Big Tech’s Race to Build the All-in-One AI App
Google and other tech giants are building AI-powered everything apps to handle diverse tasks in one platform. These tools use personal data for tailored, conversational experiences but face accuracy and privacy challenges.
Big Tech’s AI Endgame Is Coming Into Focus
One App to Rule Them All
If Google has its way, the traditional search bar might soon become obsolete. The company is shifting from keyword-based searches to AI-driven conversations. At its recent software conference, Google introduced “AI Mode,” a significant overhaul of its search engine. Unlike the current AI summaries that appear above search results, AI Mode replaces the typical list of links with direct, conversational answers—similar to ChatGPT. You ask a question, get a detailed response, and can follow up naturally.
Currently, AI Mode is rolling out to U.S. users as a new tab below the search bar. Google plans to add advanced features soon, such as generating research reports in minutes, assisting with real-world tasks by “seeing” through smartphone cameras, booking reservations, and even handling payments. While these ambitions are bold, Google seems focused on building an everything app—a single platform capable of handling nearly every online task.
Other tech giants share this vision. OpenAI’s ChatGPT writes code, summarizes documents, aids shopping, and creates graphics. Elon Musk wants to transform X into an everything app. Meta promotes its AI for “everything you need,” Amazon positions Alexa+ as an always-available assistant, Microsoft markets AI Copilot as a companion for all tasks, and Apple is upgrading Siri and its AI offerings to transform iPhone usage. Even Airbnb is expanding beyond vacation rentals to become a platform where users can “sell and do almost anything.”
These everything apps are a natural step toward artificial general intelligence (AGI). A bot capable of handling any task fits perfectly into a product designed to do just that. At the same time, these apps deepen the integration of tech into daily life. Google, Apple, and Meta already operate vast ecosystems covering shopping, navigation, communication, and more. Condensing all that power into a single app would be a major shift.
These ambitions are possible because of the vast amounts of personal data these companies have collected over the years. This data fuels AI models that promise customized experiences. For example, AI Mode’s responses can incorporate your search history and even email content. When I entered “line up” in AI Mode, it returned the New York Mets game lineup instead of a dictionary definition, showing how AI can personalize results.
This approach is a continuation of the data-for-service trade-off tech companies have long offered. Meta’s AI uses data from Facebook and Instagram, Apple’s AI taps into texts and notes stored on devices, and ChatGPT now includes a memory feature to recall prior conversations. If successful, these AI assistants could anticipate your needs—ordering a jacket ahead of your move or suggesting a vacation itinerary based on email hints.
However, these apps face significant challenges. AI models still make mistakes: Google’s AI-generated Mets lineup had inaccuracies, and chatbots often hallucinate facts or mess up math. Environmental concerns and intellectual property debates could slow progress. Google’s past AI features have produced bizarre advice, and some experimental tools have been misused to create inappropriate content. Errors in ordering or recommendations remain possible.
Despite these issues, the push toward everything apps continues unabated. The convenience of fully integrated tools is tempting. The more products a company offers and connects, the better the AI can personalize experiences. Google’s Gemini model, announced alongside AI Mode, aims to be a universal AI assistant, showing the company’s commitment to integrating products and data.
On the surface, AI-powered everything apps promise to transform how we interact with technology by unifying search, social media, productivity, and more into a chatbot. But this race resembles companies competing for user data and dominance rather than pure innovation. Even OpenAI, which started as a nonprofit, is moving toward building its own social network to gather more data. The future of AI looks heavily reliant on the data strategies tech giants have long employed.
For professionals in IT and development, understanding these trends is crucial. The rise of AI-driven everything apps signals a shift in how software and services will be designed and integrated. Staying informed and adapting skills to work with these AI platforms will be essential. Exploring specialized AI courses can provide practical knowledge to navigate this evolving landscape effectively.
