Ninety-five percent of what’s in it now wasn’t there 30 years ago. The core principles of the system have stood the test of time extremely well, and I’m pleased to say that almost any Version 1 program from 1988 will still run in Version 12 today (something that is very rare in the computing world). The core symbolic programming paradigm of Mathematica was also already there 30 years ago, and was broadly applicable from the very beginning. But in the intervening years, we’ve dramatically broadened and deepened the coverage of mathematical computations. We’ve also expanded into a great many other areas, to the extent that mathematical computation is now perhaps only 10% of what the system does. We’re also dealing with multiparadigm data science, machine learning, all kinds of visualization, text computation, graphs and networks, image computation, geometry, audio computation, knowledge representation, and so on. Another major thing is that the program incorporates a huge amount of built-in real-world data, about chemicals or particles or planets—or countries, movies, and companies. This is the same data that powers Wolfram|Alpha, and which in turn powers intelligent assistants like Apple’s Siri and Amazon’s Alexa.

Thirty years ago, we had already invented our notebook interface. Today that interface is considerably more developed, and it also runs in the cloud, so people can publish computable documents directly on the web. We’ve done a lot of work over the past 30 years, and the applications of Mathematica have dramatically expanded. Whether it’s being used as an embedded part of some robot or experimental data system, or for physics education with real-world data, or for the latest high-performance computation, there are things routinely done with Mathematica today that wouldn’t have been thinkable 30 years ago.