It's Starting.

FUTURE

The Age of Exploration

The timings couldn’t have been better. Last week, I gave you my best prediction about what was next in AI, “the age of exploration”, putting special emphasis on the growing importance of Reinforcement Learning as the ‘it’ moment for agents.

Luckily for me, two days-old releases, Grok 4 and China’s Kimi K2, have pretty much confirmed our suspicions, as both extremely successful releases represent a step-function improvement for agents.

Not exempt from concerning issues (like the MechaHitler drama), Grok 4 is nonetheless the first of its kind, the first model with exploration-based learning successfully applied at a large scale.

On the other hand, Kimi K2 is quite possibly the best non-reasoning model the world has ever seen, thanks to an incredibly innovative training method, leading to a highly emotionally intelligent model that, guess what, is also open-source.

This has several substantial repercussions I intend to explain today. Among other things, we’ll:

Review the milestones that took us where we are and why these models represent another milestone in the journey to machine intelligence.

Explain why Enterprise AI finally makes sense thanks to the ‘Palantirization of AI’.

Understand why these models will kill human-to-human management

And comprehend why these models will also kill software as it is

This post is not about Grok 4, I talked about that already; it’s about what Grok starts.

Let’s dive in.

Why This Time is Different.

To understand why this week marks the beginning of the new era, the exploration era, we must first go back in time a little bit.

A Change in Behavior

While I will mostly avoid technicalities as today is much more about seeing the future than talking about AI theory, let’s ensure we are all on the same page.

Back in September, OpenAI presented the o1 model, the first of its kind. A Large Language Model that thought for longer on tasks by generating a stream of ‘thinking’ words before answering, which helped it break problems into more straightforward steps that, when combined, yield the correct answer with much higher statistical likelihood.

The reason is obvious; thinking step-by-step makes the problem easier to solve, just like in the example below.

Therefore, the straightforward answer to what made o1 different from everything else we had seen before was that, while all previous models would choose the left route, o1 would respond using the right approach.

Previous to o1, we would usually ask models to ‘think step-by-step’ to induce standard LLMs to generate chains of thought. The key difference is that this is no longer necessary as it’s the default thinking mode for reasoning models like o1/o3 or Grok 4.

But as we learned in our last newsletter post, there’s much more to the change; what makes these models unique is that they were trained in a fundamentally different way, a way that xAI and Grok 4 have taken to a totally new level.

The change is not behavioral; it goes deeper.

The Age of Exploration

While…

Enjoyed the introduction? Subscribe or sign in to continue with this edition.