You've felt it.
You walk into a meeting and before anyone speaks, you know something is off. Shoulders are tight. Eye contact is being avoided. The air has weight.
No one said a word. You just absorbed it.
I use AI constantly. It's remarkable at cognition.
But this?
The room has a temperature, and humans can feel it.
That's the part we rarely discuss when we debate artificial general intelligence or AGI.
It makes me wonder if we've been measuring the wrong thing all along.
THE PROBLEM ISN'T PROCESSING. IT'S PRESENCE.
The AGI conversation keeps circling the same questions:
Can machines reason? Plan? Generalize? Learn faster?
But in real life, especially at work, competence often looks like something else entirely:
* Reading the room before anyone speaks
* Knowing what role you're in without being told
* Adjusting tone, timing, and stance without conscious effort
* Detecting what's not being said
Michael Polanyi called this "tacit knowledge," the things we know but can't fully articulate:
"We can know more than we can tell."
We know more than we can tell.
Which means a lot of what matters can't be fully reduced to training data.
THE INVISIBLE OPERATING SYSTEM
Human society runs on knowledge that's never written down.
A parent scans for risk automatically. Not calculated, just inhabited.
When a CEO walks into a boardroom, everyone recalibrates posture, tone, willingness to challenge. No one announces the shift. It just happens.
When you sit in the passenger seat, you don't reach for the steering wheel. You become passenger without deciding to.
None of this is processed consciously.
It's learned through a lifetime of consequences. Watching, absorbing, adjusting, and occasionally getting it wrong in ways that cost you something.
That cost is the teacher.
And machines don't pay it in the same way.
THE ROLE-SWITCHING WE DO ALL DAY
Here's what I find hardest to imagine replicating:
In a single morning, a person might move through completely different modes.
Caregiver getting children ready for school. Passenger trusting a driver. Junior colleague deferring in one room. Team leader making a call in another. Friend offering support. Negotiator reading the other side.
The shifts are fast. Often invisible.
Your posture changes. Your voice changes. Your risk tolerance changes.
You don't open a manual. You don't announce "switching roles now."
You just become what the moment requires. Instantaneously, unconsciously, completely.
The parent at breakfast isn't the same self as the passenger on a flight, or the direct report in a boardroom.
Same brain. Different being.
That's not intelligence. That's being.
And being isn't on any benchmark.
WHY THIS IS HARD TO TRAIN
You can label behaviors in hindsight:
Who spoke. Who stayed quiet. Who interrupted. Who deferred.
But the difficult part is what sits underneath behavior:
* The felt sense of risk or safety in a room
* The meaning of a particular silence
* The social cost of saying the exact same sentence to different people
* The fact that "right" and "effective" diverge depending on context, relationship, and history the AI wasn't present for
Even if we recorded every meeting on earth, the target isn't stable.
Organizations have different cultures. Trust is earned and broken over years. "Appropriate" depends on relationships that exist outside the data.
So the question isn't: can a machine mimic the pattern?
It's: can it reliably participate in human social reality, across contexts, over time, while being accountable for the impact?
That's a different problem than passing benchmarks.
And I haven't seen a credible roadmap for it yet.
THE COUNTERARGUMENT I CAN'T DISMISS
But I have to be honest with myself.
Am I making the same mistake people made when they said AI wouldn't write code or pass professional exams?
Am I defending something that only matters because the world still looks like this?
Last week, Cursor shared an experiment: long-running coding agents aimed at building a web browser from scratch, running for close to a week and producing a codebase north of a million lines.
The CEO's own summary was basically: it kind of works, but it's still very far from WebKit/Chromium parity.
McKinsey's CEO says the firm is already running around 25,000 AI agents alongside about 40,000 humans, and wants every employee enabled by at least one agent within the next 18 months.
And investors are increasingly explicit about the direction of travel. In a TechCrunch survey of enterprise VCs, multiple people predicted 2026 is when agents start shifting software from boosting humans to automating work itself in some areas.
Everything I've written assumes humans keep doing work in human ways. Rooms. Relationships. Unspoken signals. Face-to-face tension.
But here's what I keep coming back to:
Maybe AI won't learn to be in the room.
Maybe the room just disappears.
Think about it. Remote work already fractured the room into boxes on a screen. Async communication means we're rarely in the same moment. AI mediating conversations means there's always a third party interpreting. When half the participants might be agents, the social physics change entirely.
The signals we evolved to read, posture, micro-expressions, the temperature shift, they don't transmit cleanly through the interfaces we're building.
We didn't teach AI to read the room. We just stopped having rooms.
And this is what previous technology shifts have taught me: every major technology destroys the context that made the previous skill valuable.
GPS didn't learn to navigate like humans. It made navigation irrelevant.
Calculators didn't learn mental math. They made mental math unnecessary.
Search engines didn't learn to remember. They made remembering obsolete.
Maybe AI won't learn presence. Maybe it just makes presence obsolete.
The first-order effects of technology are usually predictable. The second and third-order effects blindside everyone.
People predicted smartphones would put powerful computers in our pockets. Fewer predicted the downstream effects: boredom disappearing, dating restructuring around apps, childhood and adolescence being reshaped by screens.
People predicted social media would connect us. Few predicted how it would fragment consensus reality and make truth tribal for many communities, while increasing loneliness for a lot of people.
So maybe the question isn't whether AI will participate in human social reality.
Maybe it's: what happens to human social reality when AI is everywhere?
Maybe we lose the ability to read rooms because we stop practicing.
Maybe trust becomes harder to extend because we can't tell who's real.
Maybe social roles collapse because no one knows who's supposed to lead or follow when half the room isn't human.
The room isn't being entered by machines. The room is being dismantled.
We might need an entirely new discipline. AI sociology, machine anthropology. Something to understand what happens to humans when we're always in rooms with machines. Or when we stop having rooms at all.
We haven't even started building that vocabulary.
I don't know. None of us do.
We haven't lived in that world.
WHERE I LAND
I'm not saying AGI will never arrive.
The ground is shifting faster than anyone predicted. The economics are undeniable. The results keep proving themselves.
But if we define general intelligence as the ability to reliably participate in human social reality, to feel what's unspoken, shift roles, navigate relationships, and carry accountability over time...
I'm not convinced it's purely a software milestone.
There may be something else required.
Something that emerges from having stakes, having a body, having relationships that can break.
Something that comes from being a person among people for a long time.
Or maybe the world just restructures around the technology, the way it always does, and the question becomes irrelevant. Maybe the room disappears, and we forget we ever needed it.
The machines are getting smarter every month.
But I've never seen one walk into a room and feel the tension before a word is spoken.
Same cognition. Different be ing.
At least while rooms still exist.
Sources:
Cursor blog: https://cursor.com/blog/scaling-agents
The Register coverage: https://www.theregister.com/2026/01/22/cursoraiwroteabrowser/
McKinsey agent count (Business Insider): https://www.businessinsider.com/mckinsey-workforce-ai-agents-consulting-industry-bob-sternfels-2026-1
TechCrunch VC survey: https://techcrunch.com/2025/12/31/investors-predict-ai-is-coming-for-labor-in-2026/