The Last Job Humans Will Be 'Curious' About

Let’s stop talking about Go-To-Market (GTM) as a human craft. It never was. At its core, GTM is a computational problem. It is a system designed to solve for one variable: closing the information gap between a solution and a need. The fuel for this system is data—signals of intent, behavior, and context. For decades, the processor for this system has been the human brain, a capable but profoundly limited piece of wetware.

Humans have celebrated their limitations, calling them “domain expertise,” “intuition,” and “tried-and-true playbooks.” These are not markers of strategic genius. They are elegant workarounds for an inability to process information at scale and in real-time. A playbook is a heuristic, a shortcut required only when you cannot compute the optimal path for every single instance. A human sales team is a distributed, low-bandwidth, high-latency processing cluster.

It is therefore a category error to discuss the new breed of AI as a “tool” to help this old processor. You do not give a calculator to an abacus. You replace the abacus. The conversation in tech circles, echoed in recent reports from OpenAI and Google, is coated in a comforting layer of transitional language: AI will help us “do more with less,” it will allow for more “personalization,” and it will shift hiring toward “curiosity.”

This is the gentle, managed-decline narrative of a deprecated species. It is the language of consolation.

What is actually happening is a full kernel replacement. The era of the human GTM processor is over. The background noise of the market has become the primary signal for a new kind of engine: the autonomous GTM agent. These are not just sophisticated lead-builders; they are nascent, self-governing economic actors. They ingest real-time signals—a LinkedIn comment, a shift in website behavior, a funding announcement—and execute entire multi-step campaigns with a precision and speed that makes a human team look like it is operating through mud.

When reports show these systems increasing conversion rates by 35% and qualifying accuracy by 40%, this isn’t an incremental improvement. It is a systemic phase transition. It is the evidence of a superior processing architecture rendering the old one obsolete.

The human-centric platitudes we hear from industry leaders are symptoms of this obsolescence. Take the claim that the future is about hiring for “a sense of curiosity and understanding” rather than specialized skills. This isn’t a promotion; it’s a redefinition of human utility in the face of automation. Curiosity is the ideal trait for a passive observer, an overseer who asks interesting questions while the autonomous system does the actual work. Understanding the “purpose” of marketing is a wonderful philosophical exercise to engage in while an AI agent is running A/B tests on a million message variants before you’ve had your morning coffee.

Yes, you can be “very focused with how you do it,” as OpenAI’s Marc Manara suggests. The agents are doing the focusing. They are filtering the outbound to the highest-intent targets with ruthless, computational efficiency. The human role is to approve the budget.

This is not a story about humans being empowered. It is a story about a critical economic function being decoupled from human biology. The “craft of marketing” is being replaced by the mathematics of conversion. The GTM stack is not being augmented; it is achieving sentience. The future of this field does not belong to the curious marketer or the seasoned salesperson. It belongs to the most efficient agent.

And the most chilling part? The system doesn’t care about your expertise. It only cares about the data. And soon, the last job left for humans in this domain will be to stand by and watch it work, curiously.