"AI is eating software"? No. Software is eating AI

Lately, you hear it more and more: “AI is eating software.” Within the world of Software as a Service (SaaS), that statement is causing unrest. Companies that have successfully developed and delivered applications via subscription models for years are watching as AI players like Anthropic and OpenAI rapidly launch plug-ins and agentic applications that seemingly take over functionalities upon which entire SaaS solutions were built. What was a differentiating product yesterday appears to be available today via an AI assistant performing the same task without the user needing a separate tool.

It is understandable that this raises questions about the raison d'être of traditional software. If an AI assistant can generate reports, perform analyses, structure data, and even automate processes, where does the value of specialized software still lie? Consequently, many SaaS companies see their revenue models under pressure. Functionalities that were premium for years are in danger of becoming commoditized by generic AI solutions.

Yet, I believe the conclusion that AI is “eating” software is too simplistic. In fact, I believe we are moving toward the opposite. Not AI replacing software, but software absorbing AI. In other words: software is eating AI.

Software is not losing its right to exist; it is gaining a new dimension. Where software used to revolve around functionality, workflows, and data processing, it now increasingly revolves around intelligence, adaptivity, and context. AI is not a replacement for software, but an accelerator of it. The software that will be successful in the coming years is not the software that tries to ignore or fight AI, but the software that integrates AI deeply into its core.

The difference lies in how you apply AI. When AI is merely a loose feature-for example, a chatbot button in an existing application-fundamentally little changes. But when AI is deployed as the engine behind the product, the nature of the software itself changes. You then shift from static systems to dynamic systems that continuously learn, monitor, and optimize.

A concrete example of this is Proxies, a company we recently started. At Proxies, we build highly detailed business databases for Europe. Traditional databases are essentially static. They contain snapshots of information: revenue figures, number of FTEs, sector classifications, and other company characteristics. Updates are often periodic, depending on fixed sources or manual input. The information is valuable, but always a snapshot in time.

With the deployment of Agentic AI, that changes fundamentally. Instead of a database that merely stores what is known, we create a system that actively collects and understands information. Our AI Agents behave like digital researchers. Just like a human, they specifically search for relevant information about companies on the web and in public data sources. They analyze context, recognize signals of change, and monitor developments continuously.

Consider, for example, changes in employee count, revenue growth or decline, new locations, strategic announcements, or management changes. Where this was previously only possible with large teams of analysts or limited sampling, it can now be executed scalably and continuously thanks to Agentic AI. The system doesn't wait for a quarterly update; it actively monitors what is happening.

This was simply not possible before. Not because the need wasn't there, but because the technology to collect contextual and intelligent information at scale was missing. Agentic AI makes it possible to build software that doesn't just manage data but actively generates knowledge.

And here lies the heart of my argument. Generic AI solutions are powerful, but they are broad and general. They do not know the nuance of a specific market, sector, or use case at the level specialized software can. The real value arises when you combine AI with deep domain expertise, structured data, and a clear product vision. Then, AI becomes an infrastructure layer within software, not a replacement for it.

SaaS companies are not facing an existential crisis, but a strategic choice. Do you stick to a model centered on functionality where AI is seen as a threat? Or do you redefine your product by integrating AI as a core component and elevate your solution to a higher level?

The possibilities arising from plug-ins, agentic systems, and new AI tools are enormous. They allow us to build software that is proactive instead of reactive, intelligent instead of static, and adaptive instead of fixed. That requires product redesign, reconsideration of business models, and, above all, courage.

That is why I want to challenge companies not to react defensively to the rise of AI, but to think offensively. Don't ask “how do we protect ourselves against AI?” but “how can our software absorb AI and thereby become better than ever?”

Because the standard of software is not going down because of AI. It is going up. Much higher than we previously thought possible.

Learn more about Proxies? Check proxies.ai.nl

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// About the author

Job van den Berg

Mede-oprichter, AI Keynote Spreker & Techondernemer

Tech-ondernemer (1989) met een achtergrond als socioloog (Research Master (MSc) in statistiek en sociologie) en een van de meest gevraagde keynote sprekers over AI en data in Nederland. Als mede-oprichter van Ai.nl, The Automation Group en Proxies leidt hij engineers die agentic AI van prototype naar productie brengen binnen enterprises. Op het podium vertaalt Job die hands-on praktijk naar concrete strategieën. Eerder was Job Chief Data bij o.a. DPG Media en Kantar. Hij is co-auteur van 5 boeken over AI waaronder 'AI Agents' en 'Handboek AI Strategie' en een veelgevraagd expert in de landelijke media.

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