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Anyfin bags €4.8M Series A to let you refinance your existing loans by taking a photo

11:00 | 21 February

Anyfin, a startup based in Sweden that easily enables you to refinance your existing loans, including by taking a photo, has picked up €4.8 million in Series A investment. The round is co-led by Accel, and Northzone, with participation from Rocket Internet’s Global Founders Capital, and a number of unnamed angel investors from the consumer finance and fintech space.

Launched in November 2017, and currently only available in its home country despite harbouring wider European ambitions, Anyfin wants to make it easier to competitively refinance (or consolidate) loans and credit cards and therefore not get ripped off with high interest rates or compound interest.

It claims to do this with a combination of AI and publicly available consumer data, and with additional information garnered through talking a photo of your existing loan statement, including your repayment history. This, it says, gives Anyfin a more complete picture than your credit score alone, which is likely the main data point used by the original lender.

“Working in the consumer finance sector for so long we all realised that although consumer finance brings a lot of value to people, it also has this huge downside to it,” says Anyfin co-founder and CEO Mikael Hussain, who spent seven years working at Klarna where he headed up credit risk and decision science.

“Consumers are getting ripped off and paying way too much for their financing, whether that’s credit cards, loans or instalment financing – with no good alternatives. Often consumers are charged in excess of 25 percent annually on part payments and credit cards, even those with good credit scores. In many cases this is due to old and rigid processes that don’t consider the individual consumer’s situation”.

To remedy this, Hussain says Anyfin wants to make refinance “as easy as taking a selfie”. Where there was previously paperwork, the fintech startup has digitised the process, and where there were long forms to fill out, it claims to use AI to capture much of the data it needs.

“All the consumer has to do to save a bunch of money is to snap a picture of the credit card bill or loan statement and we do the rest. When a customer sends us their picture we use OCR to get the data we need, run that through our risk algorithms and, based on that, give the consumer an individual price. Typically, we’re able to cut the cost of financing by more than half,” he says.

Meanwhile, competitors are cited as credit card providers, point-of-sale financing companies, and traditional and neo banks (although I think fintech startups like the U.K.’s Pariti are worth mentioning, too). “In reality consumer credit is everywhere,” says the Anyfin co-founder. “You can barely go into a store where they don’t offer you a financing option, and practically everyone has at least one credit card”.

To that end, the Swedish fintech startup claims to be able to keep costs and rates low by automating its processes, including credit scoring, and cutting out middle companies. The latter sees it operate as a balance sheet lender, meaning that it borrows money from banking partners to finance the loans it sells at a higher interest rate.



StatusToday scores nearly $4M to grow its AI-powered ’employee insights’ service

10:00 | 21 February

StatusToday, a London startup that is building out AI tech that it claims can help companies better understand their employees and in turn improve productivity, is disclosing $3.91 million in seed investment. The round is led by LocalGlobe, with participation from Notion Capital and Firstminute capital.

Founded in 2015 after graduating from company builder Entrepreneur First, StatusToday originally set out to use AI for cyber security, specifically by analysing a company’s internal online comms and other network activity to spot rogue employees or human lapses in security. However, the nascent company has since broadened out its offering, which launched in beta in the middle of last year, to be a more comprehensive employee insights service powered by AI.

Based on the premise that “most companies and managers do not understand their employees,” StatusToday currently plugs into various online company tools, such as those from Microsoft or Google. It then uses meta-data pulled in from these systems and artificial intelligence to analyse employee actions, thus enabling companies to gain better visibility of how their workforce is operating and to make improvements accordingly.

“Our mission is to help employers and employees understand each other. Most managers do not understand their employees,” StatusToday co-founder and CEO Ankur Modi tells me. “They don’t know what makes them inefficient, when they make mistakes or what incentivizes them. This causes a severe loss of productivity, mismanagement and an increase in risks, such as misconduct, regulatory failures and so on. We want to solve that problem by helping companies better understand their employees using artificial intelligence and data. By analyzing employee activity, we are able to determine when employees are more productive, identify looming risks and help companies to plan more effectively”.

In addition, Modi claims that StatusToday adds transparency for employees, too, by redefining the meaning of work in a way that is “transparent, rather than hierarchical and subjective”. “In time we should be able to use data to shed light on issues such as workplace discrimination and bullying,” he says.

When you plug Microsoft 365 or Google Gsuite into StatusToday, the service creates connectors to map out comms, content and activity on a real-time basis for all future events. This allows StatusToday to collect metadata and audit information for all essential activity performed by any employee in the system.

“Due to the nature and source of this data, we are able to create a company graph that consolidates baseline activity, times, locations for groups without getting actual contents or confidential info,” explains Modi.

Behind the scenes, StatusToday is currently running 22 AI modules (multiple patent-pending) that crunch this activity and create behavior models that adapt in real time to the individual, company and any relevant groups (e.g. roles, departments, managers or the London office). “This allows us to measure normal and abnormal hours, influence, mistakes and areas of low visibility,” says the StatusToday CEO.

Asked how this might be useful in practice, Modi cites a recent example of how one manager using the platform discovered the real volume of weekend work going on in his company, which far exceeded his pre-conceived expectations of work-life balance. Other recent reports generated by StatusToday include “out of band use of cloud storage”, impersonation of senior management, benchmarking contractor performance, and dedicated views for newcomers and leavers.

Meanwhile, StatusToday says the new investment will be used to attract more companies to the service, further improve the AI and expand the multinational team. The company currently has 14 employees, based in London and the Ukraine.

Customers are typically in regulated industries, sectors with high value employees or high employee turnover, “or are simply companies that want to use cutting edge technology to be more agile”. The basic service is free and open to companies of any size. However, StatusToday expects to charge for additional services in future.

Adds Modi: “The true value comes with scale because we will be able to offer valuable industry insights as a premium feature to employees and employers. Down the line we will have services that help companies take action based on our insights which will also be charged for. Needless to say, this gives us the ability to create industry-level benchmarks. We will be able to replicate what market analysts and research firms have done for decades but with real-time data and analytics. That kind of insight can change whole industries and those will generate revenue in future”.



Adecco Group acquires recruiting startup Vettery for $100M

21:01 | 20 February

The Adecco Group, a global HR services firm headquartered in Switzerland, announced today that it has acquired Vettery.

The financial terms were not disclosed, but a source with knowledge of the deal told us that the price was a little over $100 million. (It’s not clear how much of that is cash versus stock.)

We’ve reached out to the Adecco Group for confirmation and will update if we hear back. A Vettery spokesperson declined to comment.

Vettery was launched in 2014. Shortly after that, co-founders Brett Adcock and Adam Goldstein told me that they’re hoping to reinvent the traditional recruiting process. They’ve created a marketplace where job candidates browse offers, schedule interviews with the employers that interest them and receive a signing bonus from Vettery when they take a job — all assisted by an on-staff “talent executive.”

The company says it now works with more than 4,000 employers to fill positions in IT, sales and finance. It’s raised a total of $11.9 million from investors including Greycroft and Raine Ventures.

According to Adecco, Adcock and Goldstein will continue to lead the Vettery team.

“The acquisition of Vettery accelerates the development of the Adecco Group’s digital strategy, broadening our offering into the fast-growing digital permanent recruitment market and complementing our professional recruitment businesses,” said Adecco Group CEO Alain Dehaze in the acquisition release. “Digital innovations have the potential to transform the recruitment industry and the Adecco Group is taking the lead.”

Recent Adecco acquisitions include life sciences staffing company BioBridges and career transition firm Mullin.



Tenor hits 12B searches in its GIF keyboard every month

20:00 | 20 February

David McIntosh’s startup Tenor builds a GIF keyboard — but he actually hopes you’ll spend as little time searching on it as possible.

Instead, Tenor’s aim has been to collapse the amount of time it takes for you to find a GIF you like and send it to a friend. Instead of trying to get people to come to the service and kind browse around on the keyboard or a different website, Tenor’s goal has been to figure out what you are trying to say in some kind of a GIF and get it out the door as quickly as possible. And with that approach, Tenor says its users now search for GIFs on its keyboard more than 400 million times a day and 12 billion times a month.

“It comes down to search, fundamentally we’re a search product, unlike Facebook and Instagram and Twitter and Snap,” Tenor CEO David McIntosh said. “They succeed by grabbing more minutes, our success is getting you the right thing faster. Can we take that 25 second session time and make it 20 seconds, or even 15 or 10. There’s a viral loop in place where every time you make search a little better it’s faster.”

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This more or less dovetails with an approach for some companies that are focusing on pitching engagement instead of a raw active user metric. Snap, for example, has stressed to investors that it is getting people to come back to the service more and more and spend more time on it. It’s roughly the same principle in terms of using Tenor, which McIntosh says is more of a search engine than an actual hub or portal. Basically, you want to communicate what you want to tell a friend in as little words as possible — except with something silly from Friends. Tenor works across a number of platforms, but now its sights have shifted abroad.

That might even be more true as Tenor begins to expand internationally, planting people on the ground to figure out what localized versions of the service look like. One of the appeals of GIFs is that it can compress a ton of information (McIntosh refers to it as “emotion”) into a short semi-video object in a messenger screen rather than having to type out a bunch of text. As it expands to more and more countries, Tenor is able to start picking off that low hanging fruit, as making small tweaks in certain regions can lead to dramatic improvements in engagement and usage, McIntosh said.

“Western content is so heavily exported all over the world that these things have almost become globally recognized object,” McIntosh said. “Often western content with a local caption will perform better. Sometimes the local content performs better. You gotta have the right set of search data, share data, community uploads, it’s the combination of all of them. It’s kind of like chicken and egg problem, it’s a slow grind until a spark happen — you’re guessing what’s gonna work. Once the flywheel is spinning really quickly you have so much data.”

It’s also begun running its first partner campaigns internationally as it’s started to expand, with the idea that it can go to potential advertisers and tell them that because people use the keyboard so much they’ll actually share that content. That includes campaigns with companies in even India and Germany. The whole goal is to, again, figure out how to get the right GIF in front of the right person in those couple of slots when they open the app and actually want to share it.

There is, of course, a data component to that problem as well. But with 12 billion searches every month, Tenor can start slightly tweaking each search to figure out what a person is looking for based on a wider array of parameters — and maybe figure out how to get that Tom Brady strip-sack in the expiring minutes of the Super Bowl this year in front of people more quickly. Two months ago, Tenor says it had 10 billion monthly searches monthly (around 330 million daily).

It might sound a little ridiculous now, but in retrospect there’s been a blossoming ecosystem around both creator tools for GIFs as well as ones for sharing them in messenger products or the web. Gfycat, which targets creators with more robust tools, says it has 130 million monthly active users, while Giphy says it has 300 million daily active users. Either way, it means that there is both a lot of competition and a lot of interest in this space — including venture financing.



Edtech company Kidaptive raises $19.1 million for its adaptive learning platform

19:19 | 20 February

Edtech startup Kidaptive, an adaptive-learning company that begin its life with a suite of curriculum-focused iPad games for kids, announced today it has closed on $19.1 million in Series C funding, in a round led by Formation 8 and Korean education company Woongjin ThinkBig. The investment follows a deal with Woongjin that will see Kidaptive powering an English language learning system Woongjin Compass wants to build; as well as deal with its parent company, a large publisher with half a million paying subscribers, to personalize their tablet experience.

The deal is one of several in the works for Kidaptive, which now styles itself as more of a “big data for learning” company, rather than maker of educational kids’ games it was known for just a few years ago. Its early apps, which involved interactive storytelling, high-quality animation, and puzzles, had helped to create educational profiles for the young players while helping young children with reading comprehension and math skills, as well as improved cognitive, emotional and social functions.

The technology powering this experience has since evolved into Kidaptive’s “Adaptive Learning Platform,” a cloud-based assessment and reporting platform that can create learner profiles with actionable insights for parents and teachers. Another important aspect to Kidaptive’s platform is that it adapts in real-time based on how well the learner is performing in order to personalize the learning experience further.

The platform can also incorporate educational activity that takes place offline to enhance those learner profiles. This is especially important at younger ages, where parental involvement – like follow-up conversations to trip to museums – could help reinforce what the child learned. In other contexts, like language learning, for example, the platform could suggest to parents supplemental materials based on the child’s performance, like additional workbooks or videos to watch.

Kidaptive had specifically targeted the Korean market a few years ago with the acquisition of Hodoo English, an MMORPG which teaches children English. The acquisition was for both the IP and the team, giving the company a foothold in Korea, and a way to expand into China.

In addition to the deal with Woongjin, Kidaptive also has projects in the works in India and China. These are still under NDA, but the deal in China, which launches at the end of this summer, involves a large brick-and-mortar retailer that sells its own educational technology products (physical goods), which it wants to enhance with parental feedback mechanisms from Kidaptive.

In India, several deals are in the works, which Kidaptive hopes to announce by Q3.

Meanwhile, Kidaptive is working with the U.S. government and PBS KIDS a part of a $100 million five-year federal grant to create a personalized learning ecosystem. Kidaptive will be providing the adaptivity and learner profile management—two central features of the grant, says Kidaptive CEO P.J. Gunsagar.

“Our ability to ask the right questions at the right time by understanding who the learner is and provide actionable insights is unique. Just like Facebook has created a social graph, and LinkedIn a professional graph, our goal is to create are learning graph,” he explains.

The company is live with one PBS KIDS app associated with digital series The Ruff Ruffman Show, but it will be rolling out in two or three more this year, and multiple apps over the next few years.

As Kidaptive becomes further integrated across this PBS KIDS ecosystem of apps, the learner profiles will take into consideration the data generated from across all the PBS KIDS app where it’s live.

However, Gunsagar stresses that parents are in control of how this data is used.

“You own the learner model, not us…this is the parents’ and the childs’ model, it stays with them to make sure we’re optimizing the experience for them the way they want,” he says. The parents will be able to control how this data is used by requesting insights or not, or by disallowing the data to be shared across apps, if they don’t want it to be.

Gunsagar says big data for learning is starting to take off, and he believes his company will achieve profitability within the next 12 months as a result of its deals. It expects to manage 10 million active learner profiles within the next four years.

With the funding, Kidaptive plans to increase its 50-person team by 20 percent in the U.S. and 20 percent in Korea. It will also hire 5 people in China and 3 in India. The product itself will be further developed as well, with the next focus on test score prediction – something that half a dozen test prep companies in India and China talking with Kidaptive are now interested in.

Featured Image: Aping Vision / STS/Getty Images



Hypergiant helps big brands look beyond the AI buzzwords

18:37 | 20 February

Artificial intelligence and machine learning are phrases that get tossed around a lot these days, to the point where they’re starting to seem meaningless. In fact, Ben Lamm said he’s seen the problem firsthand at his chatbot startup Conversable.

“We kind of noticed this huge gap,” Lamm said. “Everybody has an emotional reaction to AI, everybody wants AI, nobody seems to know what that means.”

So Lamm founded a new startup called Hypergiant, which will work with large brands and enterprise to address address what he described as “this hunger for pragmatism in AI.”

In his view, most existing AI solutions either require “super powerful” technology, or they’re “complete BS marketing fluff.” Lamm’s goal is to find the sweet spot in the middle, where the technology can be used by Fortune 500 companies to solve real business problems.

For example, Hypergiant has already worked with TGI Friday’s to create Flanagan, an AI-powered mixologist. Sound gimmicky? Well, Lamm said Flanagan allows the restaurant chain to collect more data about its customers’ tastes, and to increase loyalty by offering personalized drink recommendations.

There are actually three divisions within Hypergiant. At Hypergiant Applied Sciences, the team will be working to develop and commercialize its own AI products. However, when it comes to working with brand costumers, Lamm said Hypergiant Space Age Solutions (yes, that’s the real name) will happily adopt whatever technology best meets the customer’s needs.

And then there’s Hypergiant Ventures, which invests in AI startups. It’s already backed Pilosa, Cerebri AI and Clearblade.

Lamm founded Hypergiant with two of his old colleagues from Chaotic Moon, the technology studio that Accenture acquired in 2015 — John Fremont (who served as artificial intelligence lead at Accenture after the acquisition and is now Hypergiant’s chief strategy officer) and Will Womble (who serves as chief revenue officer). And while Lamm is Hypergiant’s executive chairman and CEO, he’ll continue working as CEO at Conversable.

Featured Image: Aniwhite/Shutterstock



xMatters snares $40 million Series D led by Goldman Sachs Private Capital Investing

18:00 | 20 February

When a crisis happens and your system is down, it’s easy for panic and chaos to ensue. Large companies use a range of tools to monitor system health, and finding the source of the problem and ensuring the proper personnel are involved is not always easy.

That’s where xMatters comes in. It acts as an uber monitoring tool allowing you to understand the source of your problem and getting the right people involved to fix it. Today the company announced it has closed a $40 million Series D round led by Goldman Sachs Private Capital Investing.

The company, which launched in 2010, has raised almost $10 million in equity financing and $45 million in debt financing to-date, according to Crunchbase data. Today’s round brings the total raised in debt and equity financing to almost $95 million.

“We provide an incident management platform, that takes data and signals [from various tools] and helps [teams] to solve the incident as quickly as they can. We’ve been successfully doing that for a number of years,” xMatters CEO Troy McAlpin told TechCrunch.

The platform doesn’t stop there though. It can inform the customer that the system is down and that you are taking steps to fix the problem. What’s more, it provides a post-mortem for what happened and uses a machine learning component so that if a similar set of circumstances come together again in the future, the system can prevent it, or at least warn system admins that there could be a problem coming before it happens.

xMatters main console. Photo: xMatters

The tool uses signals from a variety of popular tools including New Relic, Dynatrace, Atlassian, Splunk and Servicenow to monitor overall system health. McAlpin says a big difference between his company’s solutions and those of competing products like PagerDuty and Everbridge, is that they move the discussion to the tools that operations teams are using like Jira and Slack, rather than moving them into their tool to resolve the issues. xMatters is simply the glue holding the incident response together among the different components. Customers work in their tools of choice to resolve it.

With the new money in hand, they plan to expand geographically including opening three R&D centers in Canada in Vancouver, Victoria and Montreal to help expand their machine learning and data science understanding. The company processes over a million responses a day and they want to learn to use that data more effectively to help their customers proactively monitor their applications and systems.

The company currently has 500 customers including Vodaphone, Box, MGM and Manpower. While McAlpin wouldn’t share revenue figures, he said the company was growing revenue 50 percent year over year.

Featured Image: Getty Images



Tandem launches a credit card that offers cashback and no fees when spending abroad

15:11 | 20 February

Tandem, the U.K. challenger bank that recently acquired the banking arm of the famous Harrods department store partly in order to get its banking license back on track, has launched its first banking product: a credit card that offers cashback on every purchase and no exchange fees when spending abroad.

The new card is designed to work with Tandem’s Personal Finance Manager (PFM) app, which you plug into your existing bank accounts and credit cards to get spending insights and set budgeting goals to help you better manage your money.

The challenger bank, like many other consumer-facing fintechs, wants to become your financial control centre from which it can connect to and offer financial services, either products of its own, such as the newly launched Tandem credit card, or through partnerships with other fintech startups or bigger providers. On that note, Tandem says it plans to offer a savings account in the near future.

Specifically, Tandem says its new credit card brings customers a competitive combination of cashback on all purchases (0.5 percent), no overseas transaction fees, a “market leading” exchange rate, and real time updates when you purchase.

It isn’t the only credit card of its ilk in the U.K. — there are a number of cashback-styled credit cards — although Tandem isn’t charging a monthly fee, which is certainly a draw. (Its APR of 18.9 percent isn’t the lowest on the market, however).

It also points to the startup bank’s initital “attack vector” (as Monzo’s Tom Blomfield calls each fintech’s entrance point): come for the cashback credit card and zero fees when spending abroad, and stay for the PFM and soon-to-launch savings deposit accounts.

The latter, of course, means as a licensed bank Tandem can begin lending out a portion of those deposits, which would begin to form the basis of a proven business model.

To that end, Tandem says it chose to launch with a credit card for multiple reasons. A credit card gives customers free protection on purchases over £100, so you’ll get your money back if, for example, a retailer goes bust. “On top of this, everyone needs some extra cash once in a while. Tandem want to be there when you need some money to cover an emergency cost,” says the self-described “Good Bank”.

Adds Ricky Knox, Tandem’s CEO, in a statement: “With our new banking license, our banking app and our new credit card, 2018 is going to be a great year for Tandem. We are looking forward to getting our colourful cards into the hands of customers – I can’t wait to start seeing customers tapping around town”.



Homie raises $4M to help London’s ‘Generation Rent’ find their next property

11:00 | 20 February

So bad is London’s housing crisis, which sees house prices make homeownership a pipe dream for many, a 2015 report by PWC reckons that by 2025 more than half of under 40 year olds will be living into rental properties.

The answer, of course, is to build more affordable and/or social housing, but that hot political potato is continually kicked to into the long grass and, even at its most optimistic, will take a generation to fix. In other words, so-called ‘Generation Rent’ is real and here to stay.

But one societal crisis is another startup’s opportunity, and we’ve already seen a number of companies crop up to serve the rental needs of Londoners and people living in other housing stock-starved cities.

These include consumer-facing apps like Acasa, which wants to make it easy to move from one houseshare to another, B2B services like the recently troubled Goodlord, which is digitising the rental process on behalf of lettings agencies, and a plethora of online estate agents, such as Open Rent, Home Made and Rentify, that help home owners rent out their property.

Another nascent proptech startup targeting ‘Generation Rent’ is London-based Homie, which might best be described as a concierge-style service that promises to save Londoner’s time, hassle and money when going in search of their next rental property. Today the company is disclosing that it has raised a further $4 million in Seed funding in a round led by Connect Ventures, with participation from Venture Friends, Seedcamp, and The Family. It brings total funding raised to date by Homie to $6 million since being founded in 2016.

Claiming to help renters “find, view and agree the homes they want in as little as three days,” the way Homie works is as follows:

You first enter your home search details and then get assigned your own Homie, a ‘personal agent’ that runs your home search and sends you a curated list of properties that matches your needs. That’s about as #Lazyweb as it comes.

Next up you’re asked to choose your favourites via the web-app and book viewings online at your preferred date and time. The Homie then schedules all viewings with multiple agencies into one tour. They then act as the your agent, accompanying you on a cab journey across London to visit your top ten properties, whilst offering “unbiased advice” on the area.

At this point you are probably wondering how all of this is viable from a unit economics point of view. Founder and CEO Alex Eid tells me Homie generates revenue by acting as a broker between renters and real-estate agents. “We make introductory fees from the agents we place the tenants in. Very much like the Deliveroo model,” he says.

Typical Homie customers are said to be students (“first time renters who don’t understand the real estate market, need to organise the logistics to move in with their friends, and need to find a solution that best fits their limited budget”), young professionals who have limited time to go house hunting, and millennial expats who need help figuring out the best place to live and navigating the market for the best value homes.

“Our direct competition are relocation agents, expensive private agents used by corporates to find accommodation for their employees moving from one city to another. An unaffordable option for most people,” says Eid.

“The main competition however is renters doing it themselves. They have no other option but to search multiple property sites and go through the hassle of contacting real estate agencies one by one to book single viewings. Homie is a first of its kind consumer brand for real estate services; provides a complete and tailored home search across the whole market, property scheduling, transportation and guidance”.



Algorithmic zoning could be the answer to cheaper housing and more equitable cities

23:18 | 19 February

Zoning codes are a century old, and the lifeblood of all major U.S. cities (except arguably Houston), determining what can be built where and what activities can take place in a neighborhood. Yet as their complexity has risen, academics are increasingly exploring whether their rule-based systems for rationalizing urban space could be replaced with dynamic systems based on blockchains, machine learning algorithms, and spatial data, potentially revolutionizing urban planning and development for the next one hundred years.

These visions of the future were inspired by my recent chats with Kent Larson and John Clippinger, a dynamic urban thinking duo who have made improving cities and urban governance their current career focus. Larson is a principal research scientist at the MIT Media Lab, where he directs the City Science Group, and Clippinger is a visiting researcher at the Human Dynamics Lab (also part of the Media Lab), as well as the founder of non-profit ID3.

One of the toughest challenges facing major U.S. cities is the price of housing, which has skyrocketed over the past few decades, placing incredible strain on the budget of young and old, singles and families alike. The average one-bedroom apartment is $3,400 in San Francisco, and $3,350 in New York City, making these meccas of innovation increasingly out-of-reach of even well-funded startup founders let alone artists or educators.

Housing is not enough to satiate the modern knowledge economy worker though. There is an expectation that any neighborhood is going to have a laundry list of amenities, from nice and cheap restaurants, open spaces, and cultural institutions to critical human services like grocery stores, dry cleaners, and hair salons.

Today, a zoning board would simply try to demand that various developments include the necessary amenities as part of the permitting process, leading to food deserts and the curious soullessness of some urban neighborhoods. In Larson and Clippinger’s world though, rules-based models would be thrown out for “dynamic, self-regulating systems” based around what might agnostically be called tokens.

Every neighborhood is made up of different types of people with different life goals. Larson explained that “We can model these different scenarios of who we want working here, and what kind of amenities we want, then that can be delineated mathematically as algorithms, and the incentives can be dynamic based on real-time data feeds.”

The idea is to first take datasets like mobility times, unit economics, amenities scores, and health outcomes, among many others and feed that into a machine learning model that is trying to maximize local resident happiness. Tokens would then be a currency to provide signals to the market of what things should be added to the community or removed to improve happiness.

A luxury apartment developer might have to pay tokens, particularly if the building didn’t offer any critical amenities, while another developer who converts their property to open space might be completely subsidized by tokens that had been previously paid into the system. “You don’t have to collapse the signals into a single price mechanism,” Clippinger said. Instead, with “feedback loops, you know that there are dynamic ranges you are trying to keep.”

Compare that systems-based approach to the complexity we have today. As architectural and urban planning tastes have changed and developers have discovered loopholes, city councils have updated the codes, and then updated the updates. New York City’s official zoning book is now 4,257 pages long (warning: 83MB PDF file), the point of which is to rationalize what a beautiful, functional city should look like. That complexity has bred a massive influence and lobbying industry as well as startups like Envelope which try to make sense of it all.

A systems-based approach would throw out the rules while still seeking positive end results. Larson and Clippinger want to go one step further though and integrate tokens into everything in a local neighborhood economy, including the acquisition of an apartment itself. In such a model, “you have a participation right,” Clippinger said. So for instance, a local public school teacher or a popular baker might have access to an apartment unit in a neighborhood without paying the same amount as a banker who doesn’t engage as much with neighbors.

“Wouldn’t it be great to create an alternative where instead of optimizing for financial benefits, we could optimize for social benefits, and cultural benefits, and environmental benefits,” Larson said. Pro-social behavior could be rewarded through the token system, ensuring that the people who made a neighborhood vibrant could remain part of it, while also offering newcomers a chance to get involved. Those tokens could also potentially be fungible across cities, so a participation right token to New York City might also give you access to neighborhoods in Europe or Asia.

Implementation of these sorts of systems is certainly not going to be easy. A few years ago on TechCrunch, Kim-Mai Cutler wrote a deeply-researched analysis of the complexity of these issues, including the permitting process, environmental reviews, community support and opposition, as well as the basic economics of construction that make housing and development one of the most intractable policy problems for municipal leaders.

That said, at least some cities have been excited to trial parts of these algorithmic-based models for urban planning, including Barcelona and several Korean cities according to the two researchers. At the heart of all of these experiments though is a belief that the old models are no longer sufficient for the needs of today’s citizens. “This is a radically different vision … it’s post-smart cities,” Clippinger said.

Featured Image: Nicky Loh/Bloomberg/Getty Images


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