Companies to Watch: Private
Redwood City, CA; founded 2005; 50-100 employees
Funding Raised: $42 million
Offers software that allows telecom operators to insert text, graphic, and video advertisements into applications running on mobile devices such the iPhone and BlackBerry, creating a new revenue stream for operators and content providers. Read more company information.
New York City; founded 2005; 19 employees
Funding Raised: over $8 million
Its platform allows the same content to be distributed automatically in multiple channels, including YouTube, iTunes, and Internet-enabled television. Advertisers can target viewers watching specific categories of content. Read more company information.
Company: E Ink
Cambridge, MA; founded 1997; 127 employees
Funding Raised: over $150 million
Provides display technology for all the major e-readers, including those sold by Sony, Amazon, and Barnes and Noble. In the next two years, it is expected to face serious challenges from competitors such Plastic Logic and Pixel Qi, which use rival technologies. At press time, E Ink was merging with Prime View International, with the deal to be closed in December 2009. Read more company information.
Palo Alto, CA; founded 2004; over 900 employees
Funding Raised: over $700 million
Runs the world’s largest social-networking website, with more than 350 million active users. Facebook can host third-party applications and delivers targeted advertising. It is currently working to integrate its service into many different platforms, including gaming consoles and Internet-enabled television. Read more company information.
Los Angeles; founded 2007; 160 employees
Funding Raised: over $100 million
Offers video content that can be streamed from its website, effectively competing with cable and satellite television providers. NBC Universal, News Corp., Walt Disney, and Providence Equity Partners each have a stake. Read more company information.
New York City; founded 2007; 15 employees
Funding Raised: $3.8 million
Allows advertising to be incorporated into video content. The advertising is inserted at play time, allowing different audiences to be targeted with the same content. The incorporated material can also be interactive, potentially allowing viewers to purchase, for example, an item of clothing worn by an actor. Read more company information.
San Francisco, CA; founded 2006; 28 employees
Funding Raised: $8.2 million
Provides infrastructure that helps media companies develop, publish, and manage application programming interfaces, or APIs, which allow outside developers to incorporate another company’s content into their products. Read more company information.
Company: Pixel Qi
San Bruno, CA; founded 2008; number of employees not disclosed
Funding Raised: not disclosed
Is developing a technology for low-power, low-cost displays that are readable in direct sunlight. The company is moving into mass production, with netbooks targeted as the first application for its 3Qi screen. E-readers could be next. Read more company information.
San Francisco, CA; founded 2006; 70 employees
Funding Raised: $25.7 million
Uses machine learning techniques to provide advertisers with detailed demographic information about online audiences. The company hopes to encourage media organizations to submit traffic data by offering basic services free. Read more company information.
San Francisco, CA; founded 2006; 83 employees
Funding Raised: $155 million
Allows users to post 140-character messages. Twitter has become fertile ground for breaking news as interesting posts and links to external websites are rapidly reposted from user to user. The business model for sustaining the service remains unclear, perhaps even to Twitter. Read more company information.
Read more on Media:
Become an MIT Technology Review Insider for in-depth analysis and unparalleled perspective.Subscribe today
How DARPA Took On the Twitter Bot Menace with One Hand Behind Its Back
When DARPA ran a competition to find Twitter bots designed to influence online debates, it inspired a new generation of anti-bot strategies.
How One Intelligent Machine Learned to Recognize Human Emotions
Nobody knew how to identify people’s emotional states by looking at their brain waves. Then a machine learning algorithm stepped in.
How an AI Algorithm Learned to Write Political Speeches
Political speeches are often written for politicians by trusted aides and confidantes. Could an AI algorithm do as well?