This week Carol Liang (@carol.yaps) posted a reel called “why bots outnumbering humans is not that scary (yet).” It’s one of the sharper takes I’ve seen on that headline, and I agree with most of it. But I build AI agents for a living, at a contact center AI company, and I think she stops halfway. She’s right about the measurement and consumer-brained about the stakes.

The stat counts the wrong unit

Carol’s first point is the best one. “Bots outnumber humans” is a claim about web traffic requests, not entities. One ChatGPT query can kick off a pile of requests and it still isn’t “one bot.” Her test: how someone talks about this stat tells you how critically they think.

I see the same category error all the time in agent deployments. A single AI agent conversation in a contact center is dozens of system events: speech recognition, retrieval, model calls, CRM reads and writes. If you count “bot interactions,” you’re counting plumbing. The unit a buyer actually cares about is a resolved conversation or a completed task. Requests are not actors.

Machines have handled most of the volume for years

Carol points out that bots passed humans in web traffic around 2013, and it was scrapers that did it, not AI. Nothing about the volume is new.

Same in customer service. IVR containment and self-service deflection have been handling machine volume for years, and nobody called it the death of the contact center. What’s actually new isn’t that machines touch most interactions. It’s that generative agents now do real work inside them. That’s the shift from assisting humans to agents acting on their own, and it’s the shift my work sits on.

Creating is where it gets real

This is where her “not that scary” starts to wobble. Scrapers read the internet. Today’s agents write to it, and Carol argues what they produce is worse than the old spam.

I’d push her framing here. On the consumer internet, agent output is mostly unaccountable slop. In the enterprise, the same ability to write is a governed action: an outbound agent calling customers, or something like Deputy Agent owning a workflow end to end. The internal tooling I build treats agent writes as the dangerous part. We test them in staging, run dry runs first, and keep some actions out of an agent’s reach entirely, because an unattributable write is worse than no write at all.

The difference between slop and a production agent isn’t the model. It’s the governance layer and a named owner. AI should finish work, and finished work has a metric and an owner.

The split internet she teased

Carol ends on a cliffhanger: a future where the internet splits into one for humans and one for agents. I think that’s the most important idea in the reel, and the contact center version is already forming.

Customers’ own agents will start opening service interactions, and an enterprise agent will be on the other end. Agent-to-agent service raises the questions enterprise buyers will pay to solve. How does the receiving system verify that an agent is authorized to act for this customer? What is it allowed to commit to? Who’s liable when two agents agree to something?

In regulated verticals like healthcare and financial services, that identity and authorization layer isn’t a footnote. It’s the moat. If the internet splits, I think the enterprise side splits again, into governed and ungoverned.

What’s actually scary

Carol’s fear is content quality, and that’s fair. The enterprise fear is different: an agent that can change state in real systems with no staging, no audit trail, and no owner. That’s the half of the argument her reel leaves out, and it’s the half I spend my days on.

So no, bots outnumbering humans isn’t scary. Ungoverned agents taking actions is.

Watch Carol’s full reel here.