Anecdotes of the ordinary

Through the Teaching Telescope: Finding Our Focus in the AI Night Sky

swapna mathew

I often feel caught between two loud opinions about AI:
1) If you can’t build with AI, you’ve missed the boat.
2) AI is just a smart assistant—you only need to ask it the right things.

From my grounding in cognitive psychology and now learning AI fundamentals, one thing is clear: the truth sits somewhere in between, and each of us must find that balance based on our maturity, domain, and life experiences.

To make sense of this balance, I use what I call the Teaching Telescope Framework, inspired by Social Learning Theory. It helps us understand where humans excel—and where AI can take over.

The Teaching Telescope: Four Lenses for Learning (and Leading) with AI

🔭 Lens 1: Attention — Choosing the Right Focus (Human-led)

Learning starts with choosing where to point the telescope. Humans still excel here. We decide what data our AI agents should pay attention to by asking:

  1. What problem am I solving?
  2. Why does it matter?
  3. Is it even feasible?

Good focus avoids feeding garbage into the system.

This is where we avoid “feeding garbage into the model.” Our job isn’t to stare harder—it’s to choose better targets.

🧠 Lens 2: Retention — Storing What Matters (AI-led, Human-guided)

AI can retain and retrieve information far better than we can—if we guide what to store and how. We provide structure; AI provides scale. Here, AI tools shine, but we set the rules of the night.

⚙️Lens 3: Reproduction — Acting on What’s Learned (AI-accelerated)

For transactional or repeatable tasks, AI is already outperforming humans. With its emotion-free consistency, AI can also support more complex tasks—yet human judgment still matters. The best results come when we let AI handle the mechanics and keep humans on meaning, trade-offs, and empathy.

🌟 Lens 4: Motivation — Reinforcing What Works (Human-led)

Deciding what outcomes are worth repeating is still a human strength. AI can show patterns, but motivation, ethics, and long-term direction require mature human decision-making. Reinforcement learning runs in machines, but the reinforcement climate—what gets rewarded, what is off-limits, what “good” means—belongs to mature human minds and teams.

The Sky Is Busy, But We Don’t Need to Rush the View

Recent industry data shows about 62% of organizations are still experimenting and piloting AI, far from the “you’re too late” narrative. Despite AI’s origins in the 1940s, this phase is an opportunity to pause, evaluate, and decide what we can safely hand over to our artificial partners—and at what cost.

As someone wisely said, “Do not fear failure; be terrified of regret.” I’d add: regret often comes from rushing a decision you didn’t fully understand.

A Field Guide for Practitioners: Using the Teaching Telescope

Here’s a practical way to apply the metaphor in your next AI initiative:

  • Point (Attention):
    • Write your problem statement in one sentence.
    • Name the user, the outcome, and the smallest valuable slice you can test.
  • Catalog (Retention):
    • Identify the data needed and who owns it.
    • Decide how you’ll store, label, and retrieve it—before you build.
  • Track (Reproduction):
    • Start with a scripted, low-risk workflow and instrument it for quality.
    • Let AI handle the repetitive; keep humans on escalation and exceptions.
  • Reinforce (Motivation):
    • Define “success” numerically and ethically (e.g., time saved and bias checks passed).
    • Decide how frequently you’ll review—and who can say “stop” or “scale.”

Between Awe and Agency

The narrative that “you’ve missed the boat” suggests the sky has already been charted. The claim that “AI will do whatever you ask” assumes you know exactly what to ask. Both miss the deeper truth:

Leadership in the age of AI is the art of choosing what deserves attention, curating what we retain, orchestrating how we act, and deciding what we reward.

As 2026 rises on the horizon, my intention is simple: observe, learn, and adapt—with curiosity as my compass, and a teaching telescope in hand. The sky isn’t empty, and it isn’t fully mapped. It’s alive with possibilities. The view you get depends on where you stand, how well you focus, and the wisdom to know when to let your instruments track—and when to take your eye off the scope and look up.

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