Why AI Is The End Of Microservices

Many dream of becoming captains and conquering the seas, but very few people, when looking at a massive cruise liner, seriously decide to build their own ship and set sail.

Building a small boat is a much more realistic task, which many can accomplish. However, what happens if a machine appears that can build boats faster and better than a carpenter?

Something similar is happening to small programs and applications built for one specific task: AI is simply taking over their niche. Does it still make sense to create your own software in 2026?

Small Online Services Vs. AI

For a long time, things were a bit different with internet businesses and IT companies. Historically, the internet has been full of microservices—or, more accurately, monoservices—websites built for one specific task:

In essence, services such as these exist because large, full-featured programs are either too complicated or too expensive for casual users. Even Adobe has started offering some PDF editing features for free, possibly because such services began competing with its product.

Why Microservices Became Vulnerable

These services aren't very complex by themselves, and with the rise of AI and vibe coding, programming has become even faster and easier.

As a result, the value of such products decreases. If a service is easy to copy, many clones appear and competition grows. Unlike large projects, a single vibecoder can build a small service.

How AI Is Changing Business On The Internet

AI has changed the situation in two ways:

AI: The New Smartphone

The situation with LLMs and microservices is similar to how smartphones entered everyday life.

Before smartphones became mainstream, every task had its own separate tool: an alarm clock, radio, music player, camera, computer, notebook and pager. Now, all of this lives in one place, inside one system. Devices such as pagers are barely remembered today.

In the same way, AI is gradually taking over the niche of microservices. People are even using search engines less because of AI since LLMs can help search, process and export information in a convenient format.

How To Create A Product AI Can't Replace

This raises a natural question: How can developers survive when AI is already excellent at solving users' problems through a conversational interface? This trend is only accelerating. Today, AI sometimes writes code faster than programmers themselves.

However, AI still has its Achilles' heel. It's great at creating separate parts of a product. It's much harder for it to understand the full picture of a user's problems and create a large, complex product.

For example, AI couldn't easily replace big products that have many internal (hidden) rules such as office analogs, accounting software, social networks and website builders. These products are in a stronger position because a complex product has many nuances. It's not as easy to create or replace as, say, a simple PDF maker.

Powerful, large and complex projects are still beyond AI's reach. If you want to compete with AI and not worry that your project will have to shut down, focus on a few things during development:

1. Building Products Around A Real User Pain

AI can easily assemble template-based apps, but it struggles to deeply understand a specific audience as well as the audience's habits, limitations and the real reasons why people will use a product.

2. Creating Ecosystems People Use Constantly

If a person opens an app every day, stores data there, does their work there, configures workflows and connects their team, the product becomes part of their routine. AI can copy a single feature, but it's much harder for it to replace all of the accumulated context, habits and workflows. You need to have an army of users.

3. Thinking Of AI As A Tool Rather Than A Competitor

Don't try to outrun AI at creating simple screens, texts and template code. Use it instead to prototype, write, test and improve your product faster.

The Main Problem Of AI

AI still isn't perfect. One of its main problems is unreliable memory. It still can't fully store passwords, bank card details or other sensitive information on behalf of the user.

However, AI agents are emerging—OpenClaw, Hermes and others. They're growing in number, and they'll become more accessible to everyday users over time. These agents are able to remember data and understand a person's habits (what they like, where they order delivery from, how they make payments) to determine which actions they can perform automatically.

Over time, large AI systems will likely fully absorb microservices. Simple one-off tasks that once required separate websites will increasingly be handled directly inside LLMs or by dedicated AI agents. For developers, it's time to start adapting to AI and agents instead of trying to compete with them.

Original publication: 2026-08-17 Why AI Is The End Of Microservices