Let's walk through the history of software and map where it's going.
The 1980s was really the first decade when software (and computers) became ubiquitous in the office. Software was stored (and purchased) on a physical floppy disk. There was no such thing as "downloading." Software was on-premise, and it did one thing well.
Examples: WordPerfect, AutoCAD and Lotus 1-2-3.
The delivery medium of software changed -- it could be downloaded to a hard drive, but it remained on-premise. The software resided exclusively on the machine it was installed on. But the software itself was more powerful. Instead of doing one thing well, professionals could buy "suites" of software that did many things.
Examples: Microsoft Office Suite (Word, Excel PowerPoint), Adobe Suite (Photoshop, Illustrator)
Enter: Software-as-a-Service (SaaS). SaaS meant nobody had to download anything -- but most SaaS in the 2000s followed the concept of software in the 80s, doing just one thing well. You'd subscribe to one product for your contact Rolodex, one for managing the contact relationship and one for sending emails to them. Eventually, a typical business might have needed a software stack of 15+ different solutions to operate effectively. A lot of companies still operate this way.
Examples: Salesforce, Netsuite, Constant Contact.
NOTE: 2005 is when the mobile app ecosystem started to take off, with software for early smartphones, and eventually Apple's App Store and Google's Play marketplace. They followed the software trends of the 80s as well, doing one thing well. That's still largely true today.
Following the trend from the 80s and 90s, the 2010s is when the software stack in a business began to consolidate. Instead of making 11 software decisions, companies only needed to make 3 or 4. This is pretty standard on the marketing stack, with software like Marketo and Hubspot owning more and more of the stack. This is where we're seeing a lot of startup opportunity -- software that looks at underserved parts of the stack and consolidates. Localist is firmly in this camp, consolidating the "event software" stack.
Examples: Marketo, Hubspot, Salesforce, Squarespace, Zenefits
NOTE: 2015 is when "app fatigue" really set in, where convincing a user to download "yet another" app reached a breaking point.
2018-2020 - Transitioning
Desktop apps are where "work gets done," where mobile apps give you the essentials you need on the go. Both are coalescing around data. Software companies are becoming data companies. Their value depends entirely on the value of their data and what can be done with it, not the features of the software itself. What can those features do with the data?
The next decade will be about SaaS + Services. Nobody buys software for its features anymore. They buy it for what it delivers. Software creators will have to bring domain expertise to the table in addition to raw engineering might, or UX design. Companies will buy software that includes human domain experts to deliver them what they’re ultimately looking for: more leads, retained customers, etc.
The winners in this space will be the ones who have accumulated the most data over the previous decade. The more data they have, the more opinions they have about best practices, what the most effective solutions to problems are, etc. That’s powerful.
We're already seeing this with Hubspot, who will build branded landing pages for the customer, or Salesforce, who will provide an expert to optimize your CRM database for the customer, all baked into the monthly subscription fee.
We’re doing this too, with our Support Bold product -- essentially delivering a successful calendar without the customer having to lift a finger.
The 2020s used human experts to gather enough "soft" data to train AI appropriately. AI then replaces these human experts, driving higher profitability in the software companies that employ it (they no longer need to hire and train experts -- the AI layer, applied to the data, provides all the domain expertise these companies need. The winners in these spaced need to have started collecting data over the previous fifteen years, and adopted AI/ML/DL technologies early to appropriately adapt their solutions to take advantage.