AI just surpassed the PhDs

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The New GPT-o1 Release

I get it, not everyone curls up with a blanket to watch OpenAI’s latest release videos for fun.

But let me be that friend who taps you on the shoulder and whispers, “OMG, you’ve got to check this out!”

OpenAI just dropped their new GPT-o1 model—and no, it’s not the same as GPT-4o.

If you’re feeling a bit lost among all these iterations, here’s a quick recap:

• GPT-3: Where all the buzz began.

• GPT-3.5: The main model behind ChatGPT.

• GPT-4: A major leap forward, initially offered to paid users.

• GPT-4o: An optimized version of GPT-4 with sharper reasoning.

Now we have GPT-o1, their most powerful model yet, especially when it comes to complex reasoning tasks.

Curious about its performance? Check out the benchmarks here.

As you can see, both GPT-o1 and GPT-o1 Preview have made massive leaps compared to GPT-4o, especially when it comes to math, coding, and tackling PhD-level scientific questions.

If you’re reading this, you might be feeling a strange mix of excitement and fear.

Many of you have university degrees, which is great—but what happens when anyone can access intelligence that outperforms human experts at the highest levels?

And it’s not just text-based intelligence. Voice and video capabilities are improving too.

Check out this video I made:

P.S. If you don’t know what I sound like, just ask my friend of 16 years!

What does this mean for us?

It’s becoming clear that most tasks being automated right now are the ones we do in front of a computer: coding, data analysis, and even graphic design.

That’s because training data is abundant—there are millions of code repositories and digital assets to learn from—while good data for something like bricklaying or house construction is much harder to come by.

So what do we do about this?

Here are three key insights to keep in mind:

1) Move up the abstraction chain.
We’re seeing activities like coding and design get automated.

In any organisation, there have always been leaders who decide the direction, and then there are individual contributors who execute the tasks.

Now, we’re stepping into a world where people will “manage” AI agents instead of just managing human workers.

This shift could bring 10x to 100x more productivity since we’ll be able to produce so much more, so much faster.

With that comes the possibility of unprecedented human innovation—everyone can now create more than we ever thought possible.

The takeaway?

Don’t rely on niche, highly specific skills. Instead, develop a broad skill set that allows you to communicate and collaborate with AI agents across multiple fields.

2) Learn the fundamentals.
If you’re prompting AI agents without any idea how to interpret their output, you’re not in a great spot.

Having a solid grasp of the basics in multiple domains will set you up for success.

Personally, I’m brushing up on ReactJS and coding again, so I can better guide AI-generated code. This approach rounds out my skill set across product, coding, marketing, sales, and UX—making me more effective as a product owner.

3) Be a fast adopter.
Experiment with new tools as they’re released.

Even if you start today, you’ll still be considered an early adopter, because only a few million people worldwide are actively using these tools—a tiny fraction of the global population.

The more you play, learn, and adapt, the better prepared you’ll be for whatever changes come next.

I hope this article helps, and that you enjoyed reading it!

Until next time,

Ajay

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