I asked ChatGPT about current stock prices recently and got back real-time data that was accurate and completely up to date. But wait, ChatGPT’s training data cuts off in 2023, so how exactly did it know current information?
It called an external API, probably Google’s or Bing’s search service, rather than using its own web crawler. Here’s how AI search actually works under the hood and the infrastructure companies making it all possible.
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AI Models Can’t Search The Web Alone
Large language models are fundamentally designed to generate text by predicting the next word in a sequence, and that’s literally all they can do on their own. They don’t have built-in web browsers, don’t run crawlers continuously indexing the internet, and don’t maintain searchable indexes of web content, which means they fundamentally can’t search the web by themselves.
When Perplexity, ChatGPT, or Claude appear to retrieve current information, they’re actually calling external search APIs behind the scenes, receiving structured search results back, and then formatting those results into natural-sounding language for you. The AI acts as a natural language interface while the traditional search engine does all the actual work of finding information.
How The Architecture Works
The complete AI search flow operates through multiple coordinated steps. You ask “What’s the latest Tesla stock news?” and the AI recognizes this question needs current data it doesn’t have in its training. The system calls a search API from Google or Bing to retrieve fresh results and receives back structured JSON containing URLs, text snippets, and publication dates. The AI then formats this raw data into a natural language response so you receive “Tesla stock is up 3% trading at $247 according to Reuters…” The answer feels seamless to you. But behind the scenes there are multiple API calls, JSON parsing, and careful response formatting happening.
The Search API Business
AI companies desperately need search capabilities to stay relevant, but building a production web crawler from scratch costs millions of dollars in infrastructure. Google has been crawling and indexing the web for 25 years accumulating unmatched data, while Bing benefits from Microsoft’s essentially unlimited financial resources, so startups can’t realistically compete overnight.
The practical solution is to buy access to existing search infrastructure and pay per query for the service. Google Custom Search API offers cheap pricing for low volume but becomes expensive at scale, Bing Web Search API provides similar pricing and capabilities, and numerous specialized APIs focus on specific verticals like news aggregation, academic papers, and product catalogs.
This market opportunity is exactly where Noble enters the picture.
Noble’s Role
Noble provides search APIs designed specifically for AI systems rather than human consumers browsing results pages, with their infrastructure built from the ground up for LLM integration. They deliver perfectly structured responses as clean JSON that models can easily parse, maintain real-time indexing to ensure data freshness, offer cost-efficient pricing optimized for high-volume AI applications, and pre-filter results for relevance which removes spam and low-quality content.
When you get search results through Perplexity, there’s a good chance Noble’s API is somewhere in the technology stack powering those results. You never see their brand name prominently displayed, but their infrastructure quietly powers the search functionality behind the scenes.
Why This Business Model Works
AI companies fundamentally want to build better AI systems, not become search engine companies reinventing Google’s infrastructure. Noble along with competitors like Brave Search API, Serper, and SerpAPI provide the critical search layer as infrastructure, executing the classic picks-and-shovels strategy of selling essential tools to gold miners rather than mining gold themselves.
This is arguably a better business model than building frontier AI models because Noble serves multiple customers since essentially every AI product needs search, they generate predictable recurring revenue from API usage, they avoid the AI hype cycle’s extreme volatility, and they don’t need to compete in the expensive race to build ever-larger foundation models.
The Multi-API Strategy
Modern AI search systems don’t rely on a single source but instead query multiple specialized APIs simultaneously. They hit Google for general web results, use dedicated news APIs for current events, query financial data APIs for stock prices, access academic APIs for research papers, and pull from product databases for shopping queries, then intelligently stitch all these disparate sources together into coherent responses.
This multi-source approach is why AI search feels dramatically better than traditional Google search. Google gives you 10 blue links to sort through yourself, while AI search provides a synthesized answer drawing evidence from multiple authoritative sources. The AI isn’t actually smarter at searching, but it’s vastly better at combining information from diverse sources and presenting it in digestible form.
The Cost Problem
Every API call costs real money that adds up frighteningly fast at scale. Google Custom Search charges $5 per 1,000 queries which sounds incredibly cheap at first glance, but millions of queries daily translate to serious money. Perplexity processes millions of searches every single day while hitting multiple different APIs for each query, so their costs add up to substantial operational expenses.
This cost structure explains why AI search tools aggressively limit free usage tiers, charge monthly subscriptions for unlimited access, cache results extremely aggressively to avoid redundant API calls, and batch similar queries together when possible. Better answer quality directly equals more API calls which equals higher per-query costs that someone has to pay.
What Happens When AI Companies Build Their Own
Some ambitious AI companies are building proprietary search infrastructure to reduce dependencies. OpenAI is actively experimenting with running their own web crawlers and building search indexes, while Anthropic might follow suit eventually since no company wants critical third-party dependencies they can’t control.
But building production search infrastructure is incredibly hard even for tech giants. Google has been at it for decades and still struggles with spam detection, relevance ranking, and content freshness across billions of pages. Building from scratch requires running crawlers 24/7 worldwide, storing and indexing petabytes of data efficiently, developing sophisticated ranking algorithms that work, constantly fighting evolving spam techniques, maintaining real-time indexing for breaking news, and scaling globally to handle massive traffic, creating a massive distraction from the core mission of building better AI models.
Most AI companies will sensibly keep using third-party APIs because search infrastructure isn’t their core competency. Companies like Noble make it embarrassingly easy to integrate production-quality search without building everything yourself.
Why You Should Care
If you’re building AI products, you absolutely must understand this multi-layered architecture rather than assuming it’s just about model inference costs. Your actual costs include not only the LLM API calls but also search API fees, data enrichment services, maintaining knowledge bases, and accessing real-time information sources.
Your total cost per query includes all these infrastructure layers stacked together. More importantly, your product’s quality depends entirely on which APIs you integrate with because cheap APIs deliver spam and outdated results that make your product feel broken, while premium APIs provide better data that creates superior user experiences worth paying for.
The Future
The search API market is heading toward even more specialization across narrow verticals. We’ll see dedicated domain-specific search for industries like legal research and medical literature, real-time event tracking for breaking news and live sports scores, verified authoritative sources pulling from academic journals and government databases, and hyper-local business information including reviews and current operating hours.
Companies like Noble build this critical infrastructure layer so AI product companies can focus on being great at AI rather than becoming search engine companies. The best AI search products in the future won’t have the best foundation models, they’ll have the best API integrations and orchestration logic.
The Bottom Line
AI search isn’t magic or particularly mysterious once you understand the architecture. It’s just APIs working together. When ChatGPT provides current information, it’s calling Google or Bing behind the scenes. When Perplexity cites multiple sources, it’s hitting several different search APIs simultaneously. When you get real-time data, someone is buying access to proprietary data feeds.
Noble and similar infrastructure companies make all this possible by providing the unsexy infrastructure layer powering sexy consumer-facing AI products. You never see their brand prominently displayed in the user interface, but you’re using their services with every AI search question that can’t be answered from the model’s training memory alone.
Now you know.