On December 21, a 25-year-old clip of Google co-founder Larry Page began circulating again for a simple reason: it sounds like a script for today’s AI race.
In the footage, recorded in 2000—just two years after Google was founded—Page describes what he called the “ultimate search engine”: one that understands everything on the web, figures out exactly what you want, and gives you the right answer. He immediately labels that end-state for search as artificial intelligence. [1]
Fast-forward to late 2025, and Google is actively reshaping its most important products around that exact idea—shipping new Gemini 3 models across Search and the Gemini app, and pushing multimodal AI deeper into how people discover information. [2]
At the same time, investor interest is tracking the story closely. A market note published on December 21 framed Google’s latest AI advances as a key driver of excitement around Alphabet’s shares—an argument that’s becoming common as the company pivots from “blue links” to AI-driven answers and tools. [3]
The resurfaced 2000 clip: “ultimate search engine” = AI
The reason the clip is resonating now is how directly it maps onto the current generative AI moment.
Page’s line that’s getting repeated most: if Google had the “ultimate search engine,” it would “understand everything on the web,” understand “exactly what you wanted,” and provide “the right thing”—which he calls “obviously artificial intelligence.” [4]
Even more striking is the engineering framing. Page points to the early scale Google already had at the time—talking about vast data, massive computation, and thousands of machines—as the raw ingredients needed to get “incrementally closer” to that AI goal. [5]
That “incrementally closer” idea is basically the story of Google’s 2025 product strategy.
Google’s Gemini 3 era: shipping AI “at the scale of Google”
Google officially positioned Gemini 3 as a major step in that direction with a November 18 announcement: it called Gemini 3 its “most intelligent model,” and emphasized that it’s now “shipping Gemini at the scale of Google.” [6]
The same announcement highlights how widely Gemini is being woven into Google’s ecosystem—spanning Search (AI Mode), the Gemini app, developer tools, and enterprise platforms like Vertex AI. [7]
Google also made unusually specific adoption claims in that post, including:
- “AI Overviews” reaching 2 billion users every month
- The Gemini app surpassing 650 million users per month
- More than 70% of Cloud customers using Google’s AI
- 13 million developers building with its generative models [8]
Those figures matter not just as bragging rights, but because they support Google’s core argument to markets and advertisers: this isn’t an AI side project—it’s a distribution engine.
Nano Banana Pro: the viral multimodal moment that pushed beyond text
While Gemini 3 is the flagship “brain,” the internet’s attention has also been drawn to Google’s newest image generation and editing model: Nano Banana Pro.
Google DeepMind introduced Nano Banana Pro on November 20, describing it as a “state-of-the-art” image generation and editing model built on Gemini 3 Pro, designed for greater control and improved text rendering—especially legible text directly inside images. [9]
Google also positioned it as broadly available across products, including the Gemini experience and advertising and creator workflows. [10]
Why does this matter in a “search” story? Because Google is making multimodality part of discovery, not a separate creative toy. When images, text, video, and real-time web knowledge blend into one interface, “search” stops being just retrieval and becomes synthesis.
Gemini 3 Flash arrives in Search: faster answers, more “AI Mode” momentum
A big part of whether AI search works in the real world is latency. People won’t wait—especially when they’re used to instant results.
On December 17, Google Search announced it is rolling out Gemini 3 Flash globally as the default model for AI Mode, emphasizing speed plus strong reasoning and multimodal capability “without compromising speed.” [11]
Tech coverage this week echoed the same theme: Gemini 3 Flash is positioned as a faster, cheaper model that still carries many “frontier” capabilities, and it’s being integrated directly into the Gemini app and Search experiences. [12]
This is the practical bridge between Larry Page’s “ultimate search engine” dream and daily user behavior: it has to feel as fast and frictionless as classic search, even while doing more.
The real business question: will AI answers shrink the web—or expand it?
This shift has triggered a major tension: AI answers can reduce the need to click, which threatens publishers and changes ad economics.
But Google’s leadership is pushing back hard on the idea that AI search equals fewer outbound clicks.
At the Reuters NEXT conference earlier this month, Google Search VP Robby Stein described the transition as an “expansionary moment” for the internet, arguing Google still sends “billions and billions and billions of clicks” out daily and that outbound clicks are “largely stable.” [13]
Stein also compared the moment to the move from desktop to mobile—disruptive, but ultimately something advertising adapts to rather than collapses under. [14]
That said, Reuters also notes publisher concerns are real, and references research suggesting AI summaries can reduce click-through to sources. [15]
In other words: the product vision is clear, but the ecosystem outcomes are still being negotiated in real time.
Alphabet stock on December 21: “AI advances drive investor excitement,” but note the calendar
A December 21 market post framed Google’s AI progress as a major reason investors are paying close attention to Alphabet, stating that “GOOGL stock closed at $307.16” and describing strong interest tied to AI initiatives. [16]
One important detail for readers: December 21, 2025 is a Sunday, so U.S. markets were closed. The $307.16 figure corresponds to the most recent trading close (Friday, December 19), when Alphabet (Class A, GOOGL) finished at 307.16, up 1.55%, with a 301.73 open, 307.25 high, and 300.97 low, according to historical pricing data. [17]
That nuance doesn’t change the broader point—investors are watching the AI narrative closely—but it matters for accuracy, especially for readers seeing “as of December 21” language.
The hidden battleground: Google’s chips, PyTorch, and Nvidia’s moat
The consumer-facing AI rollout is only half the story. The other half is infrastructure—who controls the compute that trains and serves these models.
On December 17, Reuters reported Google is working on an internal initiative called TorchTPU, designed to make Google’s TPUs run PyTorch more easily—aimed at lowering switching costs and weakening Nvidia’s CUDA-centered dominance. [18]
According to Reuters, Google is collaborating with Meta (a key PyTorch backer) and has considered open-sourcing parts of the effort, reflecting how urgent interoperability has become in the AI platform war. [19]
This matters to the “Larry Page prediction” storyline because it underlines a consistent theme: to build the “ultimate” AI-driven search and assistant, Google needs to win not just at models, but at distribution and infrastructure.
Gemini replacing Google Assistant: another signal of the AI-first shift
Another December 21 development reinforces how far the strategy has moved: Google is extending the timeline for Gemini to fully replace Google Assistant on Android into 2026.
The Times of India reported on December 21 that Google has confirmed Gemini will replace Assistant on Android in 2026, pushing back the earlier timetable. [20]
Independent tech reporting this week similarly describes Google “adjusting” its timeline for a smoother transition, while still positioning Gemini as the long-term default assistant across devices. [21]
From an SEO-and-discovery standpoint, this is significant: a default assistant isn’t just another app. It’s an always-available interface layer that can route users into Search, into subscriptions, into commerce, and into Google’s broader AI ecosystem.
What to watch next
As of December 21, the most important open questions aren’t about whether Google will keep pushing AI into search—it clearly will. They’re about the second-order effects:
- Search experience: How aggressively AI Mode expands, and how often users choose AI-generated answers over classic results. [22]
- Publisher economics: Whether Google can sustain “stable outbound clicks” as AI summaries and conversational search become default behavior. [23]
- Model differentiation: Whether Gemini 3 Flash’s speed-first approach becomes the mainstream “search brain,” with heavier “Pro / Thinking” modes reserved for complex tasks. [24]
- Infrastructure strategy: Whether TorchTPU materially increases TPU adoption among PyTorch-first developers—and how Nvidia responds. [25]
- Assistant transition: How smoothly Google can deliver feature parity as Gemini replaces Assistant across Android and beyond. [26]
The bottom line
Larry Page’s 2000 statement is going viral because it captures the endpoint Google is now racing toward: search that understands the web and returns the right answer, not just a list of links. [27]
In late 2025, Google is acting like a company built for that finish line—shipping Gemini 3 broadly, turning AI Mode into a mainstream search surface, expanding multimodal creation through Nano Banana Pro, and working behind the scenes to make its infrastructure more attractive to the wider AI developer world. [28]
And on December 21, the throughline is clear: the “ultimate search engine” isn’t just a concept from a rare clip anymore. It’s the business plan. [29]
References
1. timesofindia.indiatimes.com, 2. blog.google, 3. meyka.com, 4. timesofindia.indiatimes.com, 5. timesofindia.indiatimes.com, 6. blog.google, 7. blog.google, 8. blog.google, 9. blog.google, 10. blog.google, 11. blog.google, 12. www.theverge.com, 13. www.reuters.com, 14. www.reuters.com, 15. www.reuters.com, 16. meyka.com, 17. www.investing.com, 18. www.reuters.com, 19. www.reuters.com, 20. timesofindia.indiatimes.com, 21. www.theverge.com, 22. blog.google, 23. www.reuters.com, 24. blog.google, 25. www.reuters.com, 26. timesofindia.indiatimes.com, 27. timesofindia.indiatimes.com, 28. blog.google, 29. timesofindia.indiatimes.com
