Have you noticed that trying a new AI chatbot lately feels like déjà vu? You explore the latest release, and while it does impressive things, those "impressive things" are often identical to what you saw in another chatbot just last week.

The cycle is becoming predictable. One AI company pioneers a breakthrough feature, and within a remarkably short span, competitors roll out their own versions. While this rapid adoption drives overall progress, it also blurs the lines of distinction, making it harder for users to see unique value propositions. Let's look at some of these now-commonplace functionalities that were once novel differentiators:

• Web Search Integration: Remember when chatbots were limited to their training data? The introduction of real-time web search was a game-changer, allowing bots to provide up-to-date information. Initially a standout feature for a few (think early integrations by Perplexity AI or an option in ChatGPT via plugins/Bing), it's now a baseline expectation. Most leading chatbots can now browse the internet to answer your queries.

  
  

• "Deep Research" Capabilities: Beyond simple search, the ability for AI to conduct more in-depth analysis, synthesize information from multiple sources, and provide comprehensive summaries was a significant leap. What started as a power-user feature is now being democratized, with many platforms offering enhanced research modes.

  
  

• Multiple Models to Choose From: As the AI landscape diversified with various powerful Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Google's Gemini, some platforms began offering users the ability to switch between these models. This flexibility, once a niche offering for those wanting to experiment with different AI "brains," is now appearing more frequently, either explicitly or through curated model selections for specific tasks.

  
  

• Custom Chatbots (aka "GPTs," "Gems," "Agents"): The advent of tools allowing users to create their own specialized AI assistants without coding (like OpenAI's GPTs) was a major step towards personalization and niche applications. This ability to tailor a chatbot with specific instructions, knowledge bases, and capabilities quickly sparked a wave of similar offerings from other players, enabling users to build "mini-bots" for everything from summarizing internal documents to creative writing assistance.

  
  

• Gmail, Drive, and Productivity Suite Integration: Connecting AI directly into our daily workflows, such as drafting emails in Gmail or summarizing documents in Drive, promised a significant productivity boost. Google was a natural early mover here with its own ecosystem, but the trend of integrating AI into existing productivity tools and enabling connections to user data (with permission) is becoming widespread across various platforms.

  
  

• Notebook / Projects: For more complex tasks, creative endeavors, or ongoing research, features allowing users to organize their AI interactions, save "projects," or work in a more structured "notebook-style" interface (akin to how data scientists use tools like Jupyter, or how Google's NotebookLM functions) have emerged. This helps manage context and build upon previous interactions, a functionality that is now being replicated in various forms.

  
  

So What if They Are All Similar?

When all chatbots have the same tricks, a few things happen:

• For us (the users): It’s great that AI is getting powerful everywhere, but it’s less exciting to try new ones if they don’t offer something really new. It can even be confusing to pick one when they all seem the same.

  
  

• For AI companies: It's harder for them to show why their chatbot is special. They might have to compete on price or how easy their chatbot is to use, rather than on cool, unique features. They might also spend too much time copying others instead of inventing brand new things.

  
  

It’s normal for new technology to go through a phase where everyone catches up to each other. But for AI to keep amazing us, companies need to do more than just follow the leader. They need to dream up the next big thing.