Search results for "broad"
Horton Hears a Backhoe
Total conversion of San Diego’s postmodern Horton Plaza sails to approval
Re-Imagining the Modern
New book grapples with ambitious, contentious moment in Pittsburgh’s urban history
During my days as a technology vendor, I chafed at the idea of introducing government standards for technology developed by a polyglot group of stakeholders. Users, software companies, and bureaucrats often sought a “lowest common denominator” between various software, sacrificing innovation and progress for vague notions like “open access.” In the early days of Building Information Modeling (BIM), several such efforts emerged, the most prominent of which were the General Services Administration (GSA) attempts to create a standard and the development of BIM-derived digital permitting submissions in Singapore. Both projects garnered much attention but gained little traction in the form of implemented technologies or operating protocols—at least in their early forms. But they had one important effect: In the loosely organized, disparate network of the building industry supply chain, government could provide a galvanizing influence. At least when government spoke, the industry listened.
In 2011, however, we witnessed a welcome change with the publication of the United Kingdom’s “Government Construction Strategy.” Much of the early theory about industry productivity and need for process integration had long emerged from that side of the Atlantic—for example, Sir Roger Egan’s seminal “Rethinking Construction” report—but there was little action. The David Cameron government, however, saw construction as a critical economic engine, concluding that improving the cost and carbon impacts of building while bolstering U.K. capabilities as a global building leader would drive growth. One pillar of the resulting government policy document was BIM, and the following requirement: “2.32. Government will require fully collaborative 3-D BIM (with all project and asset information, documentation, and data being electronic) as a minimum by 2016. A staged plan will be published with mandated milestones showing measurable progress at the end of each year.”
As upwards of 40 percent of construction dollars in the U.K. are spent by the government, the industry snapped to attention, formed cross-industry collaborations, and established and implemented BIM requirements for all their projects (with logistical and financial support from the government). BIM adoption shot up from 10 percent in 2012 to 70 percent by 2018, and savings on the first prototype projects were estimated at as much as 2.5 percent of the total lifetime cost of designing, building, and operating the project. By my own estimate, that’s as much as five times the fees likely paid to the design team and 25 percent of original construction cost. Not bad for a first effort. And, in typical British fashion, the resulting standards (search online for “PAS 1192”) were clear, rigorous, and implementable.
The success of the U.K. effort has spread across Europe, and EU government leaders have taken similar roles (at least until Brexit) in developing standards for the entire European Union, while also establishing footholds with other global networks, most notably in Latin America and Southeast Asia. Singapore, in collaboration with the U.K. team, has spurred a multiyear effort to create a standards collaboration there. As we approach the end of the second decade of BIM, one can see the slow emergence of a global network of BIM standards leading to a single market BIM, catalyzed by what may be the only cohering force in the building universe: the long arm of the law.
Now that the technology is mature and its use stable, global BIM standards are a good thing. The U.K. effort rightly became the basis of a worldwide standard created by the International Organization for Standardization (ISO; see ISO Standard 19650) and released in early 2019. Based on the now viral PAS 1192, ISO describes its work as “recommended concepts and principles for business processes across the built environment sector in support of the management and production of information during the life cycle of built assets (referred to as 'information management’) when using building information modelling (BIM).” Note the emphasis on business process driving the technology standard; precisely the right relationship for creating a stable platform for the otherwise disparate players in the global building industry.
And there’s an even larger idea here. What’s most powerful about the U.K.’s trailblazing work on BIM standards is the origin point: Rather than start with the prosaic, bottom-up question of lowest common denominator tech standards, they chose a broad organizing principle—improving building through technology is good for the economy and the environment, and doing this in a way that is agnostic to specific technologies or proprietary software drives competitive innovation that helps the entire market.
Driving BIM standards has further benefits to government, not the least of which is transactional transparency. State-run construction is rife with overbidding, conflicts of interest, and corruption. A bedrock principle of “collaborative 3-D BIM” is information clarity—all members of the building team can see and understand the physical and technical characteristics of the project in parametric three dimensions, along with the resulting arithmetic of cost projection—which makes it that much harder to manipulate a bid.
In the early days of the U.K. project there was an appointed Chief Government Construction Advisor with a direct line to high-level policy makers in the Cabinet. The United States’ construction market, roughly five times the size of the U.K.’s, could surely benefit from some policy-driven federal leadership, something that is certainly hard to imagine in today’s administration and go-go economy. But when the inevitable downturn does occur, we’ll know which way to look for inspiration for industry improvement.
Palladio and his architecture come alive in new film
Location, location, location
How Baidu Maps turns location data into 3-D cityscapes—and big profits
Level 3, number 203. Turn right 10 feet. Go straight for 15 feet. The best way to experience data's strong grip on everyday life in China is to open up Baidu Maps, a mapping app by China’s biggest search engine company, and walk around a shopping mall for one afternoon. Inside the building, a network of Bluetooth beacons, Wi-Fi modems, and satellites from a global navigation satellite system whir and ping through the air and the ionosphere to determine your precise location. The map on the Baidu app tilts to reveal an elaborately modeled 3-D cityscape.
The resolution of Baidu Maps is stunning: Entire cities are modeled in 3-D. Within public buildings, the floorplan of each building level is precisely mapped. As I stand inside the Taikoo Hui Mall in the city of Guangzhou, China, I search for a store within the mall. Baidu Maps reveals which level the store is on and how many meters I need to walk. Strolling through the mall with the app tracking my location with a blue dot on the screen, life starts to feel like a virtual reality experience. The difference between the map's 3-D model and the reality beneath my feet is smaller than ever. The 3-D model makes an uncanny loop: Virtual models were used by architects and designers to design these spaces, which now unfold on a messy plane between real space and screen space.
China now has its own tech giants—Alibaba, JD.com, Tencent Holdings, and Baidu—homegrown behind the Great Firewall of China. Like their American counterparts, these companies have managed to surveil their users and extract valuable data to create new products and features. Baidu began as a search engine, but has now branched out into autonomous driving, and therefore, maps. The intricacy of its 3-D visualizations is the result of over 600 million users consulting the app for navigation every day or using apps that rely on Baidu Maps in the background, such as weather apps that rely on its geolocation features.
The tech company, like its counterparts such as Google, take advantage of multiple features available in smartphones. Smartphones possess the ability to determine users’ positions by communicating with an array of satellites such as GPS (Global Positioning Service); GLONASS, Russia’s version of GPS; or BeiDou, China’s satellite navigation system. Such satellite systems are public infrastructures created by American, Russian, and Chinese governments, respectively, that enable our phones to determine users’ precise longitude and latitude coordinates. The majority of apps and services on smartphones rely on location services, from food delivery to restaurant reviews. However, satellite navigation systems are still imprecise—they are often a few meters off, with anything from the weather to tall buildings affecting accuracy.
However, smartphones contain more than satellite signal receiver chips. A slew of other sensors, such as accelerometers, light sensors, and magnets are embedded in the average smartphone. In 2015, Baidu invested $10 million in IndoorAtlas, a Silicon Valley startup that specializes in indoor mapping. The company's technology is at the forefront of magnetic positioning, which allows indoor maps at 1-meter accuracy to be created simply by using an average smartphone. This technology relies on the Earth's geomagnetic field and the magnets in smartphones. By factoring in the unique magnetic "fingerprint" of each building based on the composition of its materials, such as steel, a building's floor plan can be mapped out without any data provided by the architect. However, this strategy requires user data at scale; multiple user paths need to be recorded and averaged out to account for any anomalies. Gathering large amounts of data from users becomes an imperative.
Floorplans aside, magnetic positioning is not the only dimension of user location data collection that allows data to become a spatial model. As people drive, bike, and walk, each user generates a spatial "trace" that also has velocity data attached to it. Through such data, information about the type of path can be derived: Is it a street, a sidewalk, or a highway? This information becomes increasingly useful in improving the accuracy of Baidu Maps itself, as well as Baidu's autonomous vehicle projects.
The detailed 3-D city models on Baidu Maps offer data that urban designers dream of, but such models only serve Baidu's interests. Satellite navigation system accuracy deteriorates in urban canyons, due to skyscrapers and building density, obscuring satellites from the receiver chip. These inaccuracies are problematic for autonomous vehicles, given the "safety critical" nature of self-driving cars. Baidu's 3-D maps are not just an aesthetic “wow factor” but also a feature that addresses positioning inaccuracies. By using 3-D models to factor in the sizes and shapes of building envelopes, inaccuracies in longitude and latitude coordinates can be corrected.
Much of this research has been a race between U.S. and Chinese companies in the quest to build self-driving cars. While some 3-D models come from city planning data, in China's ever-changing urban landscape, satellite data has proved far more helpful in generating 3-D building models. Similar to Google's 3-D-generated buildings, a combination of shadow analysis, satellite imagery, and street view have proved essential for automatically creating 3-D building models rather than the manual task of user-generated, uploaded buildings or relying on city surveyors for the most recent and accurate building dimensions.
None of this data is available to the people who design cities or buildings. Both Baidu and Google have End User License Agreements (EULAs) that restrict where their data can be used, and emphasize that such data has to be used within Baidu or Google apps. Some data is made available for computer scientists and self-driving car researchers, such as Baidu's Research Open-Access Dataset (BROAD) training data sets. Most designers have to rely on free, open-source data such as Open Street Maps, a Wikipedia-like alternative to Baidu and Google Maps. By walling off valuable data that could help urban planning, tech companies are gaining a foothold and control over the reality of material life: they have more valuable insights into transport networks and the movements of people than urban designers do. It's no surprise then, that both Baidu and Google are making forays into piloting smart cities like Toronto’s Quayside or Shanghai's Baoshan District, and gaining even greater control over urban space. No doubt, urban planning and architecture are becoming increasingly automated and privately controlled in the realm of computer scientists rather than designers.
In Shoshana Zuboff's 2019 book, The Age of Surveillance Capitalism, she examines how tech companies throughout the world are employing surveillance and data extraction methods to turn users into free laborers. Our “behavioral surplus,” as she terms it, becomes transformed into products that are highly lucrative for these companies, and feature proprietary, walled-off data that ordinary users cannot access, even though their labor has helped create these products. These products are also marketed as “predictive,” which feeds the desires of companies that hope to anticipate users’ behavior—companies that see users only as targets of advertising.
Over the past several years, American rhetoric surrounding the Chinese “surveillance state” has reached fever pitch. But while China is perceived to be a single-party communist country with state-owned enterprises that do its bidding, the truth is, since the 1990s, much of the country’s emphasis has been on private growth. Baidu is a private company, not a state-owned enterprise. Companies like Baidu have majority investment from global companies, including many U.S.-based funds like T. Rowe Price, Vanguard, and BlackRock. As China's economy slows down, the government is increasingly pressured to play by the rules of the global capitalist book and offer greater freedom to private companies alongside less interference from the government. However, private companies often contract with the government to create surveillance measures used across the country.
The rhetoric about the dangers of Chinese state surveillance obfuscates what is also happening in American homes—literally. As Google unveils home assistants that interface with other “smart” appliances, and Google Maps installed on mobile phones tracks user locations, surveillance becomes ubiquitous. Based on your location data, appliances can turn on as you enter your home, and advertisements for milk from your smart fridge can pop up as you walk by the grocery stores. Third-party data provider companies also tap into geolocation data, and combined with the use of smart objects like smart TVs, toasters, and fridges, it's easy to see why the future might be filled with such scenarios. Indeed, if you own certain smart appliances, Google probably knows what the inside of your home is like. In 2018, iRobot, the maker of the Roomba vacuum, announced that it was partnering with Google to improve the indoor mapping of homes, and now setting up a Roomba with Google Home has never been easier. Big tech companies in the U.S. would like us to believe that surveillance is worse elsewhere, when really, surveillance capitalism is a global condition.
Over the past 30 years, cities around the world have been the locus of enormous economic growth and corresponding increases in inequality. Metropolitan areas with tech-driven economies, such as the Shenzhen-Guangzhou-Hong Kong corridor and the Greater Bay Area, are home to some of the largest tech companies in the world. They are also home to some of the most advanced forms of technological urbanism: While Baidu may not have every single business mapped in rural China, it certainly has the listing of every shop in every mall of Guangzhou.
The overlap between cities as beacons of capital and as spaces where surveillance is ubiquitous is no coincidence. As Google’s parent company, Alphabet, makes moves to build cities and as Baidu aggressively pursues autonomous driving, data about a place, the people who live there, and their daily movements is increasingly crucial to the project of optimizing the city and creating new products, which in turn generates more wealth and more inequality. Places like San Francisco and Shenzhen are well-mapped by large tech companies but harbor some of the worst income gaps in the world.
The "smart city" urbanism enabled by surveillance and ubiquitous data collection is no different from other forms of development that erode affordable housing and public space. Reclaiming our cities in this digital age is not just about reclaiming physical space. We must also reclaim our data.
Not Throwing Away His Shot
AN interviews Hamilton set designer David Korins about the show’s exhibition
Going Down, Coming Up
Forty-five story jail tower could be coming to Lower Manhattan
In most major cities of the world, an urban tech landscape has emerged. One day, we were working on our laptops at Starbucks, and the next, we were renting desks at WeWork. We embedded our small architectural and design firms in low-rent spaces in old factories and warehouses, and then we emerged as “TAMI” (technology, advertising, media, and information) tenants, heating up the commercial real estate market. Friends who could write computer code started businesses in their apartments before moving into tech incubators and accelerators, which then morphed into a “startup ecosystem.” Though a competitive city in the 1990s might only have had one cutely named cluster of startups—New York’s Silicon Alley, San Francisco’s Media Gulch—by the 2010s, many cities were building “innovation districts.” How did this happen? And what does it mean for these cities’ futures?
The simplest explanation is that cities are catching up to the digital economy. If computers and the web are one of the primary means of production for the 21st century, all cities need the infrastructure—broadband, connectivity, flexible office space—to support them. Companies that control the means of production also need raw material—the data that newly “smart” cities can provide—to develop concepts, test prototypes, and market their wares. Local governments and business leaders have always reshaped cities around the businesses that profit from new technology; In the 19th century, they built railroad stations, dug subway tunnels, and laid sewage pipes; in the 20th century, they wired for electricity and erected office towers. Maybe we should ask why it has taken cities so long to rebuild for digital technology.
Inertia is one answer, and money is another. Entrenched elites don’t readily change course, especially if a new economy would challenge their influence on local politics and labor markets. Think about the long dominance of the auto industry in Detroit and the financial industry in New York, both late converts to digital technologies like self-driving cars and electronic banking, respectively.
Another reason for cities’ slow awakening to the tech economy is the post–World War II prominence of suburban office parks and research centers, part of the mass suburbanization of American society. On the East Coast, tech talent began to migrate from cities in the early 1940s, when Bell Labs, the 20th-century engineering powerhouse, moved from Lower Manhattan to a large tract of land in suburban New Jersey. A few years later, on the West Coast, Stanford University and the technology company Varian Associates spearheaded the construction of an electronics research park on a university-owned site of orange groves that later became known as Silicon Valley.
Silicon Valley got the lion’s share of postwar federal government grants and contracts from the military for microwave electronics innovation, missile research, and satellite communications. Venture capital (VC) soon followed. Although VC firms began in New York and Boston, by the 1960s and ’70s they were setting up shop in the San Francisco Bay Area.
The Valley’s hegemony was solidified in the 1980s by the rise of the personal computer industry and the VCs who got rich by investing in it. The suburban tech landscape so artfully represented in popular mythology by Silicon Valley’s DIY garages and in physical reality by its expansive corporate campuses was both pragmatically persuasive and culturally pervasive. Its success rested on a triple helix of government, business, and university partnerships, defining an era from Fairchild, Intel, and Hewlett-Packard (the first wave of major digital technology companies) to Apple, Google, and Facebook.
In contrast to the suburban postwar growth of Silicon Valley, the urban tech landscape was propelled by the rise of software in the early 2000s and gained ground after the economic crisis of 2008. Software was easier and cheaper to develop than computers and silicon chips—it wasn’t tied to equipment or talent in big research universities. It was made for consumers. Most important, with the development of the iPhone and the subsequent explosion of social media platforms after 2007, software increasingly took the form of apps for mobile devices. This meant that software startups could be scaled, a crucial point for venture capital. For cities, however, the critical point was that anyone, anywhere, could be both an innovator and an entrepreneur.
The 2008 economic crisis plunged cities into a cascade of problems. Subprime mortgages cratered, leaving severely leveraged households and financial institutions adrift. Banks failed if they didn’t get United States government lifelines. Financial jobs at all levels disappeared; local tax revenues plummeted. While mayors understood that they had to end their dependence on the financial sector—a realization most keenly felt in New York—they also faced long-term shrinkage in manufacturing sectors and office vacancies.
London had already tried to counter deindustrialization with the Docklands solution: Waterfront land was redeveloped for new media and finance, and unused piers and warehouses were converted for cultural activities. In Spain, this strategy was taken further in the 1990s by the construction of the Guggenheim Bilbao museum and the clearing of old industrial plants from that city’s waterfront. By the early 2000s, Barcelona’s city government was building both a new cultural district and an “innovation district” for digital media, efforts that bore a striking resemblance to the 1990s market-led development of the new media district in Manhattan’s Silicon Alley and the growth of tech and creative offices in Brooklyn’s DUMBO neighborhood.
Until the economic crisis hit, both spontaneous and planned types of urban redevelopment were connected to the popular “creative city” model promoted by Charles Landry in London and Richard Florida in Pittsburgh (later, Toronto). In 2009, however, economic development officials wanted a model that could create more jobs. They seized on the trope of “Innovation and Entrepreneurship” that had been circulating around business schools since the 1980s, channeling the spirit of the economic historian Joseph Schumpeter and popularized in a best-selling book by that title by the management guru Peter Drucker. Adopted by researchers at the Brookings Institution, urban innovation districts would use public-private partnerships to create strategic concentrations of workspaces for digital industries. It seemed like a brilliant masterstroke to simultaneously address three crucial issues that kept mayors awake at night: investments, jobs, and unused, low-value buildings, and land.
In the absence of federal government funding, real estate developers would have to be creative. They built new projects with money from the city and state governments, the federal EB-5 Immigrant Investor Visa Program for foreign investors, and urban impact funding that flowed through investment banks like Goldman Sachs. Federal tax credits for renovating historic buildings and investing in high-poverty areas were important.
Though all major cities moved toward an “innovation economy” after 2009, New York’s 180-degree turn from finance to tech was the most dramatic. The bursting of the dot-com bubble in 2000 and 2001, followed by the September 11 attack on the World Trade Center and an economic recession, initially kept the city from endorsing the uncertainty of tech again. Michael Bloomberg, mayor from 2001 to 2013, was a billionaire whose personal fortune and namesake company came from a fusion of finance and tech, most notably the Bloomberg terminal, a specially configured computer that brings real-time data to stock brokers’ and analysts’ desks. Yet, as late as 2007, Mayor Bloomberg, joined by New York’s senior senator Chuck Schumer, promoted New York as the self-styled financial capital of the world, a city that would surely triumph over its only serious rival, London. The 2008 financial crisis crumpled this narrative and turned the Bloomberg administration toward tech.
By 2009, the city’s business elites believed that New York’s salvation depended on producing more software engineers. This consensus motivated the mayor and his economic development officials to build big, organizing a global competition for a university that could create a dynamic, postgraduate engineering campus in New York. Cornell Tech emerged as the winner, a partnership between Cornell University and the Israel Institute of Technology. Between 2014 and 2017, the new school recruited high-profile professors with experience in government research programs, university classrooms, and corporate labs. They created a slew of partnerships with the city’s major tech companies, and the resulting corporate-academic campus made Roosevelt Island New York’s only greenfield innovation district. Not coincidentally, the founding dean was elected to Amazon’s board of directors in 2016.
The Bloomberg administration also partnered with the city’s public and private universities, mainly the aggressively expanding New York University (NYU), to open incubators and accelerators for tech startups. After NYU merged with Polytechnic University, a historic engineering school in downtown Brooklyn, the Bloomberg administration made sure the new engineering school could lease the vacant former headquarters of the Metropolitan Transportation Authority nearby, where NYU’s gut renovation created a giant tech center.
Meanwhile, the Brooklyn waterfront was booming. The Brooklyn Navy Yard added advanced manufacturing tenants and art studios to its traditional mix of woodworking and metalworking shops, food processors, and suppliers of electronics parts, construction material, and office equipment, and began to both retrofit old machine shops for “green” manufacturing and build new office space. While tech and creative offices were running out of space in DUMBO, the heads of the downtown Brooklyn and DUMBO business improvement districts came up with the idea of marketing the whole area, with the Navy Yard, as “the Brooklyn Tech Triangle.” With rezoning, media buzz, and a strategic design plan, what began as a ploy to fill vacant downtown office buildings moved toward reality.
Established tech companies from Silicon Valley and elsewhere also inserted themselves into the urban landscape. Google opened a New York office for marketing and advertising in 2003 but expanded its engineering staff a few years later, buying first one, then two big buildings in Chelsea: an old Nabisco bakery and the massive former headquarters of the Port Authority of New York and New Jersey. Facebook took AOL’s old offices in Greenwich Village. On the next block, IBM Watson occupied a new office building designed by Fumihiko Maki.
Jared Kushner’s brother, the tech investor Jonathan Kushner, joined two other developers to buy the Jehovah’s Witnesses’ former headquarters and printing plant on the Brooklyn-Queens Expressway. The developers converted the buildings into tech and creative offices and called the little district Dumbo Heights. By 2015, the growth of both venture capital investments and startups made New York the second-largest “startup ecosystem” in the world after Silicon Valley. Within the next three years, WeWork (now the We Company) surpassed Chase Bank branches as Manhattan’s largest commercial tenant.
All this development was both crystallized and crucified by Amazon’s decision to open half of a “second” North American headquarters (HQ2) in the Long Island City neighborhood of Queens, New York, in 2018. Amazon organized a competition similar to the Bloomberg contest that resulted in Cornell Tech, but in this case, the contest was a bidding war between 238 cities that offered tax credits, help with land assemblage, and zoning dispensations in return for 50,000 tech jobs that the company promised to create. But in announcing its selection, Amazon divided the new headquarters in two, supposedly placing half the jobs in New York and the other half in Crystal City, Virginia, a suburb of Washington, D.C. Many New Yorkers erupted in protest rather than celebration.
The amount of tax credits offered to the very highly valued tech titan, almost $3 billion in total, appeared to rob the city of funding for its drastic needs: fixing the antiquated subway system, repairing the aging public housing stock, and building affordable housing. The decision-making process, tightly controlled by Governor Andrew Cuomo and Mayor Bill de Blasio, enraged New York City Council members, none of whom had been given a role in either negotiating or modifying the deal. The deal itself was closely supervised by New York State’s Economic Development Corporation behind closed doors, without any provision for public input or approval.
Housing prices in Long Island City rose as soon as the deal was announced. A city economic development representative admitted that perhaps half of the jobs at HQ2 would not be high-paying tech jobs, but in human resources and support services. In a final, painful blow, Amazon promised to create only 30 jobs for nearly 7,000 residents of Queensbridge Houses, the nearby public housing project that is the largest in the nation.
Amazon representatives fanned their opponents’ fury at public hearings held by the New York City Council. They said the company would not remain neutral if employees wanted to unionize, and they refused to offer to renegotiate any part of the deal. Opponents also protested the company’s other business practices, especially the sale of facial recognition technology to the U.S. Immigration and Customs Enforcement agency (ICE). Yet surveys showed that most registered New York City voters supported the Amazon deal, with an even higher percentage of supporters among Blacks and Latinos. Reflecting the prospect of job opportunities, construction workers championed the deal while retail workers opposed it. The governor and mayor defended the subsidies as an investment in jobs. Not coincidentally, Amazon planned to rent one million square feet of vacant space in One Court Square, the former Citigroup Building in Long Island City, before building a new campus on the waterfront that would be connected by ferry to Cornell Tech.
After two months of relentless, vocal criticism, in a mounting wave of national resentment against Big Tech, Amazon withdrew from the deal. Elected officials blamed each other, as well as a misinformed, misguided public for losing the economic development opportunity of a lifetime.
Yet it wasn’t clear that landing a tech titan like Amazon would spread benefits broadly in New York City. A big tech company could suck talent and capital from the local ecosystem, deny homegrown startups room to expand, and employ only a small number of “natives.”
From San Francisco to Seattle to New York, complaints about tech companies’ effect on cities center on privatization and gentrification. In San Francisco, private buses ferry highly paid Google workers from their homes in the city to the company’s headquarters in Silicon Valley, green space and cafes in the Mid-Market neighborhood proliferate to serve Twitter employees and other members of the technorati, low-income Latinos from the Mission district are displaced by astronomical rents—all of these factors stir resentment about Big Tech taking over. In Seattle, Amazon’s pressure on the city council to rescind a tax on big businesses to help pay for homeless shelters also aroused critics’ ire. Until recently, moreover, tech titans have been unwilling to support affordable housing in the very markets their high incomes roil: East Palo Alto and Menlo Park in California, and Redmond, Washington.
It remains to be seen whether urban innovation districts will all be viable, and whether they will spread wealth or instead create highly localized, unsustainable bubbles. Venture capital is already concentrated in a small number of cities and in a very few ZIP codes within these cities. According to the MIT economist David Autor, although the best “work of the future” is expanding, it is concentrated in only a few superstar cities and only represents 5 percent of all U.S. jobs.
Yet urban tech landscapes emerge from a powerful triple helix reminiscent of Silicon Valley. Elected officials promise jobs, venture capitalists and big companies make investments, and real estate developers get paid. Though these landscapes glitter brightly compared to the dead spaces they replace, they don’t offer broad participation in planning change or the equitable sharing of rewards.
Sharon Zukin is a Professor of Sociology at the City University of New York, Brooklyn College, and is author of the forthcoming book The Innovation Complex: Cities, Tech, and the New Economy.