Thoughts on o3 and o4-mini

FUTURE

As you’ve probably heard or seen by now, OpenAI has released its new flagship reasoning models, o3 and o4-mini, which are heralded by some as a huge leap in model intelligence. Some, like famous economist Tyler Cowen, claim the arrival of AGI.

Thus, today, I’m going to give you all that is known, mine and that of the community, on these models. There have been two totally different reactions:

Those that claim OpenAI has built the closest thing we have to AGI, or even AGI,

Surprisingly (or not), those who claim these models prove that OpenAI is no longer in the lead.

Two wildly different reactions, right? But which one is correct?

Today, we are going beyond the hype to truly understand this release from a more holistic view, enough to make up your mind of what to do of all this, mainly:

Describe reasoning models in clear fashion to understand why reasoning models are now dictating how hardware is built, and not otherwise, helping us comprehend and predict hardware company roadmaps,

How o3 is ironically an extra step into China’s AI plan,

Understanding o3/o4-mini’s biggest new feature, thinking with images,

The fundamental limitations these models continue to exhibit,

An updated ‘state of the art of AI analysis’. The hard data that OpenAI doesn’t want you to see,

And one last reflection on what all this means geopolitically, considering the future of the US dollar in what are dark times for the world’s reserve currency.

Let’s dive in.

A Primer on Reasoning Models

First and foremost, let’s settle the grounds around reasoning models. As you probably know, modern AI assistant models like ChatGPT work by responding to a sequence of text, pixels (images), or video that you provide them.

For example, ChatGPT will respond with a sequence of words (text) or a sequence of pixels (images) to any question or request you may have.

But what makes reasoning models particularly unique?

Reasoning Models 101

The core working principle of LLMs is that they are trained to provide a semantically valid response to the user’s input. If the user asks a question, the assistant answers that question, not something totally unrelated.

With non-reasoning models, this response is immediate; the model does not reflect on the question and simply responds with the most likely continuation to that input.

This worked great for knowledge-related tasks. If the model knows the response, it only makes sense that the response is fast and immediate; there is no added benefit to thinking longer about a task.

Think of this from a human perspective:

Thinking for longer on a task such as ‘give me the capital of Poland’ doesn’t make it more likely you answer correctly if you don’t know Poland’s capital. Either you know or you don’t know (yes, you can take some time to recall the answer, but you’re still depending on knowing the answer or not).

As another example, answering “2+2” is such an intuitive, memorized response that you can respond immediately without…

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