FROM: http://www.businessweek.com/magazine/content/11_17/b4225060960537_page_5.htm
编者按：本文来自彭博商业周刊，文章非常的有深度也很长。科技泡沫会发生，但是我们通常都会受益于其留下的重大创新和突破。而当前我们正在经历的这个科技泡沫，它由社交网络驱动，最后可能什么都不会给我们留下。
FROM: http://www.businessweek.com/magazine/content/11_17/b4225060960537_page_5.htm
Tech bubbles happen, but we usually gain from the innovation left behind. This one—driven by social networking—could leave us empty-handed
2006年，一个名叫Jeff Hammerbacher（数学天才）且才刚刚走出校门一年的哈佛大学小子来到了Facebook，那时Facebook还仍处在自己的婴儿发展期，急需招聘数据分析人员来研究人们对Facebook的使用情况。小伙子来到公司后（当时总共也才100位员工），扎克伯格给了他一个很棒的头衔—-research scientist（研究科学家），让他研究人们大多是怎样使用社交网络的。更具体的说，他的这个职务是要帮助Facebook找出其为什么在有些学校很成功而在另外一些学校却表现糟糕的原因。此外，他还要帮助公司找出高中生和大学生行为上的不同之处。
Hammerbacher说到：“我当时的任务主要就是解决这些高级问题，要知道那时Facebook还真是没有任何工具可以解决那么复杂的问题的。”
于是在接下来的这两年里，Hammerbacher组织起了一支队伍，并为Facebook开发出了一套全新的分析技术。Facebook也因此收集到了用户大量的数据并对这些数据作了极为细致的分析，了解到了很多关于人际关系，行为趋势和欲望等方面的知识。然后其又用这些数据和知识来发展这家公司整个业务的基石—–精确广告。可以说Facebook的用户就像实验室的小白鼠一样，一言一行都被这套技术追踪着，直到最后Facebook将这些数据开放给给其他公司以赚取广告收益。在Facebook看来，他们所希望的就是越来越多的用户数据能够转化成更为精确的广告和更高的收入。
但是在Facebook呆了几年以后，Hammerbacher开始感觉不自在了。他觉得许多重大的计算机科学创新和突破很早之前就已经完成了。他也仔细的思考了一下周围的这些公司包括Facebook，Google和Twitter等，看着自己和同学在为这些公司卖命，他不禁开始觉得：“我们这一代最聪明的人竟然都在这里思考着怎样让人们去大量的点击广告，真衰。”
你也许会说Hammerbacher还算是一个有责任感的工程师，因为他对当前横行于互联网技术行业中的基于广告的商业模式和营销驱动文化是持反对意见的。其实在线广告自从互联网诞生后不久就有了，但是也直到最近这几年它才变成一个令硅谷极度癫狂的商业模式。此外包括Facebook，Zynga和Groupon在内的这些互联网创业公司也在人才争夺上表现得非常激烈，都期望能雇佣到最好的管理人员和工程师。投资者也疯狂跟进，向这些技术新秀们狠狠的砸钱，然后又将他们的估值送上云霄。而当这些创业公司取得“成功”后，不可避免的又会出现大量的山寨抄袭，投资者又同样肆无忌惮的跟进，希望能够赶上下一个Facebook或者Google的早期投资。现在在纳斯达克泡沫破灭11年后，硅谷再次来到了悬崖的边缘，因为人们的行为几乎都是跟风投机然后就是双手合十希望老天眼向他们开眼给他们好运。技术投资公司Bridgescale Partners的联合创始人Matthew Cowan就表示：“毫无疑问，我们现在正处在泡沫中，而这个泡沫的产生主要来源于社交网络和其他面向消费者的应用。”
似乎的确也总会有人警告泡沫的来临，但是这里的关键是要分清楚什么时候是因为钱太多了，而什么时候不是。不过有些泡沫即便最后令市场上哀鸿遍野，他们也能带来一些好处。比如上个世纪80年代的那场泡沫就造就了微软，Compaq和Intel的崛起并迅速的带动了个人电脑的普及，然后，砰的一声，泡沫在80年代末期破灭了，但是硅谷却留下了大量的廉价微处理器以及如何使用这些微处理器的计算机科学理论。然后人们又开始迷恋网络，只要是与网络有一点关系的东西都去追捧，可是再一次砰的一声，2000年的时候泡沫又破灭了，最后蒸发掉6万亿左右的市值。但是那一次泡沫仍然留给了人们在生活和工作中都将受益无穷的东西—-互联网的基础设施。
而这次，人们的狂热则开始转向更加精确的销售了。比如说在Zynga，工程师们就正在想千方设百计的思考怎样更好的让游戏玩家去参加调查并从口袋中掏出信用卡。而在其他地方，也许就有工程师正在挑灯夜战希望无论转战多少网站也一定要让张三去点一下那个鞋类广告并去消费一笔。
当前的这一疯狂也反应了一个自然进化的结果。人们关注的中心已经由通用性质的技术（工业革命时代是蒸汽动力，信息时代是互联网技术）转向了快速消费产品如iPhone，流媒体视频等。硅谷的一位创业者Steve Perlman（曾经创建WebTV并将其以4.25亿的价格出售给了微软，现在在运营着一个在线视频游戏服务OnLive）就表示：“不论哪个时代，聪明的人都会被吸引到最赚钱的行业里，而现在这个行业就是在线广告。”
如果说这次的科技泡沫是关于怎样去骗取顾客买东西，那么一旦它破灭后会给我们留下什么呢？当听到这个问题的时候，Perlman立刻激动起来了，嗓门也变大了：那些风险投资者们现在都只顾着找到一个能一夜暴富的公司，他们已经没有当年那种投资风险很大却能为更多的创新发明奠定基础的“通用性质技术”项目的勇气了。他说：“Facebook包含的并不是那些能够让我们电话断线后重连的技术，Groupon以及其他类似的靠在线广告为生的公司都不是。”他还进一步解释说：“好吧，我们确实需要这些公司，他们也做得不错，但是他们是构建在过去的旧技术之上的，要知道这些东西就像燃料一样总会燃尽的，到那时你就没有创新的动力了。”
在线房产交易公司Redfin总裁Glenn Kelman甚至表示：“创新动力燃尽的结果将是硅谷变成好莱坞，完全沉浸到一种基于娱乐和流行却无法从根本上增强一国竞争力的商业模式中。”
现在我们再回到天才的哈佛小伙子身上来吧。Hammerbacher在08年的时候从Facebook辞了职，休整了一段时间后和他人共同创建了一家名叫Cloudera的公司，从事数据分析软件的开发。现在他已经28岁了，说话的时候语气中明显的带着硅谷创业家特有的自信和救世激情，这一点在他谈到热点问题时尤其明显。他说：“如果那些公司不是将大量的资源投在怎样更好的让用户去点击广告上，或许我们现在已经用它们解决了许多重大科学问题了。”不过，要是这位大小伙当初没有从Facebook这艘人人都想上去的财富大船上跳下来的话，他现在也一定和其他的广告天才一样。不过这些广告天才可不是你我平常所说的程序员或者工程师，而是一个在数学方面有着极高天赋的群体。
作为这个群体的一员，Hammerbacher是在印第安纳州和密歇根州长大的，父亲是通用汽车公司的一线员工。在他还在读高中的时候，打得一手非常好的棒球，甚至引得密歇根大学和哈佛大学争抢。他说：“我当时的打算是要么成为一位职业棒球员，要么成为一名诗人或者数学家。”而最后他选择了哈佛，选择了数学。不过与他的校友扎克伯格不同的是他并没有选择辍学创业，而是毕业后去了Bear Stearns（贝尔斯登）。
在华尔街，这些数学极客们可是一个被称为船竿的群体，少了他们华尔街大船可能就失去了动力。因为正是他们帮助华尔街创建了那些非常复杂的交易算法，而通过这些算法金融大鳄们则能在输入大量的市场数据后瞬间就做出买与卖的决定。Hammerbacher在这里做了10个月左右就离开了，后来他联系上了扎克伯格，得到了我们最初提到的那个研究科学家的职位。也正是从那一刻起，Hammerbacher走入了消费技术行业，并开始用自己的数学知识为这个行业的精确广告贡献自己的一份力。
在社交网络公司里，数学极客们也许是和计算机科学家或者工程师们坐在一起办公，但是他们的任务却和其他人有重大的区别：他们主要是要去挖掘数据，研究趋势以及写出最好的公式以便使公司能将合适的广告展示给合适的用户。Redfin的总裁Kelman就说：“现在在硅谷，争夺最激烈的人才并不是软件工程师，而是数学天才，因为只有这些极客们才有本事抓住用户的心让他们多点几个广告。”
比如说，社交游戏巨头Zynga大约每天就要收集600亿个数据点，包括人们一般玩多久游戏，什么时候玩，喜欢购买什么游戏物品等等。该公司的数学极客们然后又用这些数据来分析哪些人喜欢逛自己的朋友的农场和城市（Zynga开发的游戏），人们都喜欢买哪些虚拟物品以及他们给自己的朋友赠送虚拟物品的频率等。然后他们就会得出这样一个重大的发现：经常收到朋友虚拟礼物的人会更喜欢玩游戏，收不到或不那么经常收到的则不太喜欢玩游戏。Zynga的数据分析部门副总裁Ken Rudin表示：根据这个发现，一群数学极客们又想出了解决办法—–那些不那么经常收到礼物的玩家我们会让他们更加容易的找到建城（Zynga游戏）的工具，这样他们就不会过于依靠他人的礼物了。
这些社交网络公司基于用户消费目的的运营行动看起来和华尔街的交易算法系统很像。比如说，一个数学天才会开发出一个在线系统，通过这个系统他能追踪用户的搜索习惯，邮件内容以及经常浏览的网站等，这不和华尔街的输入数据以及瞬间产生决定是一样的吗？一家名叫Flite的在线广告公司CEO Will Price就说：“通过该系统你能得到所有需要的数据，然后通过这些数据创建出一个能迅速生成决定的模型。”然后，他又进一步解释到：“你甚至需要在网页载入完成之前就执行完所有的计算并作出决定。”
不过广告技术公司也能很好的满足用户的某些需求，甚至在许多情况下，他们也还能为用户提供非常有价值的服务。比如说Google就为用户打开了一扇通往互联网世界各种信息的大门，并且提供了免费的地图服务，办公软件，智能手机软件等等。另外它还将自己的部分广告收益投入到了一些工程项目比如自动汽车，登月机器人等的研究中去。而对于那些数学天才们来说，在线广告时代也能让他们受益，可以开发其另一半大脑，并促进他们学习市场，营销等方面的知识，甚至走上完全不同的职业道路。最后，在这个过程中投资者也是受益的，至少在账面上，比如说那些冲入云霄的估值。此外Nyppex的数据表明：对于Zynga这家公司来说，自去年第4季度以来，它的估值就激增了81%，达到了80亿美元（狗临天下）。
当然，没有人说那些处于第一阶梯的以广告为中心的公司比如Facebook，Google等将会因为泡沫的破灭而倒闭。但是那些处于第2阶梯或者第3阶梯的社交网络公司或者广告公司的命运就很难说了。
比如说今年3月份以巨额融资浮出水面的Color就是这样一家带着社交网络色彩的创业公司。通过该公司开发的同名应用Color，人们可以上传并存储图片，更加重要的是该应用采用的地理定位技术和环境噪点技术能够根据用户上传的图片分析出用户拍照的地点，然后自动将这些照片和附近位置的人分享。比如说张三在参加一个生日聚会的话，他就能看到这个生日聚会上其他所有人上传的照片，在这里照片成了关键中的关键。 不过现在还很难说Color以后会不会实现盈利。即便其高级管理人员也表示公司可能会从本地化广告这一模式切入，其盈利能力还仍需以后观察。而现在却并不令人意外的则是该公司的首席产品官DJ Patil是一位数学天才，因为其要负责构建起一整套能够对那些所有基于手机的信息更新进行好地理分析的具有强大的计算能力的分析工具。
Patil来自印度，父亲Suhas Patil从印度移民美国后成立了一家名叫Cirrus Logic的芯片公司。有趣的是小Patil在读高中的时候成绩都还不怎么好，而且还去过一个二流大学念过一段时间，直到最后才坚定了自己的意志一定要在数学领域里闯出来。后来他去了加州大学，在那里更是想尽一切办法出现在每一堂数学课上。经过刻苦学习，他终于成为了一名理论数学专家，然后又去从事了天气变化，沙丁鱼数量减少，沙丘的形成原因等方面的研究，甚至还有一段时间，他为国家防务部门进行过中亚生化武器监测的研究。他说：“我以前所做的所有事都是关于怎样去利用数学知识从事意义更加广泛的研究。”直到后来硅谷以高薪将他招了进来，首先是在eBay呆过一段时间，在那里主要是防欺诈系统的开发。他说：我做这个系统的时候还借鉴了生化武器威胁监测项目的一些点子，其实也就是关于你如何从网络和社交联系中确认出人们的好坏。
而现在Pati对于自己跳离当初的人生轨迹转而投向硅谷的社交网络公司开始感到痛苦起来。他说：“在生命中有一段时间你可以很容易的去从事那些有意义的大项目，但是有时候你却没办法，不然的话你要怎么去养活自己的家庭和孩子呢？”
而现在经过自我审视后的Hammerbacher也并不羡慕像Patil这样的数学极客在社交网络公司里的生活，因为他自己也曾是这其中的一员。事实上他们俩还是朋友，也经常会聚在一起讨论数据和处理大量数据的挑战等问题。Hammerbacher说在社交网络公司“有些人真的以为自己是在从事大事业—–沟通人们。我也不觉得这些人有什么问题，只是无论怎样这项事业都不能引起我的共鸣。” 在08年出走Facebook后，Hammerbacher对科学和商业版图做了一个调查，发现所有的公司和机构都面临着他当初为之服务的消费网络公司所面临的相同问题——怎样从海量的信息中获得一些有价值的东西（Vivek Wadhwa：我们已进入一个新信息时代），比如生物公司每天都在制造大量的DNA测序数据，能源公司也能得到大量的地震波数据等。也正是这个时候，Hammerbacher和创业伙伴开始意识到他们可以将自己之前为网络公司开发的分析工具用于一个完全不同的方向，也就是助力研究人员的事业，为他们提供现代分析工具。
事实上，Hammerbacher现在的公司Cloudera所从事的事业是要开发出一种能够分析大量的数据的操作系统à la Windows，这其中Windows可以管理PC的基本功能和软件，而Cloudera的技术则帮助公司将大量的数据打散成相对容易消化并能分布在廉价计算机上的的数据块，通过这种模式顾客就能迅速的提出问题并获得答案了。但是同在Facebook上问一群朋友“最喜欢什么？”完全不同的是这里的问题是“那些癌症病人都有哪些相同的基因？”
基因测序设备生产厂商Pacific Biosciences的首席研究员Eric Schadt就表示，新药物的发现和癌症的治疗都要依靠尖端分析工具，而那些使用他们公司设备的客户每天都会获得大量的基因测序数据。如果没有尖端的分析工具的支持，他们“将不同的基因，器官等各方面的生命系统的关系图都绘制出来，然后进一步研究这些关联是怎样导致疾病的出现，又怎样才能治愈”的目标很难实现。这些研究人员在开发这样的工具时遇到了困难，现在正在寻求硅谷的帮助。Schadt就表示：今后引领生物制药前进的将不再是那些老学院派的生物学家们了，而是像Jeff Hammerbacher这样懂得处理大量数据的数学天才们。
不过在线广告公司Flite的总裁Will Price对此有不同的看法。他认为就算Cloudera无法为癌症找到治疗方案，无法让硅谷摆脱广告至上主义或者无法说服这一代极客们去从事到商业软件的开发项目中去，整个技术产业仍然不会丢失它过去的那股创业精神。他表示：“你们可以去尽情的鄙视Zynga，但是你们否认不了它正在大量挣钱的事实。”此外他还进一步解释说：“硅谷最伟大的地方之一就是那些天才们能够来到这里进行高强度的锻炼，弄明白怎样才能创建好一个公司以及怎样去冒险等。很多人来到Google和Facebook后又离开去自己创业，有继续从事广告系统的，也有转到机器工程和内容发布领域的，反正这里就几乎就像一个永动机一样。”
永动机确实是个好东西，只不过它并不存在。到现在为止，这些网络公司出来的极客们也未能成功的进一步推动其他产业的发展。一位在研究硅谷经济史上颇有建树的学者Christophe Lécuyer就表示：“很明显这些基于互联网在线广告的公司和服务并没有创造出许多的工作岗位，反而导致了工业设计技能的逐步消逝，这一点非常令人担忧。”
再将时间拨回到25年前的那一次技术泡沫，也就是86年的时候，微软，甲骨文以及Sun Microsystems纷纷上市。Compaq更是在创建后只用了短短的4年时间就成长为了一家财富500强企业（当时史上成长最快的一家企业）。在泡沫破灭的时候，这些公司也都受到了沉重的打击，但是他们所开发出来的技术却成就了其他技术的发展。而现在呢？成长最快的要数Groupon了，这家公司是做什么的？每天通过电子邮件向用户发送电子优惠券。据预测，其收入今年可能达到40亿美元（去年仅为7.5亿美元），当前估值也已经达到了250亿美元。但是你想想，泡沫破灭后这家公司会给我们留下什么呢？恐怕只有那些好看且可爱的邮件吧。
回顾历史，你总会发现在基础性技术上面会再出现其他许多耀眼的衍生技术。有时这个周期的明星公司也会在下一个周期变成具有核心技术的公司，比如Amazon就是一个很好的例子，在上一次泡沫前其还只是一个纯粹的电子商务公司，但是10年过后它现在更成为了一个云计算技术的输出者。但是我们当前的这次泡沫是不是偏离轨道太远了呢？技术投资公司Bridgescale Partners的联合创始人Cowan就说到：“可以很安全的说，在未来20个月的某个时间里，资本市场会关闭，音乐会停止，世界也将再次被笼罩在一片凄凉之中。而现在一个非常合理的担忧便是我们或许会因为偏离轨道太远，在进入到这个周期的下一部分时可能会找不到可以依靠的技术之根了。”（完）
Phillip Toledano
By Ashlee Vance
As a 23-year-old math genius one year out of Harvard, Jeff Hammerbacher arrived at Facebook when the company was still in its infancy. This was in April 2006, and Mark Zuckerberg gave Hammerbacher—one of Facebook’s first 100 employees—the lofty title of research scientist and put him to work analyzing how people used the social networking service. Specifically, he was given the assignment of uncovering why Facebook took off at some universities and flopped at others. The company also wanted to track differences in behavior between high-school-age kids and older, drunker college students. “I was there to answer these high-level questions, and they really didn’t have any tools to do that yet,” he says.
Over the next two years, Hammerbacher assembled a team to build a new class of analytical technology. His crew gathered huge volumes of data, pored over it, and learned much about people’s relationships, tendencies, and desires. Facebook has since turned these insights into precision advertising, the foundation of its business. It offers companies access to a captive pool of people who have effectively volunteered to have their actions monitored like so many lab rats. The hope—as signified by Facebook’s value, now at $65 billion according to research firm Nyppex—is that more data translate into better ads and higher sales.
After a couple years at Facebook, Hammerbacher grew restless. He figured that much of the groundbreaking computer science had been done. Something else gnawed at him. Hammerbacher looked around Silicon Valley at companies like his own, Google (GOOG), and Twitter, and saw his peers wasting their talents. “The best minds of my generation are thinking about how to make people click ads,” he says. “That sucks.”
You might say Hammerbacher is a conscientious objector to the ad-based business model and marketing-driven culture that now permeates tech. Online ads have been around since the dawn of the Web, but only in recent years have they become the rapturous life dream of Silicon Valley. Arriving on the heels of Facebook have been blockbusters such as the game maker Zynga and coupon peddler Groupon. These companies have engaged in a frenetic, costly war to hire the best executives and engineers they can find. Investors have joined in, throwing money at the Web stars and sending valuations into the stratosphere. Inevitably, copycats have arrived, and investors are pushing and shoving to get in early on that action, too. Once again, 11 years after the dot-com-era peak of the Nasdaq, Silicon Valley is reaching the saturation point with business plans that hinge on crossed fingers as much as anything else. “We are certainly in another bubble,” says Matthew Cowan, co-founder of the tech investment firm Bridgescale Partners. “And it’s being driven by social media and consumer-oriented applications.”
There’s always someone out there crying bubble, it seems; the trick is figuring out when it’s easy money—and when it’s a shell game. Some bubbles actually do some good, even if they don’t end happily. In the 1980s, the rise of Microsoft (MSFT), Compaq (HPQ), and Intel (INTC) pushed personal computers into millions of businesses and homes—and the stocks of those companies soared. Tech stumbled in the late 1980s, and the Valley was left with lots of cheap microprocessors and theories on what to do with them. The dot-com boom was built on infatuation with anything Web-related. Then the correction began in early 2000, eventually vaporizing about $6 trillion in shareholder value. But that cycle, too, left behind an Internet infrastructure that has come to benefit businesses and consumers.
This time, the hype centers on more precise ways to sell. At Zynga, they’re mastering the art of coaxing game players to take surveys and snatch up credit-card deals. Elsewhere, engineers burn the midnight oil making sure that a shoe ad follows a consumer from Web site to Web site until the person finally cracks and buys some new kicks.
This latest craze reflects a natural evolution. A focus on what economists call general-purpose technology—steam power, the Internet router—has given way to interest in consumer products such as iPhones and streaming movies. “Any generation of smart people will be drawn to where the money is, and right now it’s the ad generation,” says Steve Perlman, a Silicon Valley entrepreneur who once sold WebTV to Microsoft for $425 million and is now running OnLive, an online video game service. “There is a goodness to it in that people are building on the underpinnings laid by other people.”
So if this tech bubble is about getting shoppers to buy, what’s left if and when it pops? Perlman grows agitated when asked that question. Hands waving and voice rising, he says that venture capitalists have become consumed with finding overnight sensations. They’ve pulled away from funding risky projects that create more of those general-purpose technologies—inventions that lay the foundation for more invention. “Facebook is not the kind of technology that will stop us from having dropped cell phone calls, and neither is Groupon or any of these advertising things,” he says. “We need them. O.K., great. But they are building on top of old technology, and at some point you exhaust the fuel of the underpinnings.”
And if that fuel of innovation is exhausted? “My fear is that Silicon Valley has become more like Hollywood,” says Glenn Kelman, chief executive officer of online real estate brokerage Redfin, who has been a software executive for 20 years. “An entertainment-oriented, hit-driven business that doesn’t fundamentally increase American competitiveness.”
Hammerbacher quit Facebook in 2008, took some time off, and then co-founded Cloudera, a data-analysis software startup. He’s 28 now and speaks with the classic Silicon Valley blend of preternatural self-assurance and save-the-worldism, especially when he gets going on tech’s hottest properties. “If instead of pointing their incredible infrastructure at making people click on ads,” he likes to ask, “they pointed it at great unsolved problems in science, how would the world be different today?” And yet, other than the fact that he bailed from a sweet, pre-IPO gig at the hottest ad-driven tech company of them all, Hammerbacher typifies the new breed of Silicon Valley advertising whiz kid. He’s not really a programmer or an engineer; he’s mostly just really, really good at math.
Hammerbacher grew up in Indiana and Michigan, the son of a General Motors (GM) assembly-line worker. As a teenager, he perfected his curve ball to the point that college scouts from the University of Michigan and Harvard fought for his services. “I was either going to be a baseball player, a poet, or a mathematician,” he says. Hammerbacher went with math and Harvard. Unlike one of his more prominent Harvard acquaintances—Facebook co-founder Mark Zuckerberg—Hammerbacher graduated. He took a job at Bear Stearns.
On Wall Street, the math geeks are known as quants. They’re the ones who create sophisticated trading algorithms that can ingest vast amounts of market data and then form buy and sell decisions in milliseconds. Hammerbacher was a quant. After about 10 months, he got back in touch with Zuckerberg, who offered him the Facebook job in California. That’s when Hammerbacher redirected his quant proclivities toward consumer technology. He became, as it were, a Want.
At social networking companies, Wants may sit among the computer scientists and engineers, but theirs is the central mission: to poke around in data, hunt for trends, and figure out formulas that will put the right ad in front of the right person. Wants gauge the personality types of customers, measure their desire for certain products, and discern what will motivate people to act on ads. “The most coveted employee in Silicon Valley today is not a software engineer. It is a mathematician,” says Kelman, the Redfin CEO. “The mathematicians are trying to tickle your fancy long enough to see one more ad.”
Sometimes the objective is simply to turn people on. Zynga, the maker of popular Facebook games such as CityVille and FarmVille, collects 60 billion data points per day—how long people play games, when they play them, what they’re buying, and so forth. The Wants (Zynga’s term is “data ninjas”) troll this information to figure out which people like to visit their friends’ farms and cities, the most popular items people buy, and how often people send notes to their friends. Discovery: People enjoy the games more if they receive gifts from their friends, such as the virtual wood and nails needed to build a digital barn. As for the poor folks without many friends who aren’t having as much fun, the Wants came up with a solution. “We made it easier for those players to find the parts elsewhere in the game, so they relied less on receiving the items as gifts,” says Ken Rudin, Zynga’s vice-president for analytics.
These consumer-targeting operations look a lot like what quants do on Wall Street. A Want system, for example, might watch what someone searches for on Google, what they write about in Gmail, and the websites they visit. “You get all this data and then build very rapid decision-making models based on their history and commercial intent,” says Will Price, CEO of Flite, an online ad service. “You have to make all of those calculations before the Web page loads.”
Ultimately, ad-tech companies are giving consumers what they desire and, in many cases, providing valuable services. Google delivers free access to much of the world’s information along with free maps, office software, and smartphone software. It also takes profits from ads and directs them toward tough engineering projects like building cars that can drive themselves and sending robots to the moon. The Era of Ads also gives the Wants something they yearn for: a ticket out of Nerdsville. “It lets people that are left- brain leaning expand their career opportunities,” says Doug Mack, CEO of One Kings Lane, a daily deal site that specializes in designer goods. “People that might have been in engineering can go into marketing, business development, and even sales. They can get on the leadership track.” And while the Wants plumb the depths of the consumer mind and advance their own careers, investors are getting something too, at least on paper: almost unimaginable valuations. Just since the fourth quarter, Zynga has risen 81 percent in value, to a cool $8 billion, according to Nyppex.
No one is suggesting that the top tier of ad-centric companies—Facebook, Google—is going down should the bubble pop. As for the next tier or two down, where a profusion of startups is piling into every possible niche involving social networking and ads—the fate of those companies is anybody’s guess. Among the many unveilings in March, one stood out: An app called Color, made by a seven-month-old startup of the same name. Color lets people take and store their pictures. More than that, it uses geolocation and ambient-noise-matching technology to figure out where a person is and then automatically shares his photos with other nearby people and vice versa. People at a concert, for example, could see photos taken by all the other people at that concert. The same goes for birthday parties, sporting events, or a night out at a bar. The app also shares photos among your friends in the Color social network, so you can see how Jane is spending her vacation or what John ate for breakfast, if he bothered to take a photo of it.
Whether Color ends up as a profitable app remains to be seen. The company has yet to settle on a business model, although its executives say it’ll probably incorporate some form of local advertising. Figuring out all those location-based news feeds on the fly requires serious computational power, and that part of the business is headed by Color’s math wizard and chief product officer, DJ Patil.
Patil’s Silicon Valley pedigree is impeccable. His father, Suhas Patil, emigrated from India and founded the chip company Cirrus Logic (CRUS). DJ struggled in high school, did some time at a junior college, and through force of will decided to get good at math. He made it into the University of California at San Diego, where he took every math course he could. He became a theoretical math guru and went on to research weather patterns, the collapse of sardine populations, the formation of sand dunes, and, during a stint for the Defense Dept., the detection of biological weapons in Central Asia. “All of these things were about how to use science and math to achieve these broader means,” Patil says. Eventually, Silicon Valley lured him back. He went to work for eBay (EBAY), creating an antifraud system for the retail site. “I took ideas from the bioweapons threat anticipation project,” he says. “It’s all about looking at a network and your social interactions to find out if you’re good or bad.”
Patil, 36, agonized about his jump away from the one true path of Silicon Valley righteousness, doing gritty research worthy of his father’s generation. “There is a time in life where that kind of work is easy to do and a time when it’s hard to do,” he says. “With a kid and a family, it was getting hard.”
Having gone through a similar self-inquiry, Hammerbacher doesn’t begrudge talented technologists like Patil for plying their trade in the glitzy land of networked photo sharing. The two are friends, in fact; they’ve gotten together to talk about data and the challenges in parsing vast quantities of it. At social networking companies, Hammerbacher says, “there are some people that just really buy the mission—connecting people. I don’t think there is anything wrong with those people. But it just didn’t resonate with me.”
After quitting Facebook in 2008, Hammerbacher surveyed the science and business landscape and saw that all types of organizations were running into similar problems faced by consumer Web companies. They were producing unprecedented amounts of information—DNA sequences, seismic data for energy companies, sales information—and struggling to find ways to pull insights out of the data. Hammerbacher and his fellow Cloudera founders figured they could redirect the analytical tools created by Web companies to a new pursuit, namely bringing researchers and businesses into the modern age.
Cloudera is essentially trying to build a type of operating system, à la Windows, for examining huge stockpiles of information. Where Windows manages the basic functions of a PC and its software, Cloudera’s technology helps companies break data into digestible chunks that can be spread across relatively cheap computers. Customers can then pose rapid-fire questions and receive answers. But instead of asking what a group of friends “like” the most on Facebook, the customers ask questions such as, “What gene do all these cancer patients share?”
Eric Schadt, the chief scientific officer at Pacific Biosciences, a maker of genome sequencing machines, says new-drug discovery and cancer cures depend on analytical tools. Companies using Pacific Bio’s machines will produce mountains of information every day as they sequence more and more people. Their goal: to map the complex interactions among genes, organs, and other body systems and raise questions about how the interactions result in certain illnesses—and cures. The scientists have struggled to build the analytical tools needed to perform this work and are looking to Silicon Valley for help. “It won’t be old school biologists that drive the next leaps in pharma,” says Schadt. “It will be guys like Jeff who understand what to do with big data.”
Even if Cloudera doesn’t find a cure for cancer, rid Silicon Valley of ad-think, and persuade a generation of brainiacs to embrace the adventure that is business software, Price argues, the tech industry will have the same entrepreneurial fervor of yesteryear. “You can make a lot of jokes about Zynga and playing FarmVille, but they are generating billions of dollars,” the Flite CEO says. “The greatest thing about the Valley is that people come and work in these super-intense, high-pressure environments and see what it takes to create a business and take risk.” A parade of employees has left Google and Facebook to start their own companies, dabbling in everything from more ad systems to robotics and publishing. “It’s almost a perpetual-motion machine,” Price says.
Perpetual-motion machines sound great until you remember that they don’t exist. So far, the Wants have failed to carry the rest of the industry toward higher ground. “It’s clear that the new industry that is building around Internet advertising and these other services doesn’t create that many jobs,” says Christophe Lécuyer, a historian who has written numerous books about Silicon Valley’s economic history. “The loss of manufacturing and design knowhow is truly worrisome.”
Dial back the clock 25 years to an earlier tech boom. In 1986, Microsoft, Oracle (ORCL), and Sun Microsystems went public. Compaq went from launch to the Fortune 500 in four years—the quickest run in history. Each of those companies has waxed and waned, yet all helped build technology that begat other technologies. And now? Groupon, which e-mails coupons to people, may be the fastest-growing company of all time. Its revenue could hit $4 billion this year, up from $750 million last year, and the startup has reached a valuation of $25 billion. Its technological legacy is cute e-mail.
There have always been foundational technologies and flashier derivatives built atop them. Sometimes one cycle’s glamour company becomes the next one’s hard-core technology company; witness Amazon.com’s (AMZN) transformation over the past decade from mere e-commerce powerhouse to e-commerce powerhouse and purveyor of cloud-computing capabilities to other companies. Has the pendulum swung too far? “It’s a safe bet that sometime in the next 20 months, the capital markets will close, the music will stop, and the world will look bleak again,” says Bridgescale Partners’ Cowan. “The legitimate concern here is that we are not diversifying, so that we have roots to fall back on when we enter a different part of the cycle.”
Vance is a technology writer for Bloomberg Businessweek.