Its not pretty, if you stop innovating software declines exponentially in value over time. Think about how much windows 3.1 or DOS is worth today. Who buys that? Its worth is zero. Windows 95 anyone? How about windows XP? Next question how much is Peoplesoft worth? Siebel? JD Edwards?
Well you can calculate it. http://ilpubs.stanford.edu:8090/862/1/worth43.pdf
Lets use good old Stanford's calulation on page 16 to see how the price drops over time.
5.2 Income from a software product over time
We consider two classes of software below, enterprise software, such as databases and
application tools built on top of them...
Accepting a constant expected price v1 allows us to rescale Figure 2. We see now how the rapid growth of software, especially initially, reduces the value of the initial IP contribution to the product.
The result is shown in Figure 3. One can argue that the first version contained all the truly original IP, so that the diminution should be less steep, but in Section 4.2 we determined that to realize income from that value much further work is required throughout the life of the product, generating the `New Code' shown in Figure 3. In Section 7 we present an alternative model, a model which considers the value of ongoing maintenance as well as the cost.
{look at link to see figure}
Maintaining Enterprise Software. The common strategy for providers of enterprise software is to commit themselves to deliver any further versions of the software to existing customers, as long as the annual maintenance fee is paid. Such a scheme is attractive to the customer, who can predict expenses into the future, and the vendor, who collects a steady income at low sales costs from efforts that are already required to gain additional customers. Typical rates charged customers for ongoing support are 15% of the original purchase price. Of that amount a small fraction goes into sales support, the effort to get the customer to upgrade to the new version and avoid the seller's obligation to fix problems in older versions. A larger fraction goes to routine call-center support.
The remainder of the support fees, amounting to 6 to 10% of the purchase price in every subsequent year, is available to the engineering staff for the types of maintenance presented in Section 3. That income will also support most improvements needed to attract new customers to an existing product line.
Oracle charges 22%... sweet Jesus Hallelujah! That's what keeps the lights on when you innovate absolute zero for a decade.
But what is the overall life span of a software product with out innovation? Skip a head to page 19..
A good fit to known software sales curves has been obtained using Erlang distributions [ChatfieldG:73]. That distribution is also controlled by the mean and variance of the data, but has a definite starting point when sales start. Computing the best matches to data yielded Erlang parameters from m=6 to m=20, but clustered at m=11 [Wiederhold:83].
At that value the distribution appears similar to the normal curves of Moore, but is foreshortened and has a maximum rate before its midpoint. We show in Figure 5 such curves for sales of about 50 000 units over a product's life, including distributions corresponding to small and large Erlang parameters. The areas under each curve represent total sales and should be equal up to year 9. At values of m close to 1 an Erlang distribution resembles a negative exponential - corresponding to ever-decreasing sales over time and implicitly a high value of novelty; at m=∞ it is a constant, corresponding to a one-time special sales promotion. Erlang curves have been widely used to size communication channels, and a variety of software is available to compute m for known means and variances.
For software it's best to start with the expected total sales and a sales horizon ending when annual sales are less than 10% of the best prior year, as done here for m = 12.
{See image in article}
Point here is that innovation is occurring in only two areas: Oracle Cloud and the Oracle database. Everything else has gotten the severe squeeeeeze from MH. So lets look at Siebel its revenue according to Wikipedia was 1.3 Billion in 2004. If we assume an inverse of Moores law 2^(x/24) and no innovation since its acquisition we can graph the equation.
With a simple moors law applied against revenue in millions. You can paste this in and see the revenue decline to zero (in millions) about 248 months or 20 years
https://www.desmos.com/calculator
So based on that I picked an Erlang distribution of 12 to 20... Not scientific.. But in general it starts to bottom out about 160 to 180 Months with a lower mid range number selected for the Erlang distribution and around 220 Months for a higher. I also noticed a nice correlation between the Erlang distribution and Plank Law: https://www.desmos.com/calculator/c9gtkau8ju
I am certain that someone has access to the sales numbers for Siebel. Oracle acquired Siebel in 2006 for around 5.8 billion. I like to start the decline curve before 2004... but if you pick Jan 2004 as a round guess...
So worse case 160 months is 13.3 years meaning that Siebel hit zero in revenue (sans support) in April of 2017. Don't think that's the case so we move up the ladder to 220 months and siebel is worthless (zero) in April of 2022. If that's the case then the curve shows around 216M in revenue (new sales) for Seibel this year. Who knows, that may be too high.
Whats the point? Point is aside from the Oracle Database... all the other Oracle acquisitions are seriously declining assets. Cloud has not generated revenue. What is keeping the lights on at Oracle? The primarily the Oracle database and the 22% support revenue stream.