You have multiple jobs, whether you know it or not. Most begin first thing in the morning, when you pick up your phone and begin generating the data that make up Silicon Valley's most important resource. That, at least, is how we ought to think about the role of data-creation in the economy, according to a fascinating（adj.办迷人的；吸引人的）new economics paper. If the economy is to function properly in the future一and if a crisis of technological unemployment is to be avoided—we must take account of this, and change the relationship between big internet companies and their users.
Artificial intelligence (AI) is getting better all the time, and stands poised（ poise v.(使)平衡；（使)悬着）to r transform a host of industries, say the authors. But, in order to learn to drive a car or recognise a face, the algorithms（ algorithm n.算法，运算法则）that make clever machines tick must usually be trained on massive amounts of data. Internet firms gather these data from users every time they click on a Google search result，say, or issue a command to Alexa. They also hoover up（获得大量的；吸收）valuable data from users through the use of tools like reCAPTCHA, which ask visitors to solve problems that are easy for humans but hard for AIs, such as deciphering（decipher vt.解释;译解）text from books that machines are unable to parse. People “pay” for useful free services by providing firms with the data they crave（vt.渴望;恳求）.
These data become part of the firms’ capital，and，as such，a fearsome（adj.可怕的；害怕的）source of competitive advantage. Would-be startups that might challenge internet giants cannot train their AIs without access to the data only those giants possess. Their best hope is often to be acquired by those very same titans, adding to the problem of uncompetitive markets. That, for now, AI's contributions to productivity growth are small, the authors say, is partly because of the free-data model, which limits the quality of data gathered. Firms trying to develop useful applications for AI must hope that the data they have are sufficient, or come up with ways to coax（vt.哄；哄诱）users into providing them with better information at no cost.
To tackle these problems, they have a radical（adj.激进的）proposal. Rather than being regarded capital, data should be treated as labour—and, more specifically, regarded as the property of those who generate such information, unless they agree to provide it to firms in exchange for payment. In such a world, user data might be sold multiple times, to multiple firms, reducing the extent to which data sets serve as barriers to entry. Payments to users for their data would help spread the wealth generated by AI. Firms could also potentially generate better data by paying.
The authors9 ideas need fleshing out（flesh out充实，具体化）；their paper, thought-provoking though it is, runs to only five pages. Parts of the envisioned scheme seem impractical（adj.不切实际的，不现实的）. Would people really be interested in taking the time to describe their morning routine or office habits without a substantial monetary inducement（n.诱因,刺激物）( and would their data be valuable enough for firms to pay a substantial amount)? Might not such systems attract data mercenaries, spamming firms with useless junk data simply to make a quick?
Nothing to use but your brains
Still, the paper contains essential insights which should frame discussion of data’s role in the economy. One concerns the imbalance of power in the market for data. That stems partly from concentration among big internet firms. But it is also because, though data may be extremely valuable in aggregate, an individual^ personal data typically are not. So effective negotiation with internet firms might require collective action: and the formation，perhaps, of a “ data-labour union”.This might have drawbacks. It might make all user data freely available and extract compensation by demanding a share of firms，profits; that would rule out the pay-for-data labour model the authors see as vital to improving data quality.
Most important, the authors’ proposal puts front and centre the collective nature of value in an AI world. Each person becomes something like an oil well，pumping out the fuel that makes the digital economy run. Both fairness and efficiency demand that the distribution of income generated by that fuel should be shared more evenly, according to our contributions. The tricky （adj.狡猾的;机警的）part is working out how.