. The question is, who will move first, and who will have the biggest appetite.