Why Most Modern Apps Will Be Useless in the Age of AI

AI vs. Off-the-Shelf Applications

Let me say something controversial: most apps you’re paying for today will be irrelevant in 3 years. Not because they’re bad. Because AI will build you a better, cheaper, personal version – on demand. The digital products market is entering a phase where simple web services and mobile apps will gradually be replaced by rapidly assembled, personalized solutions built on top of large language models. This trend is confirmed by the growth of an entire class of generative consumer applications and AI services that allow users to access the functionality they need without installing dozens of highly specialized apps.

This isn’t speculation. The very concept of a finished product is changing: there is a growing preference for solutions tailored to a specific scenario, business process, or an individual’s habits. This shift is supported by the explosive growth of consumer AI applications. By March 2026, ChatGPT alone had grown to 900 million weekly active users. This is a strong argument for our claim that by 2026, consumer AI will no longer be a niche but a widespread habit.

How AI Is Making Software Development Cheaper and Faster

Building software used to take months. Now it takes hours.

AI is radically transforming the economics of development. Whereas building a SaaS service or mobile app used to require a team of developers, months of work, and a substantial budget, large language models and specialized frameworks now handle a significant portion of that process.

The most effective tools leading this shift? Codex and Claude Code – they replace entire stages of the pipeline. Pair them with open frameworks like LangChain or Dify, and a solo founder can ship in days what used to take a team months.

No-code LLM builders are emerging too. Describe your app in plain language and get a working product.

The next step is autonomous agent systems that operate 24/7: they learn, adapt, and make decisions without constant human intervention. Solutions like OpenClaw represent the next level: an infrastructure for self-developing AI agents that don’t need to wait for commands.

It is precisely this combination – powerful models, open frameworks, and user-friendly builders – that can be seen as the main factor putting pressure on traditional SaaS models. Building an AI application from scratch or forking an open-source solution is becoming a viable alternative to purchasing a subscription in cases where the product consists of a user-friendly interface built on relatively simple logic and an external API.

Why simple B2C apps are the first to take the hit

Who’s most at risk? Simple B2C apps: habit trackers, calorie counters, basic fitness tools, AI coaches with no unique content. If your product can be described in two sentences and doesn’t sit on proprietary data or a strong community – you’re exposed.

The consumer app segment with simple functionality will be hit hardest: habit trackers, calorie counters, basic fitness apps, and AI coaches lacking unique content and complex mechanics. Services are already widespread that allow users to generate their own tracker or planner tailored to their lifestyle with just a few requests, without monthly payments to multiple different apps.

The quality of such automatically generated apps is still limited: they handle simple scenarios well, but fall short when it comes to complex animations, game mechanics, and children’s interactive features. This means that the first wave of devaluation will affect precisely those products that can be easily described in two or three sentences and do not rely on unique data, a strong brand, or a surrounding community.

That said, it’s fair to say that not all users are willing or able to build their own apps, just as not everyone is willing to grow their own food. Combined with the fact that new generations of users are growing up in an environment where it’s natural to ask AI for help rather than search for an app in a store, this is shaping a long-term trend toward more flexible, personalized solutions.

Why Enterprise SaaS and Infrastructure Will Remain Relevant

Who’s safe? Complex enterprise platforms with deep integrations, compliance layers, and real data moats.

In the enterprise segment, issues of security, compliance, data management, and integration with existing systems remain critical, and here, mature SaaS platforms with built-in AI capabilities often appear preferable to in-house solutions.

The SaaS market as a whole continues to grow: in recent years, the average number of cloud applications used by companies has only increased, and AI is enhancing many of these platforms, improving analytics, personalization, and automation.

What Strategy Should Companies Adopt?

The main mistake many SaaS and app developers make today is ignoring the pace of change. Technological progress in models and tools is indeed accelerating, and what seemed like the distant future a year ago is already in production today.

Stop competing on features. Start solving workflows too complex to express in a single prompt. Embed AI as infrastructure, not a feature. And take security seriously – it’s your moat against quickly assembled alternatives.

Most simple, easily replicable applications will indeed come under intense pressure from AI-driven builds and open-source tools, whereas complex enterprise platforms and the infrastructure layer will not disappear but will evolve and grow stronger.