A Paradigm Shift in Online Search Is Coming. You Need to Master It Now

Quick Takeaways:

• ChatGPT rapidly gained popularity, reaching one million users in five days and 100 million monthly active users (MAUs) within three months, highlighting the swift adoption of AI technologies.
• But DeepSeek disrupted the narrative that vast computational resources and deep pockets are necessary for building advanced AI, achieving significant capabilities with a shoe-string budget and open-source approach.
• This faster-than-expected proliferation of AI into our everyday lives could mean significant disruption for your business, as SEO tactics become subsumed by AI search optimization (AISO), also known as generative engine optimization (GEO).
• Optimizing for AI means optimizing for large language models (LLMs). Those who get ahead of it could turn disruption into opportunity — trouncing competitors who continue to spend on SEO strategies involving keyword-stuffing rather than natural language understanding.

When ChatGPT hit the market in November 2022, it took just five days for the chatbot to sign up one million users. Three months later, it had more than 100 million monthly active users, leapfrogging household names like TikTok and Spotify.

Now, as I write this, OpenAI's groundbreaking chatbot claims 300 million users per week worldwide, with roughly 70 million of those users coming from the U.S. alone. It's no wonder the company is valued at more than $150 billion—almost as much as Adobe, a company that's been around since 1982.

Since ChatGPT launched, we've seen several iterations of the AI chatbot. I personally use around seven of them, depending on what I need done, and that's only for text generation! These include: Microsoft's Copilot, Google's Gemini, Meta's Llama-3, X's Grok, Anthropic's Claude, Perplexity AI, and too many to list here!

Users of LLMs are churning out editorial and marketing copy daily, generating new images, filming commercials, or just plain experiencing the internet without the need for search engines, which have become increasingly unreliable.

Google Search, the de-facto central hub of the internet, has begun stripping away nuance from natural language queries, favoring a few isolated keywords. To put it another way, when you enter a detailed search query Google drops important context, and you end up with a SERP (search engine results page) that barely address the search intent.

For example, one Redditor noted that when they searched for “wrath aeon of ruin how to see current difficulty”—a query meant to get specific instructions for a video game—Google returned results nearly identical to those from a simpler search for “wrath aeon of ruin difficulty.” In effect, the engine ignored much of the extra context, delivering generic reviews, or even a Wikipedia link, that didn’t answer the original question. This isn’t just a one-off glitch either; it reflects a broader trend where search engines — increasingly driven by advertising and simplified algorithms — compromise on delivering the nuanced, comprehensive results you would expect.

It's simply a lesser product than it used to be. In the market — where it's pitted against superior, personalized results from artificial intelligence — users will choose the more efficient product every time.

The next generation of children will grow up with this functionality, and it won't be long before "Google It" is as much of an anachronism as a Sears catalog. My kids — aged 14 and 12 — both use AI just to chat and create stories, but it's also a big part of how they explore the internet (which includes TikTok more than Google).

^{AI chatbots like ChatGPT can now search the internet in real-time so you don't have to.}

As business leaders, executives, and managers, you've undoubtedly discussed how to use AI in your processes ... but not many have given thought to what AI means for their subscribers, users, clients, and overall business...

To better understand AI, let's draw a parallel with the early days of the internet. In the nineties, when the internet was a new phenomenon, modems purred like deranged kittens, and Bryant Gumbel infamously pondered the meaning of the "@" symbol. This gave us the now-hilarious "What is internet anyway ... what, do you write to it, like mail?" gaffe.

Frankly, that's exactly where we are with AI: a state where everything is possible, new applications are unleashed, and yet, our business processes have not really changed … yet.

DeepSeek, Shallow Pockets

Where AI diverges from the dot-com boom, though, is in how AI is taking root in all things everywhere faster than anyone knew possible.

Perhaps you've heard of the Stargate AI project? This ambitious endeavor is not just a collaboration but a strategic move to position the United States as the global epicenter for artificial intelligence. Announced in early 2025, Stargate is a joint venture involving key players from both government and private sectors, specifically the White House, OpenAI, Oracle, and SoftBank.

With Donald Trump's administration at the helm, this project underscores a clear policy shift toward fostering AI development within the U.S., aiming to cement technological leadership on an international scale.

Sam Altman, CEO of OpenAI, has heralded Stargate as "the most important project of this era," focusing on both the development and ethical deployment of AI technologies. As one of the largest data center operators in the U.S., Oracle's involvement ensures the physical infrastructure necessary for AI training and deployment.

With enhanced AI capabilities coming online, businesses and developers will need to adapt to new organic content strategies, wherein AI models can better understand and predict user intent, leading to a paradigm shift in digital marketing, content creation, and search engine functionalities.

Of the $500 billion, OpenAI planned to disperse $100 billion immediately in a Texas buildout, scouting locations across the country for more campuses.

I say "planned" and not "plans" because of a development that followed the Stargate news: DeepSeek.

DeepSeek is an AI chatbot that rapidly ascended  the ranks of Apple's App Store, becoming the most downloaded free app in the U.S., surpassing even ChatGPT!

The model, R1, was trained on the Countdown game, developing reasoning skills similar to larger AI systems by refining strategies through trial and error. The study shows that structured learning and model size are crucial, challenging the need for vast computational resources, proving that, with efficient techniques, one can achieve advanced AI without large budgets.

Prior to DeepSeek, the thinking was that it would take hundreds of billions of dollars in infrastructure and compute (AI chips like Nvidia's H100) to make AI ubiquitous.

In addition to deep pockets, training an AI model requires significant computational resources and financial investment. But the low cost of DeepSeek has changed the narrative. The Chinese AI lab behind DeepSeek, or R1 as its model is dubbed, delivered a large-language model on par with that of any big tech model—and they did it for less than $6 million (allegedly), and with 2,000 H800 (inferior to the H100) AI chips. To get an idea of how innovative that is, OpenAI is estimated to have spent $100 million to train its o1 model, using 10,000 of Nvidia’s H100 and A100 chips.

For developers, DeepSeek’s R1 is open source, which means it can be replicated by smaller, nimbler companies than those of the “Broligarchy.” For users, R1 is entirely free, removing any financial barriers to entry and allowing a broader audience to use large-language models. This level of accessibility and openness will rapidly proliferate, making AI an everyday global ubiquity. And indeed, Berkeley researchers not only managed to replicate R1-Zero’s core capabilities, they did it for less than 30 bucks.

By being open source, DeepSeek has tapped into a global network of talent, allowing for rapid advancements and improvements that might not be possible within a closed system. Users can tailor the platform to meet their specific needs, creating bespoke solutions that align with their unique requirements. The Berkeley team’s recreation of R1 used a 3-billion-parameter language model with reinforcement learning for self-verification and search abilities, making reinforcement learning research much more accessible.

In fact, Amazon and others are already hosting R1 for developers, and it won’t be long before a company like Meta uses DeepSeek’s training methods to push out Llama 4 and, as a colleague of mine believes, introduce “infinite access to an o1/R1-level model via Facebook/Instagram/WhatsApp/etc.”

Here’s Intel’s ex-CEO on the three most important things DeepSeek reminded the world of computing history:

Basically, DeepSeek’s arrival is already ushering in a new era of innovation in AI — one that will exponentially increase the speed at which we get to AGI (artificial general intelligence). Along the way, the digital landscape as we know it will be changed to fit the new paradigm, not unlike the internet in the early aughts.

So, then, with AI more popular than a No. 2 pencil, why are companies only focused on using AI to create more of the same content, optimized for more of the same broken search algorithms?

AI Search: Generative Engine Optimization Isn't Coming; It's Here

In the rapidly evolving landscape of AI, the emergence of models like DeepSeek challenges traditional notions of technological advancement and accessibility. As AI becomes more integrated into daily life, businesses must rethink their strategies to harness its potential effectively.

The concept of AI search optimization (AISO) is central to this shift, emphasizing the need for content that resonates with large language models. Unlike traditional SEO, generative engine optimization (GEO) focuses on semantic relevance and natural language understanding, requiring content to be rich in context and coherence.

Amid this democratization of AI, companies can no longer rely solely on conventional content creation and optimization strategies. Instead, they must adapt to a world where LLMs play a pivotal role in information discovery and dissemination. This involves not only optimizing content for AI, but optimizing schemas and structured data, and even collaborating with AI developers to include one's content as part of the training data for upcoming models.

Businesses that embrace these changes and leverage the power of GEO/AISO will be well-positioned to thrive. For those who don't, a gradual and steep drop-off in organic users is an inevitability.

The future of AI is not just about creating more content, but about creating the right content that aligns with the evolving capabilities of AI systems.

In this new paradigm, the question is not whether AI will change the way we do business, but how quickly we can adapt to its transformative potential.

In our next story, we will discuss the new paradigm taking shape right before our eyes, how it is changing organic search, and what your business can do today to stay ahead or get ahead of previously unbeatable competitors.

If you're ready to take your company's content strategy into the next era, visit us at Gen-Prompt.com, and join our beta list. We're preparing a boutique solution single-mindedly focused on creating content in the Age of AI Search, which will help you level the playing field for your small- or mid-size business.

We hope to chat with you soon!

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