The Economist UK - 07.09.2019

The EconomistSeptember 7th 2019 57

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S

omebody lessdriven than Tom Siebel

would have long since thrown in the

towel. In 2006 the entrepreneur, then 53

years old, sold his first firm, Siebel Sys-

tems, which made computer programs to

track customer relations, to Oracle, a giant

of business software. That left him a bil-

lionaire—but a restless one. In 2009, a few

months after Mr Siebel had launched a new

startup, he was trampled by an elephant

while on safari in Tanzania. When, a dozen

surgeries later, he could work again, the

enterprise almost went bankrupt. Unde-

terred, he rebooted it.

Mr Siebel’s fortitude has paid off. The

firm, now called c3.ai, raised $100m in ven-

ture capital last year, valuing it at $2.1bn. It

was an early bet on data analytics, which

converts raw data (from a machine’s sen-

sors or a warehouse) into useful predic-

tions (when equipment will fail or what the

optimal stocking levels are) with the help

of clever algorithms. Many investors see

fortunes to be made from this new breed of

enterprise software, which is spreading

from Big Tech’s computer labs to corpora-

tions everywhere.

Worldwide, 35 companies that dabble in

data analytics feature on a list of startups

valued at $1bn or more, maintained by cb

Insights, a research firm. Collectively,

these unicorns—some of which brand

themselves as purveyors of artificial intel-

ligence (ai)—enjoy a heady valuation of

$73bn. According to PitchBook, another re-

search company, the six biggest alone are

worth $45bn (see chart 1 on next page).

Many venture capitalists who back them

are hoping to emulate the successful initial

public offerings this year of less exalted

business-services startups like Crowd-

Strike, which provides cybersecurity, or

Zoom, a video-conferencing company. And

then some.

As is often the case in Silicon Valley,

hype springs eternal, fuelled by big num-

bers from consultancies. idcreckons that

spending on big-data and business-analyt-

ics software will reach $67bn this year. But

it will, boosters say, at last allow businesses

to see the computer age in their productivi-

ty statistics, freeing them from the shadow

of Robert Solow, a Nobel-prizewinning

economist, who in 1987 observed that in-

vestment in information technology ap-

peared to do little to make companies more

efficient. Just as electricity enabled the as-

sembly line in the 19th century, since ma-

chines no longer had to be grouped around

a central steam engine, data-analytics

companies promise to usher in the assem-

bly lines of the digital economy, distribut-

ing data-crunching capacity where it is

needed. They may also, as George Gilbert, a

veteran business-itanalyst, observes, help

all kinds of firm create the same network

effects behind the rise of the tech giants:

the better they serve their customers, the

more data they collect, which in turn im-

proves their services, and so on.

Consultants at Gartner recently calcu-

lated that in 2021 “ai augmentation” will

create $2.9trn of “business value” and save

Information technology

The digital assembly line

REDWOOD CITY AND SAN FRANCISCO

Technology firms vie for billions in corporate data-analytics contracts

Business

58 German business and populists

59 Bartleby: Retirement postponed

60 Samsung’s prodigal son

60 Chinese netizens’ privacy fears

61 High-tech fitness

62 Schumpeter: Popenomics

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