“Human in the loop” (HITL) is the most overused phrase in AI today. It’s also the most misunderstood.
In many workflows, the human has become a mechanical bottleneck – a “rubber stamp” meant to click ‘Approve’ or ‘Next’ without truly engaging. This isn’t just a waste of talent; it’s a recipe for mediocrity.
In 2026, we don’t just need a human in the loop. We need an Expert Human in the Loop.
The difference? • HITL (Mechanical): Checking for typos or formatting. Approving output because it “looks right.” • EHITL (Expert): Challenging the AI’s logic. Applying domain-specific nuance. Spotting the subtle hallucinations that only a pro with 10+ years of experience can see.
AI can give us the 80% in seconds. But that final 20% – the part that actually moves the needle – requires us to apply our expertise to the AI, not just after it.
Don’t just check the AI’s homework. Teach it how to think.
“Isn’t it funny how day by day nothing changes, but when you look back, everything is different?” – C.S. Lewis.
Looking back at the trajectory of AI from 2024 to 2026, the transformation is staggering. We’ve moved from simple chatbots to sophisticated Multi-Agent systems. We are no longer just “using AI”; we are orchestrating it.
Welcome to the ‘Agent Manager’ era, where the most valuable skill is no longer just ‘prompting’ – it’s ‘intent engineering.’
To thrive, we must adapt:
• From Chatbots to Agents: GPT-5.4 and Claude 4.6 now execute complex workflows autonomously. • The Multi-Agent Norm: Orchestration (via OpenClaw) mirrors high-functioning human teams. • Human Edge: Our value lies in judgment, curation, and defining clear goals.
Charlie Munger famously said: “All I want to know is where I’m going to die, so I’ll never go there.”
When it comes to implementing AI, most leaders are desperately trying to figure out how to be brilliant. They want to disrupt the market, revolutionize their workflows, and implement the most bleeding-edge models available.
But Munger’s philosophy of inversion suggests a much more practical starting point. Instead of asking how to win, ask: How do we guarantee this AI project will completely fail?
If your goal is to kill an AI initiative, here is the exact playbook:
Start with the tech, not the problem: Buy an expensive enterprise LLM license first, then wander around the company looking for a vague use case to justify the cost.
Ignore your data foundation: Assume the AI will magically untangle years of undocumented, siloed, and messy legacy data. Garbage in, garbage out – at scale.
Remove the human immediately: Automate a high-stakes, customer-facing workflow end-to-end on day one without a “human-in-the-loop” to catch the inevitable edge cases or hallucinations.
Skip change management: Drop a powerful new AI tool on your employees’ desks without training them on how to use it, or adjusting their KPIs to reflect their new workflows.
In the rush to adopt AI, the tech world is obsessed with seeking brilliance. But in complex systems, avoiding stupidity is often the faster path to ROI.
Don’t ask how AI is going to make your company a genius. Figure out what will cause the implementation to die – and then just don’t go there.
Right now, we are living through an era of technological whiplash.
The advancements we are seeing in Artificial Intelligence aren’t just incremental; they are fundamentally rewriting how we work and live.
Consider the recent leaps happening right in front of us:
• 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗔𝗴𝗲𝗻𝘁𝘀: We are moving past chatbots that simply answer questions to autonomous digital assistants that can plan, reason, and execute multi-step workflows on our behalf.
• “𝗩𝗶𝗯𝗲 𝗖𝗼𝗱𝗶𝗻𝗴”: We are shifting from writing strict, line-by-line syntax to guiding AI with natural language and intent—letting the machine build the software while we direct the vision.
• 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀: AI is rapidly breaking out of the screen and into the physical world, interacting with our environments in increasingly capable, autonomous ways.
The pace of this development is staggering. And naturally, it makes a lot of us want to cling to what we know.
We try to fit these paradigm-shifting tools into our existing, comfortable boxes. We treat powerful agents like fancy search engines, or we view AI coding through the rigid lens of traditional software engineering.
But here is the hard truth: 𝗢𝘂𝗿 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘁𝗼 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗰𝗿𝗲𝗮𝘁𝗶𝘃𝗶𝘁𝘆 𝗼𝗿 𝗮 𝘀𝗵𝗼𝗿𝘁𝗮𝗴𝗲 𝗼𝗳 𝗻𝗲𝘄 𝗶𝗱𝗲𝗮𝘀. 𝗜𝘁 𝗶𝘀 𝗼𝘂𝗿 𝘀𝘁𝘂𝗯𝗯𝗼𝗿𝗻 𝗿𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝘁𝗼 𝗮𝗯𝗮𝗻𝗱𝗼𝗻𝗶𝗻𝗴 𝗼𝘂𝘁𝗱𝗮𝘁𝗲𝗱 𝗺𝗲𝗻𝘁𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀.
To truly leverage this era of AI, we have to let go of HOW we used to do things and focus entirely on WHAT we are trying to achieve.
So, how do we escape the old ideas?
• 𝗚𝗲𝘁 𝘆𝗼𝘂𝗿 𝗵𝗮𝗻𝗱𝘀 𝗱𝗶𝗿𝘁𝘆: Don’t just read about the tech. Pick one AI tool or agent and use it for a real task today.
• 𝗘𝗺𝗯𝗿𝗮𝗰𝗲 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀: You will prompt it poorly at first. It will make mistakes. You will get frustrated. That is a necessary part of the learning curve. Learn from those errors to understand how the machine “thinks.”
• 𝗜𝘁𝗲𝗿𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄: Stop asking, “How can AI do my old process?” and start asking, “What does a completely new, optimized process look like now that I have AI?”
The future belongs to those willing to unlearn just as fast as they learn.
What outdated mental model are you trying to let go of right now? Let me know in the comments! 👇
Ten years might feel like an eternity in our daily routines, but in the timeline of #𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, it’s a blink of an eye that changed everything.
Just ten years ago this week, the world tuned in to a historic confrontation: #𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱’𝘀 #𝗔𝗹𝗽𝗵𝗮𝗚𝗼 𝘃𝘀. 𝗚𝗼 𝗚𝗿𝗮𝗻𝗱𝗺𝗮𝘀𝘁𝗲𝗿 𝗟𝗲𝗲 𝗦𝗲𝗱𝗼𝗹. It was a watershed moment that redefined our understanding of machine intelligence.
The match wasn’t just about a computer winning a game; it was about the profound “creativity” and “resilience” displayed by both sides:
• 𝗠𝗼𝘃𝗲 𝟯𝟳 (𝗚𝗮𝗺𝗲 𝟮): AlphaGo placed a stone in a location no human expert would have ever considered. It was a move so “inhuman” it shocked the commentators, yet it ultimately proved to be a stroke of strategic genius.
• 𝗧𝗵𝗲 “𝗗𝗶𝘃𝗶𝗻𝗲 𝗠𝗼𝘃𝗲” (𝗚𝗮𝗺𝗲 𝟰): Just when it seemed the machine was invincible, Lee Sedol played Move 78 – a brilliant, unexpected wedge that confused the algorithm and secured a victory for humanity. It was a stunning display of human spirit and the ability to find a path where none seemed to exist.
Since that week in 2016, the pace of AI advancement hasn’t just continued—it has accelerated exponentially. We are no longer just watching AI play games; we are working alongside it to solve complex global challenges.
If you haven’t seen it yet, I highly recommend watching 𝘁𝗵𝗲 𝗔𝗹𝗽𝗵𝗮𝗚𝗼 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲. It is a gripping, emotional look at this turning point in history.
The last decade proved that AI can surprise us, but the “Divine Move” reminded us of the unique power of human ingenuity. Now is the time for us to work together, leveraging these tools to make the 𝗯𝗲𝘀𝘁 𝗺𝗼𝘃𝗲 𝗼𝗳 𝗼𝘂𝗿 𝗹𝗶𝘃𝗲𝘀.
How has your perspective on AI changed since that 2016 match? Let’s discuss in the comments.
