In the mid 90s, the Netscape browser introduced most of the world to the web and brought on the Internet era. Microsoft, which was the dominant tech company at the time, famously reacted by bunding its Internet Explorer browser into the Windows operating system and started taking share from Netscape.

Eventually, Internet Explorer became the dominant web browser and Netscape was sold to AOL.

Ironically, that battle for Internet dominance missed that the most important piece of software was the search engine, not the browser. And so the winnner ended up being an entirely different company - Google.

As Mark Twain famously told us:

History doesn't repeat itself, but it does rhyme

Google's purchase of DeepMind back in 2014 was the first shot fired in the foundation model battle. Google went on to develop the transformer architecture and it shared that idea with the world in a paper written in 2017. OpenAI took that architecture and started training large language models and introduced GPT1 in 2018.

What OpenAI was willing to do, that Google was not at that time, was to train its large language models on the entire Internet, regardless of who owned the content. I believe Google's lawyers and top executives, under massive scrutiny from DC and around the world, were not willing to go that far.

And so OpenAI developed a significant lead in large language models, leading to the launch of ChatGPT in late 2022. That moment was the equivalent of the Netscape browser and ChatGPT introduced usable artificial intelligence to the world.

And, like Microsoft thirty years before, Google "woke up" and eventually caught up. With the recent introduction of Gemini 3, most observers believe that Google has erased OpenAI's lead, at least for now.

And just like Microsoft, Google is also bunding Gemini in every consumer surface area it owns. This browser that I am typing into has the Gemini Icon in the upper right corner and I use it to draw the header images to my posts.

Wall Street is getting increasingly concerned that the current AI mania will burst and bring the entire market down with it. Silicon Valley brushes that concern off, and VCs and big tech companies continue to pour money into AI in search of big payoffs.

So who is right?

At times like this, I like to turn to the data and ignore the prognosticators.

Evan O'Donnell is a VC and blogger who took it upon himself to build a model that looks at the rate of growth of inference token usage and compares that to infrastructure investment and comes up with some answers. This post details that approach.

But what I like most is Evan's dashboard, which you can see here.

My only critique of this approach is that the data is not real-time. Not even close. When I asked Evan about that via email this past weekend, he said:

No material update on the token numbers.

As of Sept/Oct, token consumption is growing at ~13% monthly across providers (down from 30-40% earlier this year). I'm tracking everything