AI creating other AI

Where does your value lie if not in the (AI) coding?

Every GTM operator 'needs to' build AI workflows and agents.

Think building them is hard? Think again.

It's becoming more and more simple.

We are at the beginning of a big change.
AI can now create other AI.

That custom workflow that scores and outreach to ICP?
You can build it in few hours with Clay and Smartlead.

That account research algorithm you paid $15k last year?
I've built it in 15 mins with AirOps co-pilot.

___

So, where does your value lie if not in the coding?

First of all, you'll still need coding for advanced stuff.

But here's what imho AI alone can't help with (yet):

1. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴
Which business problems are worth solving based on your unique context and situation

↳ Need to be a hands-on operator, in the trenches

2. 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲
Knowing which AI and workflows are best to solve your specific challenge

↳ Gotta check LinkedIn, X, Product Hunt and stay current

3. 𝗦𝘆𝘀𝘁𝗲𝗺 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴
Architect how multiple specialized AIs can share data inputs and output actions among each other

↳ Gotta know some APIs and data pipelines

4. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
Understanding how to guide the AI to carry out the job in the exact way you want them to

↳ Need to be an excellent, crystal-clear (technical) writer

___

Riccardo Vandra calls this the "Implementation Paradox."
(creds to him who inspired this post!)

He says:

"As building gets easier, knowing what to build becomes exponentially more valuable.

The businesses winning with AI aren't the ones with the best engineers. They're the ones with the clearest problem definition."

I might be biased, but the answer to the above to me is:
GTM Engineering.

They don't just automate because 'we can'.

But they work back from prioritized business challenges.

In 2025, anyone can build AI agents because 'we can'.

But it's much harder to know what's worth building.