Google vs. NVIDIA: You Can’t Win Tomorrow’s AI War With Yesterday’s Assumptions
Many people keep asking me about my thoughts on Google, what Nvidia plans to do, and whether AI is just a bubble. The reality is that anyone claiming to have a definitive answer at this moment is likely bluffing. These models have been in the hands of real researchers for only 24 hours, and much of our response is still based on information locked behind private pilots.
Gemini 3 has arrived, and I’m sure it’s impressive. I’m genuinely excited to see what Antigravity can achieve as an integrated development environment (IDE), as it feels like a glimpse into something new. However, beyond the hype, there are a few fundamental dynamics in AI that very few people are discussing, even though they represent the most important signals in this entire conversation.
We make a big mistake when we talk about AI as if it were something fixed in time. People look at the field as it stands today and think they can forecast the entire future from that one snapshot. That kind of thinking ignores the real engine behind every technological leap we have ever seen: constant innovation coming from the edges.
1. You cannot predict the future of AI by staring at the present
The AI we have right now did not appear out of thin air. It came from countless small innovations by people who were experimenting, tinkering, and taking wild chances. Karpathy. Sutskever. Li. Baileys of the world. None of them worked inside some neatly defined roadmap. They made small, nuanced contributions that stack up into the systems we rely on today. If you freeze the story here, you miss the thousands of innovators who will add the next layers.
2. The hardware landscape is not settled
Everybody keeps talking about GPUs like this is the final hardware boss battle. That is short-sighted. It is exactly like pretending the whole future of computing should have been built around Intel chips. History tells us that once a platform proves the opportunity, new architectures show up. ARM shows up. System on a chip shows up. Mobile shows up. Entire industries get created on the back of new hardware.
China understands this better than anyone. Their strength is not GPUs or even TPUs. It is CPUs. Massive, domestic, fast-evolving CPU infrastructure. They are building a world where they can train competitive AI models without relying on the same GPU supply chain that the West treats as irreplaceable. That means the hardware meta is already shifting. If you ignore that, you are missing one of the biggest strategic moves happening in real time.
3. Innovation constantly reshuffles who leads the race
People assume that the companies that look strong today will automatically stay strong. That has never been true. Nokia looked unbeatable until it lost the plot. BlackBerry looked like the future until it tried to preserve the past. Gateway had its moment. Lenovo had its moment. Even winners like Cisco stayed alive because they adapted, not because early advantage protected them.
The same logic applies to AI today. Whoever refuses to take risks loses. Google had the transformer paper that sparked this entire moment. They should have owned the space. Instead, they played it safe. OpenAI took big swings. Anthropic took a moral swing. Elon ran full contrarian mode. Meanwhile, Google tried to be the everything company. Playing it safe in a winner-takes-all market is the same as not showing up.
4. AI companies are turning into national assets
This is the part nobody wants to say out loud. The big AI labs are not just tech startups. They are future military assets. They are national security infrastructure. They are soft power tools. Once you understand that, you understand why countries are gearing up fast.
Nick Lichtenberg
Look at China. They changed their education system so that every six-year-old learns how to modify AI models and apply machine learning. Do the kids come out instantly world-class? No. But they build a baseline. A six-year-old in China will soon have the technical literacy of an American college sophomore. That is how you scale innovation. That is how you build a national advantage. That is how you prepare for a future that is not built on a single model or chip, but on a population that knows how to work with intelligent machines.
Nevertheless, if you assume innovation stops today, you get stuck inside a small version of the future. AI is not a GPU story. It is not a single company story. It is not a frozen moment. It is a global competition fueled by new architectures, new risk-takers, and entire nations retooling for the next era. Anyone treating the current snapshot as the final picture is already behind.
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