The post-SaaS era: The end of rental software? How generative AI radically reduces IT costs – from “as-a-service” to “as-you-own”

From tenant to owner: Why we will soon own our software again

The end of the subscription economy: Why generative AI is shattering the foundation of "Software-as-a-Service".

For over two decades, an unwritten rule prevailed in the digital economy: software is not bought, but rented. The "Software-as-a-Service" (SaaS) model promised companies flexibility and providers like Salesforce, HubSpot, and Adobe fantastic margins through endless subscription fees. But in 2024, massive price corrections at former stock market darlings show that this golden age is beginning to crack. This is not just a cyclical market downturn, but the harbinger of a fundamental structural change.

The reason for this upheaval is the rapid rise of generative artificial intelligence. While SaaS is designed to rent standardized solutions to millions of users, AI now enables the exact opposite: the customized creation of software "on demand." Why should companies continue to pay expensive monthly fees for bloated feature packages when they can generate their own lean tools in seconds using LLMs (Large Language Models)?

We are at the beginning of the "post-SaaS era." In this new phase, software is transforming from a service back into a proprietary asset. The focus is shifting from centralized platforms to decentralized, AI-driven in-house development. This promises not only drastically reduced IT costs and greater independence from tech monopolies, but also forces the entire capital market to reassess what digital value creation means.

The following analysis, in 17 points, highlights how this break in the software paradigm will change markets, why "Digital Ownership" is experiencing a renaissance, and what strategies companies now need to develop to survive in a world where software is no longer subscribed to but generated.

The break in the software paradigm

Over the past two decades, Software-as-a-Service (SaaS) has dominated the digital business world like almost no other model. It promised predictable revenues for providers, agile integration for users, and a democratization of advanced software features. However, since 2024, there have been increasing signs that this model is reaching its economic and structural limits. Stock performances such as those of HubSpot (-45% YTD), Monday.com (-33% YTD), and Salesforce (-20% YTD) serve as indicators of a deeper shift process, not merely cyclical market corrections.

The reasons are multifaceted. The SaaS model thrives on recurring fees, high gross margins, and economies of scale through centralized infrastructure. However, these very core elements are increasingly under pressure due to advances in generative artificial intelligence, automated development, and local computing capabilities. Companies are beginning to question whether they should continue paying rent for software that can be generated or customized using AI tools.

The economic recipe for success of SaaS – and its Achilles heel

SaaS emerged as an evolutionary response to the inefficiency of traditional licensed software. Instead of high upfront costs and complicated maintenance fees, a subscription model was established, offering regular updates, cloud access, and user-friendly scalability. This model fueled massive market capitalization: Salesforce, Adobe, Atlassian, and ServiceNow achieved margins that previously could only be explained by platform network effects.

However, the economic advantage – the "subscription flywheel" – also carries risks. Providers depend on constantly adding new features to justify price increases and secure customer loyalty. At the same time, price pressure is increasing: Almost all market-leading SaaS companies have doubled their CAC (Customer Acquisition Costs) in the last five years, while net retention rates are declining. This means the model is mature, but increasingly costly and saturated.

AI-powered software generation could expose this structural weakness – much like SaaS once displaced the classic licensing model.

The rise of the “generative production economy”

Since around 2023, a new software logic has been emerging: AI-supported "on-demand generation" instead of centralized deployment. Foundation models like GPT-4, Claude, and open-source systems like Mistral or Llama 3.2 enable autonomous code generation, data structuring, user interface design, and integration into enterprise infrastructures with minimal human input.

For example, a medium-sized company can now use generative AI to specify, generate, and deploy an internal CRM system within a few hours – fully integrated into ERP and communication systems, without an external SaaS subscription. This transformation has profound economic implications.

Value creation is shifting from license and service fees to one-time, targeted generation. Software is once again becoming a capital asset – something owned by the company rather than rented. The economic core of this paradigm shift lies in the reduction of transaction costs, the elimination of centralized pricing, and the drastic individualization of digital tools.

The structural cost advantage of personalized software

The traditional SaaS model is based on average users: it offers uniform feature sets for a broad target group. This inevitably leads to complexity, overhead, and functional bloat. Companies often pay for modules they never use, while essential customizations are only possible via expensive enterprise tiers or integrations.

AI-based software generation solves precisely this problem. Systems analyze specific use cases, business processes, and data structures, and then generate customized tools without unnecessary features. This results in digitally "lightweight" systems with higher performance, lower dependencies, and better governance.

From an economic perspective, this is the key: If companies only pay once per application, the Customer Lifetime Value (CLV) of traditional SaaS providers is drastically reduced. At the same time, new margin models emerge – for example, for maintenance, training, and local computing provision – which, however, follow entirely different profit structures.

From “Software Stack” to “Software Stream”

Traditional IT architecture follows a layered model: Infrastructure, Platform, Application. Each layer costs money and requires management. SaaS positioned itself in the application layer, abstracting complexity and ensuring a steady cash flow through subscription structures.

In the post-SaaS world, these layers merge. Generative AI not only generates code but also dynamically orchestrates infrastructure (e.g., AWS, Azure, on-premises servers). Applications are no longer installed but synthesized as needed. The idea of ​​a company maintaining fixed software contracts seems anachronistic in this scenario.

The "software stream" refers to fluid, situationally generated tools that emerge from data and models – short-lived, but precisely optimized for a specific purpose. This transience contradicts the traditional thinking of IT departments, but reduces the total cost of ownership (TCO) in the long run.

Impact on corporate strategies and market mechanisms

When software reverts to being a proprietary product, the balance of power between providers and users shifts. Companies regain control over its design, but simultaneously lose access to the pooled innovation that SaaS enabled through its collective database.

For SaaS providers, this means they need to reposition themselves – from product orchestrators to platform orchestrators. In the future, they will no longer sell software, but rather the ability to configure, maintain, and secure AI-based software generators. The competition is therefore shifting from feature complexity to model expertise and data sovereignty.

On the market side, this development is leading to a unbundling of established tech monopolies. Many small AI models or specialized open-source systems are taking over functions that were previously centralized. This lowers barriers to entry but also creates more fragmented ecosystems. Network effects remain relevant—but more so in the data and model space than at the level of concrete applications.

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AI as a production factor in the software economy

Post-SaaS Revolution: How AI Generators Will Redefine Software Ownership and Business Models by 2035

Economists are increasingly speaking of "knowledge automation capital," a new form of capital that systematically reproduces knowledge. AI is becoming a factor of production that does not replace labor but exponentially increases intellectual capacity. In this sense, software generation is a form of automating knowledge itself.

This means that companies are no longer primarily investing in IT staff, but in AI expertise and data networks. In the future, a company's value will be measured more by its ability to translate internal processes into software using machine intelligence. As a result, the traditional IT stack is losing its central role – and the boundary between software development and business strategy is blurring.

The role of the open-source movement

Open source is the invisible architectural foundation of this new phase. Models like Llama, Mistral, and Falcon allow local control over code generation processes, thus dramatically lowering barriers to entry. Community innovation is increasingly replacing proprietary frameworks that were embedded in SaaS dependencies.

From an economic perspective, this creates a paradox: Open source enables massive value creation without directly generating revenue. At the same time, free-to-own systems force established providers to focus on service quality, security architecture, and integration – aspects that were previously peripheral but now constitute key differentiators.

This also shifts the focus of competition: away from functional diversity and towards a trust-based economy. AI-generated software will only become suitable for mass adoption if users can understand, verify, and control its reproduction behavior.

Infrastructure and energy economics of the AI ​​age

An often underestimated aspect: the infrastructure costs of this new world order. While SaaS providers have benefited from centralized data centers, AI generation is leading to new energy dynamics.

Training large models remains resource-intensive, but inference—the application of these models—is becoming increasingly efficient. Local computing power (edge ​​computing) and personalized models reduce bandwidth requirements, increase data privacy, and lower costs.

This could lead to the formation of new regional value chains: local data centers optimized for medium-sized businesses, specialized AI compilers, automated testing systems, and energy partnerships. Economically, this would create a decentralized AI production sector, comparable to the industrial revolution of the 1880s, when electricity generation was localized and democratized.

Labor markets and shifts in skills

The shift from SaaS to generative software production also has massive labor market policy consequences.

– Traditional IT administration roles are becoming less important as infrastructure scales automatically.
– Software developers are transitioning from code writers to process designers and quality managers for generative systems.
– Business analysts are gaining importance as their subject matter expertise can be directly translated into generative prompts.

This creates a hybrid job market between the technical domain and strategic thinking. Education systems focused on linear programming training will have to adapt: ​​away from syntax and towards systems understanding, ethics, monitoring, and prompt architecture.

Capital markets and valuation logic

Capital markets are already beginning to price in this shift. SaaS companies are losing valuation multiples because investors expect the transition to AI-generated tools to weaken margin stability.

While traditional SaaS companies achieved an EV/Sales multiplier of 8–12, this has fallen below 6 for many providers since 2024. At the same time, we are seeing rising valuations for AI infrastructure startups specializing in orchestration, model monitoring, or code generation.

This signals that capital is no longer merely seeking recurring revenues, but rather control over the production logic of the future.

Digital Ownership: The Return of Ownership Rights

A compelling narrative element is the return of the concept of digital ownership. In the SaaS system, companies paid for usage, not ownership. Generative AI changes this: When a company builds its own tool, it owns the code, data structure, and functional logic.

This opens up new possibilities for tradable software assets, internal IP management, and the monetization of individual code components. Software is becoming a commodity again – individualized, unique, and interchangeable.

Economists could speak of a "re-privatization of digital capital" here. Instead of platform monopolies, thousands of micro-ecosystems of specialized tools are emerging. This trend runs counter to previous platform strategies – and could lead to a dismantling of central tech power in the long term.

Regulatory affairs, security and institutional change

The more personalized and decentralized software becomes, the more complex its governance becomes. Data protection, quality control, liability, and licensing law all need to be rethought. When AI generates software, the question arises: Who is liable for functional errors?

Regulatory institutions – from the EU to the US Department of Commerce – are beginning to develop new categories: “AI-Generated Software Accountability”, “Model Transparency Act”, “Auditable Code Frameworks”. These standards could ultimately determine market access.

Europe has a potential advantage here: its emphasis on data protection, traceability and fairness could form the basis for trustworthy, exportable AI production standards.

The strategic future scenario until 2035

A plausible scenario for 2035:

1. Companies have internal AI generators that synthesize software applications on demand.
2. Generic SaaS functionalities (CRM, HRM, Collaboration) are licensed as models, not as platforms.
3. Maintenance, security, and energy optimization are becoming new service industries.
4. Software is developed on a project basis, temporarily, and iteratively.
5. Data sovereignty and model expertise are replacing brand loyalty as a key success factor.

This does not mean the end of SaaS, but its transformation: from "as-a-service" to "as-you-own".

Macroeconomic long-term consequences

When the software market shifts from subscription models to ownership models, this also affects macroeconomic indicators.

• Corporate investment in intangible assets is increasing, while operating expenses are decreasing.
• National innovation statistics should include AI-generated software as a capital asset.
• The digital economy is shifting value creation from US-centric platforms to regional, distributed production.

This dynamic is similar to the shift from a manufacturing to a knowledge economy – only this time within the intangible realm.

Societal dimension: Autonomy instead of dependence

In the long run, it's about more than just efficiency. The post-SaaS era symbolizes the reclaiming of digital self-determination. When organizations, municipalities, or individuals can once again create and own software themselves, a new form of technological sovereignty emerges.

This is also a political question: Who defines digital tools, who controls updates, data access, and integrations? AI-generated software leads back to decentralized, democratized control over technology – provided it is not monopolized again through proprietary models.

From renting to building your own

SaaS won't disappear, but it's losing its untouchable status. The combination of cost pressures, AI automation, and a growing desire for flexibility is challenging the foundations of existing cloud capitalism.

In ten years, software could become what it once was: a custom-tailored tool – only this time generated, not hand-coded.

Companies that adopt this logic early on can not only reduce costs but also gain strategic independence. For investors, regulators, and technologists, this marks the beginning of a new phase in the digital economy: an era in which software is no longer rented but produced – situationally, intelligently, and autonomously.

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