Economist: Why Japan lost the mobile-phone wars

Science & Technology

Tech.view

Dropped call

Why Japan lost the mobile-phone wars

Mar 7th 2008 | From The Economist online

JAPAN is probably the world’s most important source of technology equipment. Yes, America dominates the high-end chip market with Intel, AMD and Texas Instruments. It also dominates software with Microsoft, Oracle, IBM and a bevy of open-source code products. Taiwan is a powerhouse, China is obviously vital, and Europe boasts a few stars.

But the components that power the IT industry are developed in Japan. Crack open Apple’s iPod, for instance, and the two most expensive bits are the hard drive (from Toshiba, at around $70) and screen (by Toshiba-Matsushita, at $20).

AFPCorporate pride went before the fall

Japan is also the mobile society’s social pioneer. The country created the wireless-data business model with NTT DoCoMo’s iMode service in 2000. Back then, Europeans were preoccupied with wireless-license auctions, Americans lugged around mobiles the size of bricks and Africa was not yet on the grid.

Today, around 25% of Japanese access the internet only from their mobiles. Operators’ revenue tops $90 billion a year. Many customers upgrade their phone every year; last year almost 50m new mobiles were sold. The market is saturated, with 100m subscribers in a country of 130m.

Despite this apparent success, Japan is actually losing the global mobile-phone market. Among the top seven manufacturers, only one is Japanese—Sony Ericsson, at fourth place (and that’s a joint venture with a Swedish firm).

The market leader in Japan is Sharp, but it only ranks eighth in the world, with one-fortieth the sales of Nokia, the top company. In recent years, the Japanese have watched as its South Korean rivals, Samsung and LG, have crept up the league tables to second and fifth place.

How did companies with such a promising beginning fare so badly? Four reasons stand out.

First, too many Japanese companies make phones. All major electronics firms sell them, mainly as a matter of corporate pride: not to do so would be a sign of weakness. As a result, 11 different domestic makers compete, most of them at a loss.

Consolidation is starting to emerge—in December Sanyo Electric sold its mobile phone business to Kyocera, and just this week, Mitsubishi Electric said it was exiting the market. Still, a surplus of makers persists.

Second, Japanese manufacturers concentrated on the domestic market at the expense of global growth. Yet the national business model is unique: mobile operators design the features of the phone, and the manufacturers must comply.

So the makers do not have a good understanding of what users want—they only know what the operators demand. And the makers have little experience in selling to foreign operators. Some firms, such as Toshiba, Panasonic and NEC, tried to expand overseas. Kyocera tested the Chinese market. But all got beaten back.

Third, the manufacturers designed products around home-grown technical standards and special features that are not used elsewhere. (Most foreign visitors to Japan grumble upon discovering that their phones are incompatible with the country’s wireless networks.) So the Japanese makers can only build phones tailored to the overseas market at a high cost and lower margins, making it an unattractive business to enter.

Fourth, high-end customers who want sophisticated phones drive the Japanese market, but the main growth in the wireless industry overall is in emerging markets, which need cheap phones. The world’s top three makers—Nokia, Samsung and Motorola—focus on this segment.

This actually hurts Japanese makers in subtler way than just a missed market opportunity. In the tech-hardware business, competition over price is really a function of scale: firms with higher volume can make things more cheaply. So Japanese firms are caught in a vicious circle: because they are not selling to poor countries, their volume stays low, which keeps prices high, which makes selling to poor countries infeasible.

Japanese mobile makers face other difficulties as well. Their excellence in hardware is not matched in software and usability, areas in which North American firms excel (think of gadgets like Apple’s iPhone and RIM’s BlackBerry). And the West’s “smart phones” tend to have small keyboards, while Japanese kanji characters require just a numberpad. So for cultural reasons, Japanese makers are at a disadvantage when selling to Western markets.

Consequently, Japanese electronics manufacturers lost the ripest global technology-hardware market of the past decade, and perhaps the next decade as well. And now even their home market is less generous. Mobile-phone sales last year fell by around 15% and are expected to decline a further 20% in 2008. Operators are changing their billing plans, encouraging customers to pay more for phones and keep them longer, rather than subsidising the cost of frequent upgrades.

Moreover, foreign brands like Samsung have a healthy presence, and Apple’s iPhone is coming. So business is getting tougher at a time when much more R&D investment is required to be competitive.

But the biggest problem holding back Japanese firms are the companies themselves: their corporate pride. Until the electronics makers are willing to prune their businesses to concentrate on a few targeted areas rather than stock a bit of everything on their shelves, they will continue to flounder as the world passes them by.

Public versus Private Equity

Public v private equity

The business of making money

Private equity's strengths and its increasingly apparent weaknesses

Daniel Mackie

Jul 5th 2007 | From The Economist print edition

BACK in the late 1980s, the Financial Times carried a spoof story about a planned buy-out of General Motors. Nowadays the sale of such a giant would not be regarded as a joke. Every day yet another company seems to succumb to the clutches of private equity. And this week saw what could be the biggest deal ever: a $48.5 billion offer by a consortium of investors for BCE, a Canadian telecoms group. It was swiftly followed by a potential $22 billion bid for Virgin Media, a British cable-television company, and the $26 billion purchase of Hilton Hotels.

Even after those deals, the private-equity titans have plenty of firepower left. According to Private Equity Intelligence, a research group, the industry raised $240 billion in the first half of this year, leaving it well placed to surpass last year's record of $459 billion. That compares with less than $10 billion raised in 1991. In the process, private equity's share of mergers and acquisitions has grown massively (see chart).

Private equity has become a byword for money-making skills. “Why are we here attending conferences when we should be setting up private-equity firms?” quipped Niall Ferguson, a historian, at a conference held at the London Business School on July 2nd. But the industry's wealth has also made it plenty of enemies, with trade unions and left-wing politicians calling for curbs on its activities and higher taxes on its earnings.

The intellectual argument in favour of private equity has not changed much in 20 years. In 1989 Michael Jensen, of the Harvard Business School, wrote a paper* suggesting the public company had outlived its usefulness. Economic developments, in particular the recession of the early 1990s, made that forecast seem premature. But its underlying arguments have more force today.

Public tedium

Life is no longer much fun in a publicly quoted company. Executives have to suffer the slings and arrows of intrusive media coverage, the oppressive tedium of “box-ticking” corporate-governance codes, the threats of activist investors and short sellers, and the scrutiny of single-minded political campaigners.

And what do companies get in return? Traditionally they have had three main reasons to list their shares on a stockmarket. The first is to raise capital, either to expand the business or to allow the founders to realise their wealth. The second is to help retain staff, who can be offered share options as an incentive to stay and work hard. The third involves prestige; customers, suppliers and potential employees may be reassured (and attracted) by the apparent seal of approval given by a public listing. However, all three reasons seem to be less compelling than they used to be.

Historically companies have got their equity capital from four sources: pension funds, insurance companies, mutual funds and retail investors. The first three groups faced legal or regulatory impediments to buying unquoted shares, while the public naturally valued the liquidity a stockmarket listing could bring.

In the absence of a public quote, companies often had only one financial alternative: the banks. In some areas of the world this worked quite well. Banks were reliable partners to Germany's Mittelstand of unquoted companies and to Japan's industrial empires. But in the Anglo-Saxon economies companies often felt nervous about being in hock to the banks. A change in lending policy, due to new management or an economic downturn, could lead to the sudden withdrawal of credit.

Nowadays companies have many more options when it comes to raising money. Banks are much less important as a source of lending; they have been “disintermediated” by capital markets. Banks might arrange loans, but they quickly offload them to outside investors such as hedge funds. Bond markets are much more liquid than they used to be, and thanks to high-yield products even companies with a poor credit-rating can tap them.

Then, of course, there is private equity. It can provide finance at an early stage (venture capital) or as an attractive alternative for companies that have a public quote (the leveraged buy-out). Whereas pension funds will be reluctant to hold a direct stake in an unquoted company, they are willing to pay hefty fees to private-equity firms to invest money on their behalf.

So companies have no difficulty in finding capital outside the public market these days. Just as importantly, in recent years they have had little need to raise capital at all. Corporate profits have risen to a 50-year high as a proportion of America's GDP. Companies have used the cashflow from those profits to buy back shares and pay down debt.

Mr Motivator

In the 1990s it seemed as though everybody in America had a neighbour or a relation who was about to become a millionaire through their stock options. Companies were handing them out like free newspapers on Piccadilly. Company boards were happy to offer options since accounting rules allowed them to pretend they had no cost. Employees were happy to take them in the belief that an ever-rising stockmarket would allow them to buy the condominiums of their dreams. Companies without a listing seemed likely to fall behind in the race for talent.

But the collapse of the dotcom bubble made lots of options worthless. These days many employees would just as soon be rewarded with good old-fashioned cash. And now that options are properly accounted for, companies are just as happy to hand cash over.

Besides, partnerships such as lawyers and accountants (not to mention hedge funds) have historically managed to offer very generous rewards to their top employees without the need for a stockmarket quote. And private-equity groups have also been successful at retaining important staff by offering them potentially lucrative stakes. Indeed, top executives may prefer the private sector. For a start, private-equity bosses can keep what they earn secret, while chief executives of quoted companies find themselves the subject of impertinent comments from the media and activist shareholders.

Perhaps as a result, managers can earn a lot more in the unquoted sector. The most famous example is Dave Calhoun, a top GE executive who turned down jobs at S&P 500 companies for the chance to run privately owned VNU, a Dutch media group, for a reported $100m package.

Of course, such executives will take more risks and work hard for their money; private-equity partners can be tough taskmasters. But at least there will be only one set of masters and the goals will be clear. There is no need to worry about the onerous bits of the Sarbanes-Oxley law (in America), or shareholder resolutions separating the roles of chairman and chief executive (in Britain) or hedge funds demanding that businesses be sold off (pretty much anywhere). Public companies have to reveal a lot more than private ones. Pressure groups can pore over every detail of company policy from the use of child labour to carbon emissions.

The danger is that executives running public companies end up spending so much time dealing with shareholders, regulators and campaigners that they neglect the business. Indeed, these different “stakeholders” may well demand different, and irreconcilable, things. Entrepreneurs, the type of people who like to “get things done” may not want the hassle.

There is another problem, identified by Professor Jensen almost two decades ago. The structure of a public company creates an inherent conflict between investors and the managers they hire to run the business. The main problem is what to do with free cashflow, the money left over after all profitable investment projects have been funded. In theory this money should be returned to shareholders, but managers may be reluctant to do so. Holding on to cash means they do not have to go cap in hand to capital markets.

Professor Jensen argued that borrowing imposed discipline on executives. They needed to generate cash to meet interest payments. And, if they wanted to finance a project, they would have to convince investors that it was worthwhile. The result ought to be fewer unprofitable projects because cash is no longer left burning a hole in managers' pockets.

Private-equity firms apply this lesson in spades. They gear up the balance sheets of companies they buy with more debt than public firms are willing to accept. Nearly 20 years of economic stability have led some to believe that even notoriously cyclical businesses, such as carmaking, can now bear higher levels of debt.

In theory, executives working for private-equity owners respond by cutting costs, weeding out unprofitable operations and expanding those parts of the business where returns are highest. This is what generates charges of asset-stripping. But some of this occurs in most takeovers, whether public or private. Most takeovers are justified by “synergies”, which usually means shedding jobs at head office. This is all part of the “creative destruction” process that allows capital to be allocated more efficiently. Academic studies have suggested that private-equity firms create jobs rather than destroy them, although a lot more research needs to be done before everybody will be convinced.

Workers do have a legitimate concern about the security of their pensions. When a company takes on a lot of debt it undoubtedly makes the “covenant” between a company and its pensions scheme less secure. For a start, it increases the risk that a company may go bust, and so may not be making contributions into the scheme in future. And in the short term executives will concentrate on paying down debt rather than making additional payments to close a pension deficit.

It may well be that the shift away from quoted companies turns out to be detrimental to workers' pensions rights. However, those rights were already being eroded, with many quoted-company schemes being closed to new members or to future accruals for existing employees. Private equity is not the main, or even a leading, cause of the pensions crisis.

The conglomerate model

Another potent criticism of private equity is the parallel with the conglomerates of the 1970s and 1980s, such as ITT, BTR and Hanson. Like private-equity firms, the conglomerates used their financial muscle (in their case, highly rated shares rather than borrowed money) to construct diverse industrial empires. They argued, just as private equity does today, that they could improve the companies they owned through superior management.

Eventually, those empires fell apart. Like a shark compelled to keep swimming forward to catch its prey, they needed ever-bigger acquisitions to make progress. Investors concluded that they could diversify on their own, by buying shares in different sectors. They did not need a conglomerate to do the job for them.

Private-equity groups insist they will not run into the same problem. “We don't hang on to the businesses,” says the leader of one. But that creates another potential problem: investing for growth. If a business is going to be sold within, say, five years, what incentive is there to approve the financing of projects that may take a decade or more to pay off?

Private-equity bosses maintain that it is not in their interest to ruin the companies they buy, because they want to sell them again. And it is also the case that the executives of publicly quoted companies can sometimes skimp on capital expenditure, given that they are often under pressure to meet quarterly profit targets.

Superior returns?

In the end, the argument comes down to a simple one: if private-equity firms are organising the assets of companies more efficiently, then the founders of the industry deserve their billions (though not, perhaps, all of their tax breaks). But it is hard to measure the efficiency of private-equity firms directly. The best that can be done is to look at their returns. Here, the evidence is murky. One much-cited study** found that average returns, net of fees, were roughly equal to that produced by the S&P 500 index between 1980 and 2001. That implies that private-equity firms do improve the businesses they own, since gross returns outperform the market. But investors do not seem to benefit. “Overall, returns have not been that special, especially if you adjust for risk,” says Richard Lambert, director-general of the Confederation of British Industry, Britain's main business lobby-group.

The calculations can be complicated by the tortuous accounting used to calculate the private-equity industry's returns. A recent study† suggests that the residual values of companies that remain in private-equity portfolios may have been overstated. Allowing for this cuts the average net return to three percentage points below that of the S&P 500 index.

However, analysis does suggest that a small proportion of private-equity groups has consistently achieved superior returns. And a study by three American academics†† found that the results achieved by end-investors (such as pension funds and private banks) differed widely; college endowments earned returns that were 14 percentage points better than average. This suggests that a headlong rush by pension funds into the sector in pursuit of diversified returns from “alternative assets” might leave many disappointed.

Going private

Could the private-equity model become the norm, replacing the public company? And would that be a good thing? What might be logical for an individual company might not be best for the economy overall. If all companies were to substitute debt for equity on the scale that private-equity firms have, there would be an increase in the cost of debt. That would make superior equity returns hard to achieve.

In addition, private-equity firms need an exit route to sell their investments. Although there is a growing trend for secondary deals, where one group sells a firm it has bought to another, there must be a limit to which further efficiencies can be squeezed out of any particular business. In the end, a public market will be needed for someone to realise their profit.

Indeed, the need for an exit route was neatly demonstrated by the recent flotation of Blackstone, one of the largest private-equity groups, on the New York stockmarket and the decision this week by Kohlberg Kravis Roberts, another of the industry's titans, to follow suit. It does seem a bit hypocritical for these firms, who regularly tout the benefits of the private model, to head for the public markets—but what other route could they take? They could hardly agree to be bought by each other.

A bigger role for private equity might make the economy more vulnerable. Historically, recessions have often occurred when rising interest rates have cut into corporate profits, causing firms to slash employment and capital expenditure. In a world where most companies carried private-equity-style debt levels, companies would be much more vulnerable and recessions might become much more frequent. Monetary policy would become more difficult, with even small changes in interest rates having the potential to cause massive damage to business. And government revenues might be affected if large portions of industry were financed by tax-deductible debt.

But private equity still accounts for only a small proportion of corporate ownership. Much of the industry's activity is among small and medium-sized companies. There is still plenty of scope for private-equity firms to expand.

It may well be, however, that the peak of the cycle is close at hand. Private equity is inevitably a “feast and famine” business: when one fund can raise a lot of capital, they all can. Competition to buy companies then pushes up the price of doing deals, increasing the interest burden and reducing the returns for equity holders. More deals will be done this year, but they may not deliver the kind of returns that investors are hoping for, just as the late 1980s buy-out of RJR Nabisco, the emblematic deal of the era, proved a disappointment.

Since 2003 conditions have been almost ideal for private-equity firms, with low interest rates, lots of liquidity and rising asset prices. But recent events have been moving against them. Bond yields have been rising, making takeovers (which replace equity with debt) more expensive. The high level of corporate profits suggests that there may not be much more to be wrung out of businesses. And the relentless campaign against private-equity tax privileges has made the groups look like easy targets for finance ministers. It may be symbolic that Blackstone's shares quickly slid below the offer price.

Bad debts

Investors also seem to have woken up to the potential risks, perhaps alerted by the losses being suffered in another part of the credit universe—subprime mortgages. They had previously been happy to extend credit on easy terms, such as “covenant-lite” loans (debts with few checks on operating performance) or payment-in-kind notes, where borrowers can substitute more debt for interest payments. Now they are starting to turn down deals where private-equity firms push their luck too far. Banks are getting reluctant to provide the “blank cheques” that private-equity groups were demanding for the bridge financing of deals. In addition, exits may be becoming more difficult: the sale of New Look, a British retailer, collapsed when the last two remaining bidders pulled out.

It is important, however, to distinguish between the cyclical and structural tides. The 1980s private-equity boom ended in the face of rising interest rates and a slumping economy. The same combination might cause another retreat over the next few years. But after that tide has ebbed, more businesses will be in private hands. And when interest rates inevitably fall again, the private-equity wave will once again capture new ground.

iSuppli and Teardowns

Business

The business of dissecting electronics

The lowdown on teardowns

Ripping apart smart-phones reveals their true cost

Jan 21st 2010 | From The Economist print edition

GADGET-LOVERS the world over are already salivating at the thought of getting their hands on Apple’s much-hyped tablet computer, which is expected to be revealed on January 27th. Most of them just like the idea of playing with a new high-tech toy. But a few have darker designs. They cannot wait to rip the device apart, analyse its design, identify its parts and calculate how much it costs to make.

These “teardowns”, as they are called, are common practice in the electronics industry, and are usually performed in-house. Now, however, more of this activity is being outsourced to specialised firms such as iSuppli and UBM TechInsights. Their findings provide a glimpse into the inner workings not just of individual devices but also the fast-growing consumer-electronics business itself.

Derek Lidow, iSuppli’s boss, says companies that design and market gadgets sometimes hire firms like his to tear apart and analyse products whose manufacture they have outsourced to others. Some customers simply want to know why their competitors, often of Chinese provenance, can be so cheap—either to prove suspicions of dumping or to show their own engineers how to do better.

With a smart-phone, for example, iSuppli starts by looking at how its 1,000 or so parts are put together. Apple has the most creative and dense designs, says Andrew Rassweiler, who heads iSuppli’s teardown service. But its rivals are catching up fast. The design of Google’s recently launched phone, the Nexus One, made by HTC of Taiwan, is almost as advanced as that of the latest iPhone.

The next steps in a teardown are identifying the parts and calculating the costs of materials and assembly. Components, especially memory chips, have continued to fall in price. This is why the first iPhone model cost $218 to build and the latest only $170, despite its superior performance. Assembly costs are minimal—just $6.50 for the current iPhone. Even though the parts in high-end smart-phones differ widely, their total construction cost often falls in a narrow range of $170-180 (see chart). Makers apparently set a budget and see what they can fit in, says Mr Rassweiler.

Most smart-phones’ retail prices (before operator subsidies) are around $500-$600. Not all of the difference is profit. There are many other costs, such as research, design, marketing and patent fees, as well as the retailer’s own costs. But the big gap between the cost of building a smart-phone and its price in the shops should widen further as ever more previously discrete components are packed on to a single main microchip. Howard Curtis of UBM TechInsights predicts that as software and mobile services come to represent more of a smart-phone’s overall value, this too will widen the gap between manufacturing costs and selling prices.

What this gap demonstrates is that for smart-phones, like most other electronic devices, most of the value lies not in manufacturing but in all the services and intellectual property it takes to create and market such products. That is something for politicians to ponder: instead of making empty promises about saving ailing manufacturers they might instead consider how best to promote the growth of high-value service industries.

Wired: New Industrial Revolution

There are a log of interesting ideas in this article. First thoughts are on the use of "crowd sourcing." Interestingly, most of the car was either designed through a competition (instead of a collaboration) or through in-house expert engineers. This is not terribly different than the growing use of contests in contracting else where (see the JSF program).

Interestingly though, Local Motors more resembles Lotus than it does General Motors. Lotus also sources the majority of its components, while only designing and making the chassis. Also: Lotus's products also sell at a similar price point (>$50K). The "democratization" of manufacturing probably doesn't represent the demise of large manufacturing in the United States, as US manufacturers primarily make heavy industrial goods. Rather, the industry shift probably represents a greater threat to company that both design and manufacture consumer goods (as opposed to large capital goods) as the shift reduces the strategic advantages of vertical integration between manufacturers and designers. Similar virtualization is occuring through many other industries: semiconductors, cellphones, data centers, etc...

As a proof in point, look at some of the other articles posted this month. Japan's mobile phone industry has declined because of its inability achieve greater scales and embrace global standards and supply chains. Motorola has seen similar disadvantages due to its tendency to source from freescale even when their products aren't as cost efficient or as technically able.

Of course, these changes also represent some possible challenges to virtual firms that just design goods. Apple uses Foxconn to make its product (such as the iPad), and there are reports that Foxconn is planning to produce its own-branded competing products. Similarly, HTC was tapped to make many private label phones for American carriers and Google. Now HTC is starting to produce under its own brand name.

In the Next Industrial Revolution, Atoms Are the New Bits

• By Chris Anderson
• January 25, 2010 |
• 12:00 pm |
• Wired Feb 2010

In an age of open source, custom-fabricated, DIY product design, all you need to conquer the world is a brilliant idea.
Photo: Dan Winters

The door of a dry-cleaner-size storefront in an industrial park in Wareham, Massachusetts, an hour south of Boston, might not look like a portal to the future of American manufacturing, but it is. This is the headquarters of Local Motors, the first open source car company to reach production. Step inside and the office reveals itself as a mind-blowing example of the power of micro-factories.

In June, Local Motors will officially release the Rally Fighter, a $50,000 off-road (but street-legal) racer. The design was crowdsourced, as was the selection of mostly off-the-shelf components, and the final assembly will be done by the customers themselves in local assembly centers as part of a “build experience.” Several more designs are in the pipeline, and the company says it can take a new vehicle from sketch to market in 18 months, about the time it takes Detroit to change the specs on some door trim. Each design is released under a share-friendly Creative Commons license, and customers are encouraged to enhance the designs and produce their own components that they can sell to their peers.

The Rally Fighter was prototyped in the workshop at the back of the Wareham office, but manufacturing muscle also came from Factory Five Racing, a kit-car company and Local Motors investor located just down the road. Of course, the kit-car business has been around for decades, standing as a proof of concept for how small manufacturing can work in the car industry. Kit cars combine hand-welded steel tube chassis and fiberglass bodies with stock engines and accessories. Amateurs assemble the cars at their homes, which exempts the vehicles from many regulatory restrictions (similar to home-built experimental aircraft). Factory Five has sold about 8,000 kits to date.

One problem with the kit-car business, though, is that the vehicles are typically modeled after famous racing and sports cars, making lawsuits and license fees a constant burden. This makes it hard to profit and limits the industry’s growth, even in the face of the DIY boom.

Jay Rogers, CEO of Local Motors, saw a way around this. His company opted for totally original designs: They don’t evoke classic cars but rather reimagine what a car can be. The Rally Fighter’s body was designed by Local Motors’ community of volunteers and puts the lie to the notion that you can’t create anything good by committee (so long as the community is well managed, well led, and well equipped with tools like 3-D design software and photorealistic rendering technology). The result is a car that puts Detroit to shame.

It is, first of all, incredibly cool-looking — a cross between a Baja racer and a P-51 Mustang fighter plane. Given its community provenance, one might have expected something more like a platypus. But this process was no politburo. Instead, it was a competition. The winner was Sangho Kim, a 30-year-old graphic artist and student at the Art Center College of Design in Pasadena, California. When Local Motors asked its community to submit ideas for next-gen vehicles, Kim’s sketches and renderings captivated the crowd. There wasn’t supposed to be a prize, but the company gave Kim $10,000 anyway. As the community coalesced around his Rally Fighter, members competed to develop secondary parts, from the side vents to the light bar. Some were designers, some engineers, and others just car hobbyists. But what they had in common was a refusal to design just another car, compromised by mass-market needs and convention. They wanted to make something original — a fantasy car come to life.

While the community crafted the exterior, Local Motors designed or selected the chassis, engine, and transmission thanks to relationships with companies like Penske Automotive Group, which helped the firm source everything from dashboard dials to the new BMW clean diesel engine the Rally Fighter will use. This combination — have the pros handle the elements that are critical to performance, safety, and manufacturability while the community designs the parts that give the car its shape and style — allows crowdsourcing to work even for a product whose use has life-and-death implications.

Local Motors plans to release between 500 and 2,000 units of each model. It’s a niche vehicle; it won’t compete with the major automakers but rather fill in the gaps in the marketplace for unique designs. Rogers uses the analogy of a jar of marbles, each of which represents a vehicle from a major automaker. In between the marbles is empty space, space that can be filled with grains of sand — and those grains are Local Motors cars.

Local Motors has just 10 full-time employees (that number will grow to more than 50 as it opens build centers, the first of which will be in Phoenix), holds almost no inventory, and purchases components and prepares kits only after buyers have made a down payment and reserved a build date.

Local Motors CEO Jay Rogers combined the power of crowd sourced design and professional experience to develop the Rally Fighter.
Photo: Adrian Gaut

Rogers was practically destined for his job. His grandfather Ralph Rogers bought the Indian Motorcycle Company in 1945. When the light Triumph motorcycles began entering the US after World War II, the senior Rogers recognized that his market-leading Chief, a big road workhorse, was uncompetitive. The solution was to make a new light engine so Indian could produce its own cheap, nimble bikes. He went bust trying to develop the motor. It was just too hard to change direction — and eventually he lost the business.

Today, Rogers’ grandson intends to do something even more radical — create a whole new way of making cars — on a shoestring budget. His company has raised roughly $7 million, and he thinks that’s enough to take it to profitability. The difference between now and then? “They didn’t have resources back then to enter the market, because the manufacturing process was so tightly held,” he says. What’s changed is that the supply chain is opening to the little guys.

The 36-year-old Rogers favors military-style flight suits, an echo of his time as a captain in the Marines, including action in Iraq, and he boasts both a Harvard MBA and a stint as an entrepreneur in China.

While at Harvard, Rogers saw a presentation on Threadless, the open-design T-shirt company, which showed him the power of crowdsourcing. Cars are more complicated than T-shirts, but in both cases there are far more people who can design them than are currently paid to do so — Rogers estimates that less than 30 percent of car design students get jobs at auto companies upon graduation. The rest become frustrated car designers, exactly the pool of talent that might respond to a well-organized vehicle design competition and community. Today, the Local Motors Web site has around 5,000 members. That’s a 500-to-1 ratio of volunteer contributors to employees. This is how industries are reinvented.

Here’s the history of two decades in one sentence: If the past 10 years have been about discovering post-institutional social models on the Web, then the next 10 years will be about applying them to the real world.

This story is about the next 10 years.

Transformative change happens when industries democratize, when they’re ripped from the sole domain of companies, governments, and other institutions and handed over to regular folks. The Internet democratized publishing, broadcasting, and communications, and the consequence was a massive increase in the range of both participation and participants in everything digital — the long tail of bits.

Now the same is happening to manufacturing — the long tail of things.

The tools of factory production, from electronics assembly to 3-D printing, are now available to individuals, in batches as small as a single unit. Anybody with an idea and a little expertise can set assembly lines in China into motion with nothing more than some keystrokes on their laptop. A few days later, a prototype will be at their door, and once it all checks out, they can push a few more buttons and be in full production, making hundreds, thousands, or more. They can become a virtual micro-factory, able to design and sell goods without any infrastructure or even inventory; products can be assembled and drop-shipped by contractors who serve hundreds of such customers simultaneously.

Today, micro-factories make everything from cars to bike components to bespoke furniture in any design you can imagine. The collective potential of a million garage tinkerers is about to be unleashed on the global markets, as ideas go straight into production, no financing or tooling required. “Three guys with laptops” used to describe a Web startup. Now it describes a hardware company, too.

“Hardware is becoming much more like software,” as MIT professor Eric von Hippel puts it. That’s not just because there’s so much software in hardware these days, with products becoming little more than intellectual property wrapped in commodity materials, whether it’s the code that drives the off-the-shelf chips in gadgets or the 3-D design files that drive manufacturing. It’s also because of the availability of common platforms, easy-to-use tools, Web-based collaboration, and Internet distribution.

We’ve seen this picture before: It’s what happens just before monolithic industries fragment in the face of countless small entrants, from the music industry to newspapers. Lower the barriers to entry and the crowd pours in.

The academic way to put this is that global supply chains have become scale-free, able to serve the small as well as the large, the garage inventor and Sony. This change is driven by two forces. First, the explosion in cheap and powerful prototyping tools, which have become easier to use by non-engineers. And second, the economic crisis has triggered an extraordinary shift in the business practices of (mostly) Chinese factories, which have become increasingly flexible, Web-centric, and open to custom work (where the volumes are lower but the margins higher).

The result has allowed online innovation to extend to the real world. As Cory Doctorow puts it in his new book, Makers, “The days of companies with names like ‘General Electric’ and ‘General Mills’ and ‘General Motors’ are over. The money on the table is like krill: a billion little entrepreneurial opportunities that can be discovered and exploited by smart, creative people.”

A garage renaissance is spilling over into such phenomena as the booming Maker Faires and local “hackerspaces.” Peer production, open source, crowdsourcing, user-generated content — all these digital trends have begun to play out in the world of atoms, too. The Web was just the proof of concept. Now the revolution hits the real world.

In short, atoms are the new bits.

Mark Hatch (standing center) and Jim Newton (far left, glasses) of Tech Shop, where members pay for access to sophisticated prototyping tools.
Photo: Leon Chew

It all starts with the tools. in a converted brewery in Brooklyn, Bre Pettis and his team of hardware engineers are making the first sub-$1,000 3-D printer, the open source MakerBot. Rather than squirting out ink, this printer builds up objects by squeezing out a 0.33-mm-thick thread of molten ABS plastic. Five years ago, you couldn’t get anything like this for less than $125,000.

During a visit in late November, 100 boxes containing the ninth batch of MakerBots are lined up and ready to go out the door (as a customer, I’m thrilled to know that one of them is coming to me). Nearly 500 of these 3-D printers have been sold, and with every one, the community comes up with new uses and new tools to make them even better. For example, a prototype head delivers a resolution of 0.2 mm. Another head can hold a rotating cutter, turning the printer into a CNC router. (CNC is short for computer numerical control, which simply means that the machines are driven by software.) And yet another can print with icing, for desserts.

Out of the box, the MakerBot produces plastic parts from digital files. Want a certain gear right now? Download a design and print it out yourself. Want to modify an object you already have? Scan it (a researcher at the University of Cambridge has developed a technology that will allow you to create a 3-D file by rotating the object in front of your webcam), tweak the bits you want to change with the free SketchUp software from Google, and load it into the ReplicatorG app. Within minutes, you have a whole new physical object: a rip, mix, and burn of atoms.

Other tools offer additional tricks. The $18,000 ShopBot PRSalpha can work door-sized pieces of wood. Or buy a smaller kit for $1,500 at buildyourcnc.com. If metal is your material, try a CNC mill for around $2,000. Or, if you’re more into electronics, use the free CadSoft Eagle software to create your own circuit boards, then upload the file to have it fabbed in a few days at places like Advanced Circuits.

So, too, for CNC laser cutters, plasma cutters, water-jet cutters, and lathes. You can make anything from fine jewelry to car chassis this way, and tens of thousands of people are doing just that. We’ve already seen this DIY creation movement boom around such simple platforms as T-shirts and coffee mugs, then expand into handcrafting at Etsy (which did about $200 million in sales last year). Now it’s moving to more complex platforms — like 3-D models and plastic fabrication — and open source electronics hardware like the pioneering Arduino project.

With the tools in place, the second part of this new industrial age is how manufacturing has been opened up to individuals, letting them scale prototypes into full production runs. Over the past few years, Chinese manufacturers have evolved to handle small orders more efficiently. This means that one-person enterprises can get things made in a factory the way only big companies could before.

Two trends are driving this. First, there’s the maturation and increasing Web-centrism of business practices in China. Now that the Web generation is entering management, Chinese factories increasingly take orders online, communicate with customers by email, and accept payment by credit card or PayPal, a consumer-friendly alternative to traditional bank transfers, letters of credit, and purchase orders. Plus, the current economic crisis has driven companies to seek higher-margin custom orders to mitigate the deflationary spiral of commodity goods.

For a lens into the new world of open-access factories in China, check out Alibaba .com, the largest aggregator of the country’s manufacturers, products, and capabilities. Just search on the site (in English), find some companies producing more or less what you’re looking to make, and then use instant messaging to ask them if they can manufacture what you want. Alibaba’s IM can translate between Chinese and English in real time, so each person can communicate using their native language. Typically, responses come in minutes: We can’t make that; we can make that and here’s how to order it; we already make something quite like that and here’s what it costs.

Alibaba’s chair, Jack Ma, calls this “C to B” — consumer to business. It’s a new avenue of trade and one ideally suited for the micro-entrepreneur of the DIY movement. “If we can encourage companies to do more small, cross-border transactions, the profits can be higher, because they are unique, non-commodity goods,” Ma says. Since its founding in 1999, Alibaba has become a $12 billion company with 45 million registered users worldwide. Its $1.7 billion initial public offering on the Hong Kong Stock Exchange in 2007 was the biggest tech debut since Google. Over the past three years, Ma says, more than 1.1 million jobs have been created in China by companies doing ecommerce across Alibaba’s platforms.

This trend is playing out in many countries, but it’s happening fastest in China. One reason is the same cultural dynamism that led to the rise of shanzhai industries. The term shanzhai, which derives from the Chinese word for bandit, usually refers to the thriving business of making knockoffs of electronic products, or as Shanzai.com more generously puts it, “a vendor, who operates a business without observing the traditional rules or practices often resulting in innovative and unusual products or business models.” But those same vendors are increasingly driving the manufacturing side of the maker revolution by being fast and flexible enough to work with micro-entrepreneurs. The rise of shanzhai business practices “suggests a new approach to economic recovery as well, one based on small companies well networked with each other,” observes Tom Igoe, a core developer of the open source Arduino computing platform. “What happens when that approach hits the manufacturing world? We’re about to find out.”

Not long ago, all this was impossible. To see how it used to be back in the 20th century, watch the movie Flash of Genius. The film, which is based on a true story, starts in the mid-1960s and tells the sad tale of the invention of the intermittent windshield wiper. A lone inventor — college professor Bob Kearns — tinkers in his basement until he finally creates a working prototype. Rather than sell the technology to a big car company, Kearns decides he wants to build his own company and make the wiper himself. Ford signs on to install Kearns’ wipers in one of its new models. That means he needs to build a factory. He leases a huge warehouse and starts outfitting it with assembly lines, forklift loaders, and other heavy equipment — a classic industrial-age scene.

How to Build Your Dream

In the age of democratized industry, every garage is a potential micro-factory, every citizen a potential micro-entrepreneur. Here’s how to transform a great idea into a great product.

1) INVENT Stop whining about the dearth of cool products in the world — dream up your own. Pro tip: Check the US Patent and Trademark Office Web site to ensure no one else had the idea first.

2) DESIGN Use free tools like Blender or Google’s SketchUp to create a 3-D digital model of your invention. Or download someone else’s design and incorporate your groundbreaking tweaks.

3) PROTOTYPE You don’t need to be Geppetto to crank out a prototype; desktop 3-D printers like MakerBot are available for under $1,000. Just upload a file and watch the machine render your vision in layered ABS plastic.

4) MANUFACTURE The garage is fine for limited production, but if you want to go big, go global — outsource. Factories in China are standing by; sites like Alibaba.com can help you find the right partner.

5) SELL Market your product directly to customers via an online store like SparkFun — or set up your own ecommerce outfit through a company like Yahoo or Web Studio. Then haul your golden goose to Maker Faire and become the poster child for the DIY industrial revolution.

As Kearns is getting close to firing up his facility, Ford abruptly backs out of the deal. With no revenue in sight, the factory shuts down before producing a single wiper.

Eighteen months later, Kearns is walking home in the rain and sees a brand new Ford Mustang turn the corner. The windshield wiper sweeps, then pauses, then sweeps again. His brilliant idea has been stolen. Kearns is ruined and will soon go mad, thus the rest of the movie. (The real-life Kearns eventually sued Ford and Chrysler for patent infringement and, after years of litigation, won nearly $30 million.)

Today, Kearns would do it differently. As before, he would have made the first prototype in his basement. But rather than building a factory, he would have had the electronics fabbed by one company and the enclosure made by another. He then would have paid a wiper manufacturer in China to create a custom assembly with these components. They would have shipped straight to his customers, the car companies, and the whole process would have happened in months, not years — too fast for big companies to beat him. No factory, no lawsuits, no madness. He could have fulfilled his dream of turning his invention into a company without tilting at windmills.

To see this model emerging in the real world, you need only visit TechShop, a chain of DIY workspaces that offer access to state-of-the-art prototyping tools for around $100 a month.

On a recent afternoon at the facility in Menlo Park, California, Michael Pinneo, a successful former executive in the synthetic-diamond business, is machining a vapor- deposition chamber for his newest approach to creating colorless diamonds. Over in the corner stands the base of a rocket lander being developed by a team that’s competing in the Google Lunar X Prize. At another table, Stephan Weiss, vice president of Interoptix, and one of his colleagues are assembling circuit boards used to manage electricity grids. They’re doing 50-unit runs, which Weiss describes as “too small for a factory but too big for your garage.” The devices carry the badge of ABB, a giant engineering firm; the utility customers may never know that they were made by hand in a hackerspace.

This is an incubator for the atoms age. When TechShop founder Jim Newton went looking for an executive to run it, he quickly decided on Mark Hatch, a former Kinko’s executive. The analogy is apt: In the same way that Kinko’s democratized printing and, in the process, created a national chain of service bureaus, TechShop wants to democratize manufacturing. It now has two additional locations, in Durham, North Carolina, and Beaverton, Oregon, and has plans for hundreds more. One of the spots being considered is San Francisco, within the facilities of the much-shrunken San Francisco Chronicle. The irony is delicious: the seeds of tomorrow’s industry growing in the ashes of yesterday’s.

Over lunch, Hatch reflects on the arc of manufacturing history. With the rise of the factory in the industrial age, Karl Marx fretted that a tradesman could no longer afford the tools to ply his trade. The economies of scale of industrial production crowded out the individual. Although the benefits of such industrialization were lower prices and better products, the cost was homogeneity. Combined with big-box retailers, the marketplace became increasingly dominated by the fruits of mass production: goods designed for everyone, with the resulting limited diversity and choice that implies.

But today those tools of production are getting so cheap that they are once again within the reach of many individuals. State-of-the-art milling machines that once cost $150,000 are now close to $4,000, thanks to Chinese copies. Everybody’s garage is a potential high tech factory. Marx would be pleased.

Blogger Jason Kottke wrestled with what to call this new class of entrepreneurship, these cottage industries with global reach targeting niche markets of distributed demand. “Boutique” is too pretentious, and “indie” not quite right. He observed that others had suggested “craftsman, artisan, bespoke, cloudless, studio, atelier, long tail, agile, bonsai company, mom and pop, small scale, specialty, anatomic, big heart, GTD business, dojo, haus, temple, coterie, and disco business.” But none seemed to capture the movement.

So he proposed “small batch,” a term most often applied to bourbon. In the spirits world, this implies handcrafted care. But it can broadly refer to businesses focused more on the quality of their products than the size of the market. They’d rather do something they were passionate about than go mass. And these days, when anyone can get access to manufacturing and distribution, that is actually a viable choice. Walmart, and all the compromise that comes with it, is no longer the only path to success.

For a final example of that, swing to the Seattle suburbs to meet Will Chapman of BrickArms. Out of a small industrial space, BrickArms fills gaps in the Lego product line, going where the Danish toy giant fears to tread: hardcore weaponry, from Lego-scale AK-47s to frag grenades that look like they came straight out of Halo 3. The parts are more complex than the average Lego component, but they’re manufactured to an equal quality and sold online to thousands of Lego fans, kids and adults, who want to create cooler scenes than the standard kits allow.

Lego operates on an industrial scale, with a team of designers working in a highly secure campus in Billund, Denmark. Engineers model prototypes and have them fabricated in dedicated machine shops. Then, once they meet approval, they’re manufactured in large injection molding plants. Parts are created for kits, and those kits have to be play-tested, priced for mass retail, and shipped and inventoried months in advance of their sale at Target or Walmart. The only parts that make it out of this process are those that will sell in the millions.

Chapman works at a different scale. He designs parts using SolidWorks 3-D software, which can create a reverse image that’s used to produce a mold. He sends the file to his desktop CNC router, a Taig 2018 mill that costs less than $1,000, which grinds the mold halves out of aircraft-grade aluminum blocks. Then he puts them in his hand-pressed injection molding machine, melts some resin beads, and pumps them through. A few minutes later, he’s got a prototype to show to fans. If they like it, he gets a local toolmaker to reproduce the mold out of steel and a US-based injection molding company to make batches of a few thousand.

Why not have the parts made in China? He could, he says, but the result would be “molds that take much longer to produce, with slow communication times and plastic that is subpar” (read: cheap). Furthermore, he says, “if your molds are in China, who knows what happens to them when you’re not using them? They could be run in secret to produce parts sold in secondary markets that you would not even know existed.”

Chapman’s three sons package the parts, which he sells direct. Today, BrickArms also has resellers in the UK, Australia, Sweden, Canada, and Germany. The business grew so big that in 2008 he left his 17-year career as a software engineer; he now comfortably supports his family of five solely on Lego weapon sales. “I bring in more revenue on a slow BrickArms day than I ever did working as a software engineer.” Life is good.

In the mid-1930s, Ronald Coase, then a recent London School of Economics graduate, was musing over what to many people might have seemed a silly question: Why do companies exist? Why do we pledge our allegiance to an institution and gather in the same building to get things done? His answer: to minimize “transaction costs.” When people share a purpose and have established roles, responsibilities, and modes of communication, it’s easy to make things happen. You simply turn to the person in the next cubicle and ask them to do their job.

But several years ago, Bill Joy, one of the cofounders of Sun Microsystems, revealed the flaw in Coase’s model. “No matter who you are, most of the smartest people work for someone else,” he rightly observed. Of course, that had always been true, but before, it hardly mattered if you were in Detroit and someone better was in Dakar; you were here and they were there, and that was the end of it. But Joy’s point was that this was changing. With the Internet, you didn’t have to settle for the next cubicle. You could tap the best person out there, even if they were in Dakar.

Joy’s law turned Coase’s law upside down. Now, working within a company often imposes higher transaction costs than running a project online. Why turn to the person who happens to be in the next cubicle when it’s just as easy to turn to an online community member from a global marketplace of talent? Companies are full of bureaucracy, procedures, and approval processes, a structure designed to defend the integrity of the organization. Communities form around shared interests and needs and have no more process than they require. The community exists for the project, not to support the company in which the project resides.

Thus the new industrial organizational model. It’s built around small pieces, loosely joined. Companies are small, virtual, and informal. Most participants are not employees. They form and re-form on the fly, driven by ability and need rather than affiliation and obligation. It doesn’t matter who the best people work for; if the project is interesting enough, the best people will find it.

Let me tell you my own story. Three years ago, out on a run, I started thinking about how cheap gyroscope sensors were getting. What could you do with them? For starters, I realized, you could turn a radio-controlled model airplane into an autonomous unmanned aerial vehicle, or drone. It turned out that there were plenty of commercial autopilot units you could buy, all based on this principle, but the more I looked into them, the worse they appeared. They were expensive ($800 to $5,000), hard to use, and proprietary. It was clear that this was a market desperate for competition and democratization — Moore’s law was at work, making all the components dirt cheap. The hardware for a good autopilot shouldn’t cost more than $300, even including a healthy profit. Everything else was intellectual property, and it seemed the time had come to open that up, trading high margins for open innovation.

To pursue this project, I started DIY Drones, a community site, and found and began working with some kindred spirits, led by Jordi Muñoz, then a 21-year-old high school graduate from Mexico living in Riverside, California. Muñoz was self-taught — with world-class skills in embedded electronics and aeronautics. Jordi turned me on to Arduino, and together we designed an autonomous blimp controller and then an aircraft autopilot board.

We designed the boards the way all electronics tinkerers do, with parts bought from online shops, wired together on prototyping breadboards. Once it worked on the breadboard, we laid out the schematic diagrams with CadSoft Eagle and started designing it as a custom printed circuit board (PCB). Each time we had a design that looked good onscreen, we’d upload it to a commercial PCB fab, and a couple of weeks later, samples would arrive at our door. We’d solder on the components, try them out, and then fix our errors and otherwise make improvements for the next version.

Eventually, we had a design we were happy with. How to commercialize it? We could do it ourselves, getting our PCB fab house to solder on the components, too, but we thought it might be better to partner with a retailer. The one that seemed culturally matched was SparkFun, which designs, makes, and sells electronics for the growing open source hardware community.

The SparkFun operation is in a newish two-story building in an office park outside Boulder, Colorado. The first floor is larger than three basketball courts, with racks of circuit boards waiting to be sold, packed, and shipped on one side and some machines attended by a few technicians on the other. The first two machines are pick-and-place robots, which are available used for less than $5,000. They position tiny electronic components in exactly the right spot on a PCB. Once each batch of boards is done, technicians place them on a conveyor belt that goes into another machine, which is basically just a heater. Called a reflow oven, it cements the parts into place, essentially accomplishing what a worker could do with a soldering iron but with unmatched precision and speed.

The PCBs arrive from SparkFun’s partner firm in China, which makes millions of them using automated etching, drilling, and cutting machines. At volume, they cost a few cents each.

That’s it. With these elements you can make the basics of everything from a cell phone to a robot (structural elements, such as the case, can be made in low volume with a CNC machine or injection-molded if you need to do it cheaper at higher volume). You can sell these components as kits or find some college students on craigslist to spend a weekend assembling them for you. (I conscript my kids to assemble our blimps. They rotate roles, coveting the quality assurance task where they check the others’ work.)

SparkFun makes, stocks, and sells our autopilot and a few other products that we designed; we get to spend our time working on R&D and bear no inventory risk. Some products we wanted to make were too time-intensive for SparkFun, so we made them ourselves. Now, in a rented Los Angeles garage, we have our own mini SparkFun. Rather than a pick-and-place robot, we have a kid with sharp eyes and a steady hand, and for a reflow oven we use what is basically a modified toaster oven. We can do scores of boards per day this way; when demand outstrips production, we’ll upgrade to a small pick-and-place robot.

Every day our Web site takes orders and prints out the shipping labels. Muñoz or one of his workers heat-seals the products in protective electrostatic bags and puts them in shipping envelopes. The retail day ends at 3:30 pm with a run to the post office and UPS to send everything off. In our first year, we’ll do about $250,000 in revenue, with demand rising fast and a lot of products in the pipeline. With luck, we’ll be a million-dollar business by the third year, which would put us solidly in the ranks of millions of similarly successful US companies. We are just a tiny gear in the economic engine driving the US — on the face of it, this doesn’t seem like a world- changing economic model.

But the difference between this kind of small business and the dry cleaners and corner shops that make up the majority of micro-enterprise in the country is that we’re global and high tech. Two-thirds of our sales come from outside the US, and our products compete at the low end with defense contractors like Lockheed Martin and Boeing. Although we don’t employ many people or make much money, our basic model is to lower the cost of technology by a factor of 10 (mostly by not charging for intellectual property). The effect is felt primarily by consumers; when you take an order of magnitude out of pricing in any market, you can radically reshape it, bringing in more and different customers. Lowering costs is a way to democratize technology, too.

Although it’s shrinking, America’s manufacturing economy is still the world’s largest. But China’s growing production sector is predicted to take the number one spot in 2015, according to IHS Global Insight, an economic-forecasting firm. Not all US manufacturing is shrinking, however — just the large part. A Pease Group survey of small manufacturers (less than $25 million in annual sales) shows that most expect to grow this year, many by double digits. Indeed, analysts expect almost all new manufacturing jobs in the US will come from small companies. Ones just like ours.

How big can these small enterprises get? Most of the companies I’ve described sell thousands of units — 10,000 is considered a breakout success. But one that has graduated to the big leagues is Aliph, which makes the Jawbone noise-canceling wireless headsets. Aliph was founded in 1999 by two Stanford graduates, Alex Asseily and Hosain Rahman, and it now sells millions of headsets each year. But it has no factories. It outsources all of its production. And though more than a thousand people help to create Jawbone headsets, Aliph has just over 80 employees. Everyone else works for its production partners. It’s the ultimate virtual manufacturing company: Aliph makes bits and its partners make atoms, and together they can take on Sony.

Welcome to the next Industrial Revolution.

The Gaussian Copula Function

Recipe for Disaster: The Formula That Killed Wall Street

By Felix Salmon 02.23.09

In the mid-'80s, Wall Street turned to the quants—brainy financial engineers—to invent new ways to boost profits. Their methods for minting money worked brilliantly... until one of them devastated the global economy.
Photo: Jim Krantz/Gallery Stock

Road Map for Financial Recovery: Radical Transparency Now!

A year ago, it was hardly unthinkable that a math wizard like David X. Li might someday earn a Nobel Prize. After all, financial economists—even Wall Street quants—have received the Nobel in economics before, and Li's work on measuring risk has had more impact, more quickly, than previous Nobel Prize-winning contributions to the field. Today, though, as dazed bankers, politicians, regulators, and investors survey the wreckage of the biggest financial meltdown since the Great Depression, Li is probably thankful he still has a job in finance at all. Not that his achievement should be dismissed. He took a notoriously tough nut—determining correlation, or how seemingly disparate events are related—and cracked it wide open with a simple and elegant mathematical formula, one that would become ubiquitous in finance worldwide.

For five years, Li's formula, known as a Gaussian copula function, looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.

His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched—and was making people so much money—that warnings about its limitations were largely ignored.

Then the model fell apart. Cracks started appearing early on, when financial markets began behaving in ways that users of Li's formula hadn't expected. The cracks became full-fledged canyons in 2008—when ruptures in the financial system's foundation swallowed up trillions of dollars and put the survival of the global banking system in serious peril.

David X. Li, it's safe to say, won't be getting that Nobel anytime soon. One result of the collapse has been the end of financial economics as something to be celebrated rather than feared. And Li's Gaussian copula formula will go down in history as instrumental in causing the unfathomable losses that brought the world financial system to its knees.

How could one formula pack such a devastating punch? The answer lies in the bond market, the multitrillion-dollar system that allows pension funds, insurance companies, and hedge funds to lend trillions of dollars to companies, countries, and home buyers.

A bond, of course, is just an IOU, a promise to pay back money with interest by certain dates. If a company—say, IBM—borrows money by issuing a bond, investors will look very closely over its accounts to make sure it has the wherewithal to repay them. The higher the perceived risk—and there's always some risk—the higher the interest rate the bond must carry.

Bond investors are very comfortable with the concept of probability. If there's a 1 percent chance of default but they get an extra two percentage points in interest, they're ahead of the game overall—like a casino, which is happy to lose big sums every so often in return for profits most of the time.

Bond investors also invest in pools of hundreds or even thousands of mortgages. The potential sums involved are staggering: Americans now owe more than $11 trillion on their homes. But mortgage pools are messier than most bonds. There's no guaranteed interest rate, since the amount of money homeowners collectively pay back every month is a function of how many have refinanced and how many have defaulted. There's certainly no fixed maturity date: Money shows up in irregular chunks as people pay down their mortgages at unpredictable times—for instance, when they decide to sell their house. And most problematic, there's no easy way to assign a single probability to the chance of default.

Wall Street solved many of these problems through a process called tranching, which divides a pool and allows for the creation of safe bonds with a risk-free triple-A credit rating. Investors in the first tranche, or slice, are first in line to be paid off. Those next in line might get only a double-A credit rating on their tranche of bonds but will be able to charge a higher interest rate for bearing the slightly higher chance of default. And so on.

"...correlation is charlatanism"
Photo: AP photo/Richard Drew

The reason that ratings agencies and investors felt so safe with the triple-A tranches was that they believed there was no way hundreds of homeowners would all default on their loans at the same time. One person might lose his job, another might fall ill. But those are individual calamities that don't affect the mortgage pool much as a whole: Everybody else is still making their payments on time.

But not all calamities are individual, and tranching still hadn't solved all the problems of mortgage-pool risk. Some things, like falling house prices, affect a large number of people at once. If home values in your neighborhood decline and you lose some of your equity, there's a good chance your neighbors will lose theirs as well. If, as a result, you default on your mortgage, there's a higher probability they will default, too. That's called correlation—the degree to which one variable moves in line with another—and measuring it is an important part of determining how risky mortgage bonds are.

Investors like risk, as long as they can price it. What they hate is uncertainty—not knowing how big the risk is. As a result, bond investors and mortgage lenders desperately want to be able to measure, model, and price correlation. Before quantitative models came along, the only time investors were comfortable putting their money in mortgage pools was when there was no risk whatsoever—in other words, when the bonds were guaranteed implicitly by the federal government through Fannie Mae or Freddie Mac.

Yet during the '90s, as global markets expanded, there were trillions of new dollars waiting to be put to use lending to borrowers around the world—not just mortgage seekers but also corporations and car buyers and anybody running a balance on their credit card—if only investors could put a number on the correlations between them. The problem is excruciatingly hard, especially when you're talking about thousands of moving parts. Whoever solved it would earn the eternal gratitude of Wall Street and quite possibly the attention of the Nobel committee as well.

To understand the mathematics of correlation better, consider something simple, like a kid in an elementary school: Let's call her Alice. The probability that her parents will get divorced this year is about 5 percent, the risk of her getting head lice is about 5 percent, the chance of her seeing a teacher slip on a banana peel is about 5 percent, and the likelihood of her winning the class spelling bee is about 5 percent. If investors were trading securities based on the chances of those things happening only to Alice, they would all trade at more or less the same price.

But something important happens when we start looking at two kids rather than one—not just Alice but also the girl she sits next to, Britney. If Britney's parents get divorced, what are the chances that Alice's parents will get divorced, too? Still about 5 percent: The correlation there is close to zero. But if Britney gets head lice, the chance that Alice will get head lice is much higher, about 50 percent—which means the correlation is probably up in the 0.5 range. If Britney sees a teacher slip on a banana peel, what is the chance that Alice will see it, too? Very high indeed, since they sit next to each other: It could be as much as 95 percent, which means the correlation is close to 1. And if Britney wins the class spelling bee, the chance of Alice winning it is zero, which means the correlation is negative: -1.

If investors were trading securities based on the chances of these things happening to both Alice and Britney, the prices would be all over the place, because the correlations vary so much.

But it's a very inexact science. Just measuring those initial 5 percent probabilities involves collecting lots of disparate data points and subjecting them to all manner of statistical and error analysis. Trying to assess the conditional probabilities—the chance that Alice will get head lice if Britney gets head lice—is an order of magnitude harder, since those data points are much rarer. As a result of the scarcity of historical data, the errors there are likely to be much greater.

In the world of mortgages, it's harder still. What is the chance that any given home will decline in value? You can look at the past history of housing prices to give you an idea, but surely the nation's macroeconomic situation also plays an important role. And what is the chance that if a home in one state falls in value, a similar home in another state will fall in value as well?

Here's what killed your 401(k) David X. Li's Gaussian copula function as first published in 2000. Investors exploited it as a quick—and fatally flawed—way to assess risk. A shorter version appears on this month's cover of Wired.

Probability Specifically, this is a joint default probability—the likelihood that any two members of the pool (A and B) will both default. It's what investors are looking for, and the rest of the formula provides the answer.	Survival times The amount of time between now and when A and B can be expected to default. Li took the idea from a concept in actuarial science that charts what happens to someone's life expectancy when their spouse dies.	Equality A dangerously precise concept, since it leaves no room for error. Clean equations help both quants and their managers forget that the real world contains a surprising amount of uncertainty, fuzziness, and precariousness.
Copula This couples (hence the Latinate term copula) the individual probabilities associated with A and B to come up with a single number. Errors here massively increase the risk of the whole equation blowing up.	Distribution functions The probabilities of how long A and B are likely to survive. Since these are not certainties, they can be dangerous: Small miscalculations may leave you facing much more risk than the formula indicates.	Gamma The all-powerful correlation parameter, which reduces correlation to a single constant—something that should be highly improbable, if not impossible. This is the magic number that made Li's copula function irresistible.

Enter Li, a star mathematician who grew up in rural China in the 1960s. He excelled in school and eventually got a master's degree in economics from Nankai University before leaving the country to get an MBA from Laval University in Quebec. That was followed by two more degrees: a master's in actuarial science and a PhD in statistics, both from Ontario's University of Waterloo. In 1997 he landed at Canadian Imperial Bank of Commerce, where his financial career began in earnest; he later moved to Barclays Capital and by 2004 was charged with rebuilding its quantitative analytics team.

Li's trajectory is typical of the quant era, which began in the mid-1980s. Academia could never compete with the enormous salaries that banks and hedge funds were offering. At the same time, legions of math and physics PhDs were required to create, price, and arbitrage Wall Street's ever more complex investment structures.

In 2000, while working at JPMorgan Chase, Li published a paper in The Journal of Fixed Income titled "On Default Correlation: A Copula Function Approach." (In statistics, a copula is used to couple the behavior of two or more variables.) Using some relatively simple math—by Wall Street standards, anyway—Li came up with an ingenious way to model default correlation without even looking at historical default data. Instead, he used market data about the prices of instruments known as credit default swaps.

If you're an investor, you have a choice these days: You can either lend directly to borrowers or sell investors credit default swaps, insurance against those same borrowers defaulting. Either way, you get a regular income stream—interest payments or insurance payments—and either way, if the borrower defaults, you lose a lot of money. The returns on both strategies are nearly identical, but because an unlimited number of credit default swaps can be sold against each borrower, the supply of swaps isn't constrained the way the supply of bonds is, so the CDS market managed to grow extremely rapidly. Though credit default swaps were relatively new when Li's paper came out, they soon became a bigger and more liquid market than the bonds on which they were based.

When the price of a credit default swap goes up, that indicates that default risk has risen. Li's breakthrough was that instead of waiting to assemble enough historical data about actual defaults, which are rare in the real world, he used historical prices from the CDS market. It's hard to build a historical model to predict Alice's or Britney's behavior, but anybody could see whether the price of credit default swaps on Britney tended to move in the same direction as that on Alice. If it did, then there was a strong correlation between Alice's and Britney's default risks, as priced by the market. Li wrote a model that used price rather than real-world default data as a shortcut (making an implicit assumption that financial markets in general, and CDS markets in particular, can price default risk correctly).

It was a brilliant simplification of an intractable problem. And Li didn't just radically dumb down the difficulty of working out correlations; he decided not to even bother trying to map and calculate all the nearly infinite relationships between the various loans that made up a pool. What happens when the number of pool members increases or when you mix negative correlations with positive ones? Never mind all that, he said. The only thing that matters is the final correlation number—one clean, simple, all-sufficient figure that sums up everything.

The effect on the securitization market was electric. Armed with Li's formula, Wall Street's quants saw a new world of possibilities. And the first thing they did was start creating a huge number of brand-new triple-A securities. Using Li's copula approach meant that ratings agencies like Moody's—or anybody wanting to model the risk of a tranche—no longer needed to puzzle over the underlying securities. All they needed was that correlation number, and out would come a rating telling them how safe or risky the tranche was.

As a result, just about anything could be bundled and turned into a triple-A bond—corporate bonds, bank loans, mortgage-backed securities, whatever you liked. The consequent pools were often known as collateralized debt obligations, or CDOs. You could tranche that pool and create a triple-A security even if none of the components were themselves triple-A. You could even take lower-rated tranches of other CDOs, put them in a pool, and tranche them—an instrument known as a CDO-squared, which at that point was so far removed from any actual underlying bond or loan or mortgage that no one really had a clue what it included. But it didn't matter. All you needed was Li's copula function.

The CDS and CDO markets grew together, feeding on each other. At the end of 2001, there was $920 billion in credit default swaps outstanding. By the end of 2007, that number had skyrocketed to more than $62 trillion. The CDO market, which stood at $275 billion in 2000, grew to $4.7 trillion by 2006.

At the heart of it all was Li's formula. When you talk to market participants, they use words like beautiful, simple, and, most commonly, tractable. It could be applied anywhere, for anything, and was quickly adopted not only by banks packaging new bonds but also by traders and hedge funds dreaming up complex trades between those bonds.

"The corporate CDO world relied almost exclusively on this copula-based correlation model," says Darrell Duffie, a Stanford University finance professor who served on Moody's Academic Advisory Research Committee. The Gaussian copula soon became such a universally accepted part of the world's financial vocabulary that brokers started quoting prices for bond tranches based on their correlations. "Correlation trading has spread through the psyche of the financial markets like a highly infectious thought virus," wrote derivatives guru Janet Tavakoli in 2006.

The damage was foreseeable and, in fact, foreseen. In 1998, before Li had even invented his copula function, Paul Wilmott wrote that "the correlations between financial quantities are notoriously unstable." Wilmott, a quantitative-finance consultant and lecturer, argued that no theory should be built on such unpredictable parameters. And he wasn't alone. During the boom years, everybody could reel off reasons why the Gaussian copula function wasn't perfect. Li's approach made no allowance for unpredictability: It assumed that correlation was a constant rather than something mercurial. Investment banks would regularly phone Stanford's Duffie and ask him to come in and talk to them about exactly what Li's copula was. Every time, he would warn them that it was not suitable for use in risk management or valuation.

David X. Li
Illustration: David A. Johnson

In hindsight, ignoring those warnings looks foolhardy. But at the time, it was easy. Banks dismissed them, partly because the managers empowered to apply the brakes didn't understand the arguments between various arms of the quant universe. Besides, they were making too much money to stop.

In finance, you can never reduce risk outright; you can only try to set up a market in which people who don't want risk sell it to those who do. But in the CDO market, people used the Gaussian copula model to convince themselves they didn't have any risk at all, when in fact they just didn't have any risk 99 percent of the time. The other 1 percent of the time they blew up. Those explosions may have been rare, but they could destroy all previous gains, and then some.

Li's copula function was used to price hundreds of billions of dollars' worth of CDOs filled with mortgages. And because the copula function used CDS prices to calculate correlation, it was forced to confine itself to looking at the period of time when those credit default swaps had been in existence: less than a decade, a period when house prices soared. Naturally, default correlations were very low in those years. But when the mortgage boom ended abruptly and home values started falling across the country, correlations soared.

Bankers securitizing mortgages knew that their models were highly sensitive to house-price appreciation. If it ever turned negative on a national scale, a lot of bonds that had been rated triple-A, or risk-free, by copula-powered computer models would blow up. But no one was willing to stop the creation of CDOs, and the big investment banks happily kept on building more, drawing their correlation data from a period when real estate only went up.

"Everyone was pinning their hopes on house prices continuing to rise," says Kai Gilkes of the credit research firm CreditSights, who spent 10 years working at ratings agencies. "When they stopped rising, pretty much everyone was caught on the wrong side, because the sensitivity to house prices was huge. And there was just no getting around it. Why didn't rating agencies build in some cushion for this sensitivity to a house-price-depreciation scenario? Because if they had, they would have never rated a single mortgage-backed CDO."

Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?

They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem.

"The relationship between two assets can never be captured by a single scalar quantity," Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It's impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.

No one knew all of this better than David X. Li: "Very few people understand the essence of the model," he told The Wall Street Journal way back in fall 2005.

"Li can't be blamed," says Gilkes of CreditSights. After all, he just invented the model. Instead, we should blame the bankers who misinterpreted it. And even then, the real danger was created not because any given trader adopted it but because every trader did. In financial markets, everybody doing the same thing is the classic recipe for a bubble and inevitable bust.

Nassim Nicholas Taleb, hedge fund manager and author of The Black Swan, is particularly harsh when it comes to the copula. "People got very excited about the Gaussian copula because of its mathematical elegance, but the thing never worked," he says. "Co-association between securities is not measurable using correlation," because past history can never prepare you for that one day when everything goes south. "Anything that relies on correlation is charlatanism."

Li has been notably absent from the current debate over the causes of the crash. In fact, he is no longer even in the US. Last year, he moved to Beijing to head up the risk-management department of China International Capital Corporation. In a recent conversation, he seemed reluctant to discuss his paper and said he couldn't talk without permission from the PR department. In response to a subsequent request, CICC's press office sent an email saying that Li was no longer doing the kind of work he did in his previous job and, therefore, would not be speaking to the media.

In the world of finance, too many quants see only the numbers before them and forget about the concrete reality the figures are supposed to represent. They think they can model just a few years' worth of data and come up with probabilities for things that may happen only once every 10,000 years. Then people invest on the basis of those probabilities, without stopping to wonder whether the numbers make any sense at all.

As Li himself said of his own model: "The most dangerous part is when people believe everything coming out of it."