Stepping Out of the Middle: How AI Lets You Scale Yourself
Hey there,
Welcome to the first edition of The AI Signal, where we cut through the hype to focus on what actually moves the needle for leaders like you.
I'm kicking things off with a concept that has reshaped how I work, drawing from a sharp observation by Balaji Srinivasan: "AI doesn’t do it end-to-end. It does it middle-to-middle. The new bottlenecks are prompting and verifying."
Let me unpack that.
AI excels at the "middle"—the grunt work of processing data, generating drafts, or connecting dots. But it doesn't magically start from a vague idea, nor does it guarantee a flawless finish. Humans must own the Beginning (crafting precise inputs) and the End (scrutinizing outputs). Skip those, and you're just amplifying noise.
Scaling the Self vs. Managing a Team
This hit home for me after years of building businesses supporting the data needs of financial services firms. I historically managed teams to get things done – delegating tasks, reviewing results, iterating. That was the way to scale: by adding people.
But my own "deep work" – product strategy, complex problem solving – was different. I was always reluctant to outsource this because the nuance was too easy to lose in translation.
Then LLMs entered the picture.
These models became my most game-changing tool not because they think for me, but because they let me scale myself. Instead of directing others, I direct the AI: defining clear goals, letting it handle the heavy lifting in the middle, and rigorously validating the results.
A Real-World Example
Say I'm prepping for a client audit on data workflows. I don't just prompt "summarize this PDF."
The Beginning: I outline the key angles—compliance risks, efficiency gaps, ROI potential—and feed that structure to the AI.
The Middle: The AI pulls insights, synthesizes perspectives, and drafts sections.
The End: I cross-check against my expertise, tweak for nuance, and ensure it aligns with business realities.
Suddenly, I'm handling what used to take a team, freeing up headspace for bigger plays like refining AI document parsing pipelines.
How to Step Out of the Middle
Here is the framework to scale without losing your edge:
Master the Beginning (Context & Concepts First): Don't just give instructions; give the AI a worldview. I start by defining the specific "buzzwords," labels, or mental models the AI must use to understand the data. If you want it to analyze a transcript, first define the specific criteria for "High-Risk" vs. "Low-Risk" indicators. You are teaching it how to think about your specific problem before asking it to do the work.
Own the End (Validation vs. Review): Never just read an output and ask, "Does this look okay?" You need to go in with a pre-conceived notion of what "right" looks like. Does the output match the structural and logical expectations you set? If you expected a JSON list of five distinct anomalies and got a paragraph summary, it failed. Compare the result against your internal benchmark, not just for grammar, but for logic.
Iterate Wisely: If the output misses the mark, it’s usually because the context was weak or the concepts weren't clearly defined. Don't settle for "good enough for a bot." Refine your definitions and prompt again. This loop mirrors leading a team—if the result is bad, clarify the strategy.
The Takeaway
If you're feeling FOMO (fear of missing out) about AI, start here: Pick one routine task, apply this middle-out approach, and watch how it frees you to focus on what only you can do.
Curious to build your own workflow? My AI Impact Session is designed for exactly this – whether it's upgrading your personal stack or auditing a business bottleneck. [Link: Let's chat.]
Until next time, stay in the signal, not the noise.
—Ben
