SaaSpocalypse: Navigating the Crisis and Discovering the Future of SaaS
In recent months, the software sector has undergone a sharp correction, to the extent that talk of a ‘SaaSpocalypse’ has begun to emerge in the United States. This term describes a period of significant decline in the market value of SaaS companies. This is not an isolated event or one linked to individual financial reports, but appears to signal a more profound shift in the way the market values these companies. Around $300 billion in market value vanished in a single day for software and data companies, a sign that something structural has changed..
One of the main reasons for this reversal is the rise of generative artificial intelligence, particularly AI agents. These new tools are beginning to erode entire categories of horizontal software. Platforms that were once considered unique and difficult to replicate now risk becoming mere features that can be reproduced in-house with relatively little effort and cost. This phenomenon transforms what was a defensible competitive advantage into an easily imitable feature.
The Software-as-a-Service (SaaS) business model has been a safe haven for investment for years. However, perceptions are changing. The idea that a software company can be easily replicated by an AI model is leading to a reassessment of its competitive defences. This does not mean that software is dead, but that the criteria for assessing its value are undergoing a radical transformation. Companies offering services, especially those that can be transformed into AI-enabled platforms, are gaining new appeal [522d]. The distinction between what is defensible and what is replicable is becoming increasingly important for understanding where the real value lies in today’s technology market.
New strategic paradigms: from investment funds
Sequoia Capital’s framework: software companies disguised as service providers
In recent months, the focus of major funds has shifted towards a new interpretation of the software business. The central idea, put forward by Sequoia Capital, is that the next giant in the sector may not look like a classic software house at all, but rather a services company with a very strong technological core. Essentially, the real competitive advantage arises when software is no longer just a platform to be sold, but the silent engine that enables services to become faster, more precise and harder to copy. Today, those who offer only tools risk being overtaken by those who manage to integrate these very tools into the operational process that delivers direct value to the customer. This approach changes the way we view companies and forces us to rethink intangible priorities: differentiation is no longer just about technology, but above all about the ability to transform that technology into a concrete service.
Distinguishing between activities with a predominantly intelligence- and judgement-based content
A key aspect of this transformation concerns the nature of the activities a company carries out. A clear distinction is now made between what is based on rules, calculations and automation (intelligence), and what, on the other hand, requires personal experience, sensitivity and decision-making (judgement). Where intelligence-based activities dominate, automation via AI is advancing rapidly, making those roles more vulnerable to replacement. On the other hand, tasks requiring judgement remain in the hands of people with experience and decision-making capabilities that machines cannot replicate. This new way of segmenting activities suggests which businesses will be best protected from the changes ahead, with platforms combining data and human expertise better positioned than models of the past.
The operational value of this distinction for asset valuation
For those involved in business valuation, this new perspective demands significant changes. Describing a business solely through numbers or traditional benchmarks is no longer enough: it is necessary to demonstrate concretely how resistant a business is to automation, and how much of its value depends on unique data or vertical workflows that are difficult to replicate. Investment funds are paying much closer attention to these distinctions than in the past. The yardstick, even for SMEs looking to evolving foreign models, is becoming the ability to produce value that is difficult to replace. Even for those operating within our national context, such as in Hungary where economic changes are creating new opportunities for Italian companies, it is becoming crucial to understand which assets can withstand the impact of change and which, on the other hand, risk being quickly rendered obsolete opportunities for Italian companies.
The redefinition of value in software
Market segmentation: horizontal software vs vertical platforms
The software market is changing, and it is no longer as straightforward as it once was to define what is truly valuable. What was once considered a solid sector, horizontal software, now finds itself having to contend with generative artificial intelligence. Platforms that seemed untouchable, capable of serving just about everyone, now risk being overtaken by AI solutions that do similar things, but in a more targeted way. The real difference today is made by vertical platforms, those built for specific sectors, which have learnt to integrate complex rules and manage processes that require great attention to detail.
The importance of proprietary data and compliance logic
In this new landscape, the data a company possesses and how it manages it becomes gold. It is not just about having lots of data, but about having unique data and knowing how to use it to comply with regulations. Platforms that manage to incorporate compliance logic directly into their operations – think of sectors such as healthcare or finance – gain a significant advantage. This makes their service more secure and harder to copy, creating a natural barrier against competition. The software market is clearly showing this trend.
The polarisation of market figures and its consequences
What we are seeing is an increasingly clear divide in financial results. On the one hand, horizontal software companies that fail to adapt risk seeing their figures deteriorate. On the other hand, vertical platforms, with solid data and compliant processes, tend to perform better, attracting investment and maintaining higher valuations. For those involved in valuing companies or managing mergers and acquisitions, ignoring this divide means risking making incorrect estimates. It is essential to understand whether a company is at risk of being replaced by AI or whether it has built solid defences, such as defensible proprietary data.
The evolution of due diligence in the AI era
The need for AI due diligence
Traditional due diligence, which focused primarily on the numbers and the business structure, is no longer sufficient. With the advent of artificial intelligence, particularly agentic AI, the way we evaluate companies must change. We must start asking ourselves whether software, once considered robust, can be easily replicated or surpassed by an AI system. This means that buyers now carry out a proper ‘AI due diligence’ as a structured part of the acquisition process. It involves understanding whether the asset in question is resistant to automation or possesses proprietary data that makes it unique and difficult to copy. It is a crucial step in understanding a company’s true value today.
Assessing resistance to automation and the defensibility of proprietary data
When valuing a company, it is important to understand how difficult it is for artificial intelligence to replicate what it does. If a software solution is based on highly standardised and easily codifiable processes, it may be at risk. On the other hand, if a company has collected unique data and uses it to offer a personalised service or to comply with specific regulations, this data becomes a genuine protective shield. The real defence today lies in the ability to demonstrate that the company has actively built up proprietary data and has codified its processes in such a way as to make them measurable and difficult to replicate. This is no longer merely an option, but a key factor that directly influences a company’s valuation. It is a bit like building a moat around one’s digital castle.
Integrating AI due diligence into M&A processes
Integrating this new form of due diligence into merger and acquisition (M&A) processes has become essential. It is no longer a superficial check, but an in-depth analysis that must begin right from the early stages of preparing a transaction. For companies most exposed to the risk of being replaced by AI, it is necessary to present a concrete and measurable plan for transformation. It is not enough to promise future investments in AI; one must demonstrate that one is already actively working to integrate these technologies and build proprietary data. The window of opportunity to position oneself as an ‘AI-native’ company and secure a valuation premium is limited, estimated by international operators to be between 12 and 24 months. After this period, the adoption of AI will become a necessary condition, no longer a distinguishing feature, and companies that have not adapted could face a devaluation. For entrepreneurs considering a sale or opening up their capital, planning this timing becomes a strategic lever to maximise value. Furthermore, the uncritical use of aggregate market multiples, without considering the degree of AI integration and the quality of proprietary data, can lead to distorted valuations, both overvalued and undervalued. Valuing companies today requires a more nuanced view.
Services are becoming attractive again: which ones and why
Services that can be transformed into AI-enabled platforms
We often hear that services are making a comeback, and there is certainly some truth in that. But not all services are created equal in the eyes of today’s investors. The ones really attracting attention are those that can be transformed into fully-fledged platforms based on artificial intelligence. Think of services that already have established distribution, well-established outsourcing channels and expenditure budgets already defined as external costs. This is somewhat what Sequoia Capital’s framework suggests: replacing an outsourcing contract with a supplier that uses AI natively is a simple change of supplier, not an internal revolution. The return on investment is immediate.
The distinction between assets with a structural moat and replicable assets
The real crux of the matter, however, is that the very phenomenon making these services more attractive is penalising those that fail to adapt. Consultancy firms based on repetitive tasks, such as basic compliance, legal research or small business accounting, are viewed with greater caution. The reason is simple: the risk that AI could replicate these activities is already factored in by buyers, leading to a lower valuation. The dividing line is no longer between software and services, but between assets that have strong protection, such as proprietary data or specific regulatory requirements, and assets that can instead be easily copied by an artificial intelligence system.
The impact on Italian SMEs and the national context
For Italian small and medium-sized enterprises, which make up the bulk of the economic fabric, this distinction is fundamental. Italy, with productivity growth that has not been exceptional in recent years, becomes an interesting case study for observing how artificial intelligence will influence productivity, profits and the structure of work. The most recent analyses, such as those published by the Bank of Italy, provide concrete data on the national context. The Italian market may have its own dynamics, but the general trend is clear and must be kept under close watch strategic for the future.
Navigating the transition: operational implications for businesses
Demonstrating AI integration and the creation of proprietary data
For anyone running a business, especially in the run-up to extraordinary transactions such as disposals or capital openings, it is essential to understand that market value is measured differently today. It is no longer enough to talk about how innovative one’s software is; one must demonstrate concretely how artificial intelligence is already an integral part of business processes. This means showing which unique datasets have been collected and how workflows have been reorganised. The aim is to shift the focus from what is easily automatable to what requires human judgement, making the company more resilient to technological changes. The ability to quantify the impact of AI on financial results becomes a key factor in securing better valuations. This approach is essential for anyone wishing to understand the impact of digital transformation on business structures.
The window of opportunity for AI-native positioning
There is a limited window of opportunity, estimated at between 12 and 24 months, during which positioning oneself as an AI-native company offers a significant competitive advantage. Once this period has passed, AI integration will become a necessary condition for operating, rather than a distinctive feature that increases value. For entrepreneurs, strategically planning when and how to implement these technologies can become an important driver of value. Ignoring this window of opportunity means risking seeing the value of one’s company undervalued compared to more agile competitors. The integration of AI is, in fact, reshaping the software landscape, with direct impacts on how companies operate and manage their software assets.
Valuation discipline and the critical use of multiples
The indiscriminate use of aggregate valuation multiples, without considering the degree of AI integration, the risk of substitution or the quality of proprietary data, can lead to erroneous estimates. A more rigorous valuation discipline is required, involving market segmentation and the careful selection of comparables. This does not mean abandoning traditional methods, but rather integrating them with a thorough examination of the business model’s defensibility within the current technological context. Those supporting companies through these phases must be able to make this new value framework intelligible, helping entrepreneurs to build and measure the resilience of their business.
The tech job market is being reshaped
The growing demand for highly specialised professionals
The job landscape in the tech sector is undergoing a significant transformation. This is no longer simply a general expansion, but a genuine reorganisation. We are witnessing a significant increase in demand for professionals with highly specific skills, particularly in fields related to artificial intelligence, cybersecurity and advanced data analysis. This trend indicates that companies are actively seeking talent capable of managing and developing complex technologies, rather than profiles with a more generalist background. The Italian market, despite its specific characteristics, is following this global trend, with a digital sector that exceeded €54 billion in 2023, showing steady growth [1945].
The declining importance of junior generalist roles
In tandem with the rise of specialised roles, there has been a decline in the importance of junior and generalist positions. Companies tend to invest less in positions requiring basic and broader skills, preferring to focus on professionals capable of delivering immediate and significant added value. This does not signify an absolute decline in opportunities for recent graduates, but rather a redefinition of the expectations and skills required to access key roles. The skills scarcity curve is shifting, placing greater value on experience and specialisation.
The redefinition of the skills scarcity curve
In summary, the tech job market is becoming polarised. On the one hand, there is strong demand for experts in AI, data science and cybersecurity – roles that are becoming increasingly rare and, consequently, more sought-after. On the other hand, roles that can be easily automated or that require less specific skills are seeing their importance and bargaining power diminish. Companies that can adapt to this new reality, by investing in continuous training and the acquisition of specialised talent, will be best placed to tackle future challenges and seize emerging opportunities in a rapidly evolving sector.
Frequently Asked Questions
What is the much-talked-about “SaaSpocalypse”?
Imagine that the world of software – the software we use every day for work – has been going through a bit of a rough patch. The “SaaSpocalypse” is a term coined to describe a time when companies selling these programmes online (SaaS providers) saw their value plummet very quickly. This happened because it became clear that artificial intelligence (AI) could render many of these programmes obsolete, transforming them from unique and valuable assets into tools that are easy to copy.
Why is artificial intelligence changing the world of software?
Think of AI as a super-intelligent assistant. Previously, to do certain things on a computer, you needed a special programme. Now, AI is so good that it can learn to do those same things, sometimes even better and faster. This means that programmes that were once considered ‘special’ and expensive could become less important because AI can do a similar job, or even integrate into other tools much more easily.
What are “AI-enabled services” and why are they back in vogue?
“AI-enabled services” are jobs or activities that use artificial intelligence to be done better. For example, instead of paying someone to write a text, you could use a service that, thanks to AI, writes texts quickly and well. These services have become interesting again because, just as AI improves the service, they make the company offering it stronger and harder to beat. It’s as if AI gives these jobs an extra edge.
What does it mean that the tech job market is changing?
It means that not all roles in the tech sector are in equal demand. There is much greater demand for highly skilled professionals who know how to use AI effectively, manage complex data or create advanced systems. Conversely, simpler or more general roles that do not require highly specific skills may be less sought after because AI can perform them. It is a bit as if the market were increasingly looking for ‘specialists’ and fewer ‘jack-of-all-trades’.
How can companies prepare for these changes?
Companies must demonstrate that they know how to use AI and that they possess valuable data that makes them unique. It is not enough to say ‘we use AI’; they must show how they actually use it to improve their services and products. Furthermore, it is important to understand that there is a limited window of opportunity, perhaps a year or two, to position themselves as ‘AI-native’ companies, i.e. those born with AI in their DNA. Those who do not act quickly risk being left behind.
What is ‘proprietary data’ and why is it so important now?
‘Proprietary data’ is all that unique information a company collects and possesses, which no one else has. Think of a shop’s customer data, or the results of a pharmaceutical company’s trials. This data has become extremely important because AI learns and improves thanks to data. If a company has unique data and knows how to use it effectively with AI, it creates a huge competitive advantage, making its services more effective and harder for competitors to copy.