What’s behind the ‘SaaS-pocalypse’?

Ever since AI entered the public consciousness through ChatGPT, investors have piled into technology stocks, excited about its prospects as a genuine life-changing innovation.

That euphoria was interrupted last year when traders started to consider how AI may affect software companies, a core component of the tech sector.

Those fears were magnified at the start of 2026, when the US-based Anthropic rolled out releases allowing users to communicate with their computers in natural language to conduct complex tasks like data analysis and expense tracking.

This is considered to be hugely disruptive to expensive SaaS applications that require the user to learn the language of the software.

The potential for disruption is clear, with some software at risk of becoming obsolete in a similar way to how digital photography destroyed Kodak and touchscreens decimated Blackberry.

Investors have also raised concerns over the future of the “per seat” charging model, a common billing model in the SaaS industry whereby a software company charges fees for every individual who has access to it.

As Morningstar points out, in an AI-enhanced future, “if one person can now do the work of two, seat counts fall”.

Australia’s technology index, which contains several big-name software companies including Xero and WiseTech, is down about 17% since the start of the year, and more than 25% over six months.

The unease has captured other sectors, with investors considering whether portfolio construction, tax planning, insurance calculations and data analytics could be AI automated, making specialist firms in those fields redundant.

Are the concerns overblown?

Luke McMillan, the head of research at Sydney-based Ophir Asset Management, says investors have “shot first and asked questions later” by selling off SaaS businesses en masse.

“The next stage that we’re getting to is actually understanding which businesses will be negatively impacted by this,” he says.

Investment firms talk about “economic moats”, which refer to the structures a company has in place to protect its profits against rivals and market disruptions.

McMillan says one of those moats is when a software company uses proprietary data that AI cannot access, as opposed to software that draws from public sources that could be easily replicated.

“There’ll be some that actually have some moats that protect them from what these AI tools can do, and in fact, they’ll integrate AI into their businesses making them even better,” he says.

Lochlan Halloway, the equity market strategist at Morningstar, says while the “rush for the exit” was a kneejerk response, there’s also a risk of investors underplaying the AI threat.

“In this case, there will be winners and losers out of this,” he says.

Halloway says companies with unique data, complex systems that are hard to replicate and software that connects multiple parties will be better protected against disruption.

“We don’t want to dismiss the risks that AI poses to the software-as-a-service business model, but those are the things we’re looking for in trying to help identify which companies are more likely to stave off this threat,” he says.

What happens next?

The AI era and second term of Donald Trump have fostered a period of high volatility in global markets, with traders swinging between bouts of optimism and concerns over trade wars and a tech bubble.

The narrative-driven movements, where stories determine investment decisions, differs from historical periods when stock movements are more closely aligned with company earnings.

The “SaaS-pocalypse”, AI boom, “sell America” and “Taco” trades – the latter referring to the idea that “Trump Always Chickens Out” when facing a tariff-induced market backlash – are all examples of narratives.

Investment firms expect markets will eventually work out how to price companies in an AI world, in the same way they did after the tech boom and bust of the late 1990s and early 2000s.

Halloway points out the apparent contradiction between fears of a tech bubble and collapsing share prices of some software companies, given the former is based on the idea that AI’s promises will be unfulfilled, and the latter relies on AI being a major disrupting force. 

“It seems like markets found a reason to be worried about too little AI and too much AI at the same time,” he says.