Hey there. Grab a coffee because we need to talk about one of the quietest, yet most impactful moves Google has made recently. It’s the kind of subtle Google plan for AI that might not trend on X (formerly Twitter), but it’s sending seismic waves through the entire AI ecosystem.
The news? Google eliminated the num=100 search parameter last month.
A tiny technical tweak. Who cares?
Well, if you’re a startup founder, an AI developer, a business owner who wants to integrate AI into their business tasks, or just someone who understands that access to data is power, you should care deeply. This small change is a giant, blinking neon sign about the future of AI, and it reveals a truth so big it could reshape your business plan for AI: Google is tightening the data faucet, and we all need to rethink how we learn.
The Quiet Tweak That Shook the Data World
What was num=100, anyway? For years, developers and power users had a secret weapon when querying Google Search: the num= parameter. By adding &num=100 to a search URL, you could force Google to display up to 100 organic search results on a single page, instead of the standard 10.
Why was this such a big deal? Imagine you’re training a small AI model, a retrieval augmented generation (RAG) system, for example. Your model needs a deep, wide, and fast look at a topic to get a comprehensive understanding. Being able to pull 100 authoritative, high-ranking sources in one go was a goldmine for data collection and model training. It was efficient, scalable, and crucially provided a richer, more diverse dataset for your bot to chew on.
When Google limits search, AI loses knowledge. By removing this parameter, Google is essentially capping the firehose. Now, to get the same 100 results, you have to click through 10 pages of search results, which is a massive logistical and technical hurdle for automated data scraping and collection.
The Real Story: Why Google Removed the num=100 Search Parameter?
We assume Google did it to control data access, protect its AI advantage, and push developers toward using its official data channels.
This isn’t just about making developers work harder; it’s about control and scarcity.
The entire AI industry runs on a singular, essential fuel: data. The Google-indexed internet is the best data source on Earth because it is the largest, most trustworthy, and historically, the easiest to access for training. When Google removed the num=100 search parameter, it didn’t just break SEO tools; it strategically raised the cost and difficulty for everyone else (especially rival AI models like GPT-4) to tap into that high-quality fuel source efficiently. This move protects Google’s own AI advantage, making it harder and more expensive for competitors to build, train, and update models that can compete with Google’s internally-fed systems like Gemini, fundamentally asserting that data access is the ultimate power in the future of AI.
So, when Google limits AI access to that source, it fundamentally changes the playing field.
Think of it like this:
- Before: Training a new natural language processing (NLP) solutions model was like drawing water from a giant, easy-to-access public reservoir (the top 100 search results). Everyone could get as much as they needed, quickly and cheaply.
- Now: The reservoir is still there, but you can only fill a tiny cup (10 results) at a time. To get the same amount of water, you need to dedicate way more time, effort, and resources to the task.
This move disproportionately affects startups, small development teams, and independent researchers—anyone who relies on open, efficient access to the web to compete with the giants.
The Ripple Effects: Who Wins and Who Needs a New Plan
The fallout from this small move is massive, hitting three key groups hardest:
1. The Startup Scramble: Diversify or Die
For any startup whose business plan for AI involves gathering large amounts of fresh, high-quality data from the public web, this is a crisis moment. Your models are built on that data; without it, they stagnate.
The takeaway is clear: stop relying on a single, easy source.
We’re going to see a huge push for teams to diversify their data sources. This means:
- Investing in Proprietary Data: Creating, aggregating, and licensing unique, first-party data that competitors can’t easily replicate.
- Exploring Alternatives: Turning to other search engines, web archives, academic databases, and high-quality, specialized data repositories.
- Focusing on Efficiency: Instead of gathering more data, teams will focus on better curating, cleaning, and leveraging the data they do have. This shifts the focus from simple data volume to data quality.
2. The SEO Aftershock
While AI is the star of this story, we can’t ignore the original victims: search engine optimization (SEO) professionals and digital marketers.
Many SEO and analytics tools depend on scraping Google’s search data to track keywords and competitors. Now that access is restricted, running these tools has become more expensive and complex. This shift gives an advantage to companies already strong in AI for process automation and marketing, since they can use smarter, ethical AI methods that don’t rely on scraping to stay competitive.
3. The Giants Get Stronger
Let’s be honest. Who doesn’t this change affect? The companies that already own the data: Google, of course, but also Meta, Microsoft, and Amazon.
Google’s own AI models (like Gemini) don’t need to scrape the public web. They have direct, unmitigated access to the entire Google index, the largest private dataset in the world. By limiting public access, Google further cements its competitive advantage. It becomes harder and more expensive for any newcomer to build a truly comparable foundation model.
The bigger truth behind this quiet move is that it’s a power play: one that gives even more control to those who already have vast amounts of data, while making it harder for smaller players to get in.
For Businesses: Rethinking Your AI Strategy
If you’re a business owner, this change is actually a wake-up call and an opportunity.
Here’s how to think about it:
- Diversify Your Data Sources
Don’t depend on a single data stream like Google. Use internal data (sales, customer interactions, surveys) and combine it with publicly available datasets. Your own data can become your biggest competitive advantage. - Invest in Custom AI Development
Off-the-shelf AI tools are great, but custom-built solutions, like custom AI software development, let you design systems that learn from the data you actually control. - Automate Smarter, Not Just Faster
Use AI for process automation and marketing to create systems that make data work for you, whether it’s in customer support, lead scoring, or market trend prediction. - Leverage Natural Language Processing
With natural language processing (NLP) solutions, like virtual assistants, email filtering, etc., your business can extract insights from emails, feedback, or reviews—data that’s uniquely yours.
Who Owns the Future of AI?
The future of AI belongs to those who are resourceful, strategic, and innovative with their data.
If you’re building or leveraging AI, you need to ask yourself these questions right now:
- How diversified are my data sources? If 80% of your training data comes from easily scraped web sources, you are at risk. Start looking at licensed datasets, public APIs, and user-generated content you can legitimately own and curate.
- Is my AI “top level” or “generalist”? Generalist models that rely on broad web knowledge will suffer. Niche, focused top level AI solutions that use specialized, proprietary datasets will thrive. Focus on deep expertise where you can control the data.
- Are we using data efficiently? Instead of scraping 100 results, how can your model get the same value from the top 10? The next frontier of AI-driven customer service tools won’t be about more data, but smarter data curation.
The elimination of num=100 might seem like a boring IT footnote, but in the language of the future of AI, it translates into a powerful message: the era of cheap, easy data is over. Adapt now, or get left behind.
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