The AI Hype is Deafening. Let’s Talk About Reality
Why the early days of AI are still being written by humans, not algorithms.
If you scroll through LinkedIn for more than thirty seconds right now, you’d think the entire global economy has been automated.
Every second post is a "Top 10 AI Prompts You Need Today," a breathless announcement of a new LLM wrapper, or an "AI Guru" explaining how to replace your entire engineering org with a single chatbot.
Let's be completely honest: Most of it is pure noise.
I am not in the anti-AI camp. I use these tools daily, build sandbox logic with them, and believe they are deeply transformative. But right now, we are trapped in a massive hyper-inflated hype cycle. The truth that no one wants to admit on social media is simple: Nobody actually knows exactly how they want to use it yet.
1. The Great LinkedIn Echo Chamber
The current state of AI discussion on professional platforms has become a game of musical chairs. People are racing to post about AI features not because they have solved complex business problems with them, but because they are terrified of looking left behind.
We are seeing a massive "Synthesis Tax" paid on content—thousands of people using AI to generate generic posts about AI, creating an infinite loop of low-signal fluff.
When everyone is screaming about "revolution," the strategic move isn't to join the shouting match. It’s to look at the foundations.
2. It’s Still Early Days (And That’s Okay)
We need to treat AI the same way we treated the early cloud or mobile app eras. In 2008, nobody knew what a "mobile strategy" looked like; companies just built clunky apps because everyone else had one.
Right now, enterprises are forcing AI into workflows just to check a box for shareholders.
They want AI to write project status reports, but they haven't fixed their underlying data hygiene.
They want AI to make strategic decisions, but they haven't defined their North Star metrics.
We are trying to build the roof of the house before we've poured the concrete foundation.
3. Humans are Still Shaping the Machine
Here is the reality that gets lost in the hype: AI does not self-direct.
An LLM is just a highly sophisticated logic mirror. It reflects the data, the constraints, and the intentionality we give it. It is still human practitioners—the program managers, the architects, the strategic thinkers—who have to sit down and design the frameworks.
When I built my Strategic Drift Auditor prompt, the AI didn't magically know how to look for lost ROI. A human had to program the cynical mindset, define the variables, and force the tool to compare a 6-month-old charter with today's realities.
The machine provides the raw computational horsepower, but humans provide the governance.
⚖️ The Balanced View
We need to move away from the binary debate of "AI is taking over the world" versus "AI is useless."
We are in the messy, experimental day-one phase. The winners of this era won't be the people who copy-paste generic prompts from a LinkedIn carousel. It will be the strategic leaders who calmly quietly build custom logic gates, automate their specific administrative burdens, and use the extra mental capacity to do what machines can't: Lead people through chaos.
Cut through the noise. Focus on the logic.
