If one is going to use LinkedIn to analyze their industry’s trends, hearing “AI is the Death of SaaS” too many times may cause an over index on job scare paranoia. It’s akin to saying to someone in the auto industry that “EV is the death of Cars”. It’s an oxymoronic statement that makes no sense because just like EVs are Cars – just better, Agents are SaaS – just better. Just like the auto industry will have a long-term impact (ICE going towards EDU based propulsion) so will the software industry. No one and nothing is dying.
I feel the need to include a personal story here – just to prove this is not AI produced garbage.
My academic experience included a large brown box on which I learned to code COBOL. I think it was a Digital VAX. That machine had its dedicated room, but its terminals were in the computer lab. There was about 30 of them on one side and another 30 Intel 486 PCs on the other running MS-DOS and Windows 3.1 – on which I learned to code Basic, Pascal, and C. It was the early 90s. Windows 1.0 had been released in 1985 about 7 years earlier. Yes, by the time1991 rolled around – machines like the Digital VAX were not exactly cutting edge, but we needed to learn how to operate on one because businesses were still using them. My first job out of school 2 years later was coding C++ on Windows and my second job, which paid a lot more, 2 years after that was coding Fortran on a mainframe for a mid-sized investment firm. Computing devices like PCs were pretty much on every work desk in the US by the 1990s. The odd thing – I was using my PC at work primarily to run terminal emulation software to access a mainframe all the way up to December of 1999 when I left my third job and moved to San Francisco to be part of the dotcom revolution.
Why was I still accessing the mainframe on a terminal despite the capability of the Pentium processor before me? ERP was in full swing at that time too, where was it?
While they are arguably shorter than those in the 1990’s, organizational paradigm shifts still take time. The company I was working for was a large corporation with tens of thousands of humans working for it, with checks and controls that tested and worked, and regulatory responsibilities that make it not want to change because of the cost and risk of doing so.
There’s a tendency in tech discourse to focus on individual productivity. AI helps developers write code faster. My partner and I have coded hundreds and thousands of lines of code completing tasks in days that would take months without AI. Making software is only one thing, marketers now generate content instantly, and analysts crunch data in seconds. Humans are now hyper productive with AI – but only so much as the organizations they work for allow them to be.
Organizations Build Around Their Tech
One of the most overlooked truths in tech adoption is this: organizations don’t just use technology, they build themselves around it. Their workflows, hierarchies, compliance structures, and even their culture become deeply intertwined with the tools they rely on. And once that foundation is laid, change becomes slow, deliberate, and expensive. If you are selling a new AI solution you are selling a new way to do work, new types of persons to hire, and new organizational norms.
Take punch cards. Originally invented in 1804 to automate looms, they became the early backbone of automated number crunching when Herman Hollerith invented the tabulating machine in 1888[1]. Even as magnetic media and CRT terminals emerged in the 1950s and 60s, punch cards remained in use well into the 1980s. Why? Because organizations had built entire operational ecosystems around them from data storage (on the cards themselves) to job scheduling to physical infrastructure. The cost of change wasn’t just technical; it was organizational.
Even today, mainframes haven’t disappeared. Modern mainframes are essentially high-throughput servers optimized for transactional workloads. They’re still critical in industries like banking and insurance, not because they’re trendy, but because they’re reliable, secure, and deeply embedded, especially in finance systems which are still running some of the software I helped write in the 1990’s (at least I like to think so).
The same story plays out with on-prem software and SaaS. Salesforce may have declared “The End of Software” in 2000[2] (sound familiar?), but on-prem software didn’t vanish. It adapted. Many enterprises still run hybrid environments, mixing SaaS with on-prem systems. Similarly, cloud computing hasn’t eliminated on-prem data centers. It’s augmented them. Organizations choose what to move and what to keep based on risk, cost, control, and compliance.
This is the pattern: new technology doesn’t replace old tech outright, it coexists, competes, and jockeys for position. This dynamic eventually reshapes the organizational landscape. But that reshaping takes time. It requires rethinking not just tools, but a company’s processes, roles, and governance.
Which brings us to AI.
AI is revolutionizing individual productivity. But organizations aren’t just collections of individuals, they’re systems. Systems don’t change because one person is more productive. In fact they sometimes become less efficient because unless all humans become equally more productive, companies are only as fast as their slowest team and their slowest teammate. Even after all the “AI layoffs”, there will still be underutilized humans in organizations – companies cannot layoff the entire marketing department because they are waiting on the AI resistant finance team to pull together and approve their budgets.
For AI to truly transform business, it must address how it maintains organizational efficacy. That means:
The future of AI isn’t just about smarter tools — it’s about smarter organizations. And that’s a much harder problem to solve.
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[1] https://www.britannica.com/money/Herman-Hollerith
[2] https://www.provokemedia.com/latest/article/the-launch-of-salesforce-com-and-the-end-of-software
