The Economics of Software, Redux

Atanu Dey provides a tutorial. A summary:

Depending upon the size and complexity of what a software program does, you have to employ a bunch of programmers and have some computers for them to do their stuff on. That is the fixed cost of producing the software program. That fixed is high in the case of operating systems and huge database programs if you have to pay programmers fat salaries. If you got a whole bunch of people to collaborate and produce software for free, then you have low fixed costs. The latter is what mostly happened in the case of Linux while the former is what happens in a commercial shop like Microsoft.

Once you have produced the software program with whatever fixed costs — high, low, or intermediate — you then make a certain number of copies. Depending on the cost of the medium you use to distribute the software, your variable costs (and therefore the marginal costs of distribution) is determined. If you use punched cards or paper, marginal cost of distribution is high; if you use cds, it is low; it is lowest so far if you send it over electronically by wires or wirelessly. Because electronic distribution of software is so cheap over the internet, you can say that the marginal cost of production as well as distribution comes pretty close to zero.

Information goods are essentially different from material goods. That is so because of two reasons. First, the nature of information goods itself. And second, the cost of production of IGs. IGs have public goods characteristics, they have externalities, and have high fixed costs. All these are deviations from the conditions required for competitive markets. Thus markets will not deliver the social welfare maximizing outcome. All sorts of distortions will result such as the presense of monopolies. Monopolies, like everyone and his brother, maximize profits and since they have market power, they can charge whatever suits their fancy. So they can price an operating system at $300 a pop which is way above the marginal cost of production (exactly $0) and the marginal cost of distribution (nearly $0).

So is the economics of software essentially different from the economics of other goods? Not really. There are differences in their associated costs (fixed, marginal, average) and the ways in which they deviate from the conditions required for a competitive market. But the fact remains that there is nothing surprising about the way the market for software behaves if one were to understand the nature of software and the nature of markets. Like shoes and ships and sealing wax, they follow predictable pathways in the marketplace.

Sony’s Strategy

WSJ writes about how Ken Kutaragi, the creator of the PlayStation business, is helping remake the company’s electronics strategy:

Mr. Kutaragi’s vision was shaped by his success with the PlayStation, now a $7.5 billion-a-year business. It relies on data-processing and image-manipulating chips that only Sony makes. “Most of the intellectual creation in hardware will be concentrated in semiconductors,” says Mr. Kutaragi. “The product is just a cabinet, a costume.”

The success of Apple’s iPod is also a lesson. The iPod’s basic hardware, a hard disk for storing songs, is a commodity that any company can buy. But the look and feel created by Apple’s software, which makes it easy to build a music collection and play it in fun ways, is harder to match.

Sony’s struggle to remake itself mirrors that of other consumer-electronics makers. They all dread commoditization — the spread of standardized parts that makes it impossible for all but a handful of ruthlessly low-cost producers to survive. That is what happened in personal computers, with Dell Inc. the chief winner.

But Mr. Kutaragi’s strategy is a gamble. The first set of products produced under his reign — still available only in Japan — all take the radical step of marrying videogames with consumer electronics. That may confuse consumers. An early pet project of Mr. Kutaragi, a device that combines a videogame machine with a DVD recorder, has struggled in Japan, although Sony still plans to bring it to the U.S. Also, Microsoft and Intel are angling to control the software and semiconductors used in the living room.

Microsoft at 30

Forbes takes a look at Microsoft’s world. One of the stories discusses Microsoft’s small business initiatives:

Microsoft Business Solutions (MBS) is the second smallest, by sales, of seven operating units at the $37 billion company. But Microsoft will spend $10 billion over the next five years in the hope that the division can grow by more than a factor of ten, to $10 billion, in less than ten years. There are plenty of opportunities for the unit, but Microsoft might be hoping for too much.

The market that Microsoft is targeting is small and mid-sized businesses with up to 1,000 employees. Consider that SAP, the largest and most successful business applications company by far, has about 30,000 customers. Microsoft says it has over 350,000 small business customers now, and there are millions more in the universe that Microsoft is targeting. businesses.

Microsoft already has 8,000 partners, some of which resell its products and others that develop complimentary applications. “Microsoft builds channels better than anyone,” says Jim Shepherd, an analyst at AMR Research. “And the only way to succeed at the bottom end of the market is with volume.”

Paul Hamerman of Forrester Research agrees that Microsoft’s indirect sales channels give it an advantage but cautions against its $10 billion target. “They need to get to one billion first,” he says.

To be sure, Microsoft’s plans are not without big challenges. For starters, Microsoft sells four overlapping sets of business applications, and some say there is no clear distinction between them.

Tableau: Visualising Databases

Slashdot points to an article in the Seattle Post-Intelligencer:

Started as a government research project on the Stanford campus seven years ago, the team behind Tableau worked down the hall from Google founders Larry Page and Sergey Brin. In fact, the two companies share more than a common address, co-founder and CEO Christian Chabot said.

“We are like the sister company of Google in some sense, and I want to say that lightly so it is not misinterpreted,” Chabot said. “But Google was trying to make unstructured databases easy to use, and Tableau was founded to make structured databases easy to use.”

The difference is that Tableau’s software creates a graphical interface for “old-fashioned” databases such as Oracle, SQL Server and Excel while Google’s technology is built for unstructured data on the Internet, he said.

Creating a graphical system that allows people to easily sort through piles of corporate data is something that computer scientists have struggled with for years, Chabot said.

“Do a survey of the great unsolved problems in computer science and your research will show that just making databases easy to use is one thing we have not figured out yet,” he said. “We are swimming in data, yet no one can see what is going on.”

The company describes itself as a Visual Spreadsheet for databases.

Technology Hype Cycle

BBC News writes about tech’s roadmap:

On a curve of development, the Hype Cycle tries to predict where technologies are heading: into our pockets and living rooms, or into the Betamax graveyard.

It starts with the “technology trigger”, where a breakthrough or event generates publicity, exposing the gadget or technology to a wider audience.

A “peak of inflation” follows where great things are expected. Inevitably, this leads to the “trough of disillusionment”.

This is not a Middle Earth battle ground; it is where technology goes to wilt when it fails to deliver its promises.

As people start to learn more about the technology, it starts to struggle up the slope of enlightenment. The final stage is the “plateau of productivity” when it becomes mainstream.

The best example is the Apple iPod and the iTunes online store, which triggered an explosive growth in what used to be called “MP3 players”.

TECH TALK: Creating Options: A Flight Not Taken

During my recent US, I was trying to set up a meeting in Seattle while I was in San Francisco. There was a reasonable probability that the evening meeting would come through, but I would not know for certain until early the same morning. So, I waited till the confirmation came through before trying to book my ticket online. As it turned out, Southwest Airlines flight was fully booked. So, I chose Alaska Airlines, paying $100 more. Well, on reaching the airport, the 2:20 pm Alaska Airlines flight kept getting delayed. I finally had to cancel my meeting since there was now no way I would make it on time for my meeting. (For the record, the flight took off about four hours late.)

As I made my way back to the hotel from the airport, I kept thinking about what things I could have done differently. My error of judgment came from the fact that I did not book the ticket on Southwest Airlines earlier the fully refundable phrase was staring me in the face. I could have booked the ticket once I knew there was a reasonable possibility that Id have to travel. But I did not. That was a mistake. Of course, I could argue here that even the Southwest Airlines flight could have delayed. But knowing the on-time record of Southwest, that would have been a long shot. Almost deliberately, I had curtailed my own choices. I had limited my own maneuverability by not creating an option.

As I thought about it, I realised that a lot of life personal and professional is ensuring that we do not end up in a position where we do not have choices. Yet, for some reason, we tend to do just the opposite. We leave things till the last minute. We dont think through possible scenarios so we can anticipate changes in the environment. In other words, we make things difficult and limiting for ourselves. And when we are boxed in, we end up having to make sub-optimal decisions because we have no other choice. We rationalise by thinking that we could not have done anything differently as I initially tried to do: My flights getting delayed, and so I will not be able to come for the meeting. But what we dont always do is to trace back to the origins of the decision that we made and where it could have been corrected.

In the next couple columns, I will write about a few of my own experiences about creating options. As you read this series, think back about your own life and the options you have (or have not) created at different points of time. In as much that we would like to look to the future rather than the past, sometimes doing a What-If analysis can help throw up some interesting learnings, provided we open ourselves to it. It is always hard accepting that we have made mistakes or could have done things differently. But if we are prepared to go down that route and trace back to the roots of some of the decisions that we have made, we will find that we are better prepared to face the future and be smarter about making decisions by creating options.

Tomorrow: Personal Examples