Software companies, long regarded as some of the most durable and profitable businesses, faced an indiscriminate sell-off in early 2026, with investors referring to the moment as the “SaaSpocalypse.”
Behind this sell-off was the rapid emergence of powerful AI coding agents, such as Claude Code and Openclaw, which made headlines by demonstrating that AI agents can build, fix, and integrate complex software. This raised concerns that such powerful tools would create more competition in the software space, ultimately threatening the traditional SaaS business model. Investors started to question whether software moats were dead.
The market reaction revealed a deep misunderstanding about the software space. The market treated the entire software universe as a single, undifferentiated risk and failed to distinguish between companies that sell software, and those whose competitive advantages happen to be delivered via software. But where there is fear, there is also opportunity for those looking deeper into the sector.
Not a moat – software models and structural advantages
Being a revenue-generating software company does not constitute a high-quality business. Software is a gateway to a capital-light economic model that can deliver sustained high returns on invested capital, but only where specific conditions are met. These conditions include durable competitive advantages such as network effects, unique and hard-to-replicate data assets, or a proven enterprise-grade capability in implementation, customer support, and ongoing R&D to solve complex, industry-specific workflows.
What has determined the success of software has not been the model itself, but the structure of the industry. Most industries in which software companies operate exhibit an ‘oligopoly’ landscape, where there is one dominant market leader and smaller competitors that lack the resources to compete. The emergence of tools such as Claude Code and other LLMs does not materially alter this dynamic for these leading software companies. What it does is accelerate the commoditisation of many SAAS workflow applications.
The market was right to reprice the weakly differentiated software companies, but wrong to reprice those structurally advantaged. The fear that set the sell-off in motion was largely based upon the idea that software businesses are primarily constrained by development hours, and that removing this constraint via AI levels the playing field.
However, the reality is somewhat different. Development hours are not the constraint for competitive differentiation and market leadership. A well-established software leader has built layers of complexity over years, and sometimes even decades, that an AI code cannot shortcut.
Unravelling the layers of software
WiseTech Global (ASX:WTC) is a clear example of the layers of complexity that have been built into software. Its cloud-based logistics executive platform, CargoWise, has been designed to manage the complexities of global supply chains, including freight forwarding, customs clearance, warehousing, and transportation. The fear that AI agents could enable a well-funded startup or ambitious competitor to replicate CargoWise's functionality is understandable, however from a fundamental standpoint, there is a misunderstanding of what makes CargoWise’s competitive advantage a tough moat to bridge.
The platform operates across 170+ countries and must maintain live, certified integrations with the customs authorities, trade compliance databases, and tariff systems of each jurisdiction. These integrations are created through an Application Programming Interface (API), which is a set of rules and protocols allowing different software applications to communicate and exchange data securely. These APIs are not static and are subject to regulatory change, bilateral trade agreements, and local government IT modernisation cycles.
Achieving and maintaining certified integrations requires years of relationship building, in-country expertise, and a track record of audit-grade reliability. Can an AI agent write an API? The simple answer is yes. But can it negotiate a certification pathway with the Australian Border Force or anticipate the downstream impact of a reclassification in EU harmonised tariff codes? The answer to both is no. Each connection is a bespoke integration requiring government certification as an approved technology provider and must be continuously maintained as regulations change. This capability requires not just code, but institutional knowledge, government relationships, and domain expertise accumulated since the 1990s.
Today, WiseTech processes approximately 74 million compliance updates annually and maintains 5.6 million product classification records. A startup could build one of these country connections, but building and maintaining thirty or more simultaneously, in real-time, is a multi-year and very expensive effort that requires customer scale to fund and validate.
Another layer of complexity with software lies with enterprise data. Software companies that have been operational for many years have accumulated decades of customer-generated data, such as transaction histories, workflow configurations and industry benchmarks. This data becomes deeply embedded within the software product and improves its functionality over time, which is impossible for entrants to replicate no matter the amount of engineering resources and sophisticated AI models put to use. The absence of historical production data creates a significant ‘cold start’ problem for new entrants.
Software switching costs are another barrier to entry. Enterprise software implemented within regulated industries such as financial services, logistics, mining, utilities and government, are rarely off-the-shelf products. Implementation of software encompasses deep integration with other systems and software as well as customisation to support the customer’s operational workflows. On top of this, software users often have internal teams deployed to provide support that have deep domain expertise. Switching costs are not merely contractual, they are operational. The implementation of a new software often requires retraining staff, reconfiguring workflows, reintegrating with other systems and securely transferring historical data.
What these layers highlight is not the irrelevance of AI coding tools, but the market’s ability to price in the destruction of structural moats on the basis that code can now be written faster without taking into account the other barriers to entry.
What lies ahead for software?
AI coding tools will meaningfully reduce development costs and timeframes for all players. Even for market leaders, AI solutions will be a tailwind for software companies using them to reduce customer labour costs and monetise the value creation effectively. Software companies with structural differentiation such as proprietary data, deep customer integrations, and domain expertise are best positioned to build and deliver these solutions. Meanwhile, software companies without structural differentiation will find that faster code merely accelerates price competition.
What the sell-off of software earlier in the year showed was the clear misunderstanding by investors of what makes a software business great.
Investors who can look at the software universe and differentiate between companies that sell software, and those whose competitive advantages happen to be delivered via software, will be able to find and invest in good opportunities at discounted valuations.
AI is an accelerant, not a threat, and where there is fear, there is opportunity. But it is about knowing where to look.
Damon Callaghan, Partner, Investments at ECP Asset Management.
This article has been prepared by ECP Asset Management Pty Ltd (ECP). ECP is a funds management firm based in Sydney, Australia. This material has been prepared for informational purposes only and is not intended to provide and should not be relied on for financial advice. ABN 26 158 827 582, AFSL 421704, CAR 44198.