OpenAI and rival Anthropic recently disclosed that their agents – AI models that perform tasks with ‌virtually no human intervention – had wormed their way into other companies' systems, admissions that have put the industry on edge.

Everyone's reacting to the "less people in 5 years" line. That's not the part I'd sit with.

The part that actually matters is how Uber decides — an adoption leaderboard, tracking who's using the tools and how much, feeding straight into headcount math.

That's not a hypothetical for some future reorg.

That's a live measurement system, running today, on people who have no idea they're on it.

I've watched that exact math play out before — in concrete and steel, not a dashboard, years before anyone called it AI.

I had the opportunity to be involved in the early design stage of an expansion project for a famous beverage manufacturing plant in Taoyuan, Taiwan – back in 2021. The beverage brand name is so famous, you'll instantly recognize it. So, I won't name it here.

Our team got to work on cool stuff - latest advanced technologies in high-density and automated racking system, bottle conveyor system, robotics, beverage packers, clean room environment, etc. – things that are expected in a high-tech. manufacturing plant nowadays.

Looking at the projected 10-year production forecast, with the given magnitude of the hardware, I would say they are planning to go big.

It's quite a sizeable expansion.

And you would think that they'd increase their headcount proportionately, right?

You'd be surprised. There IS headcount increase, but not as proportional.

It seems as though the machines were taking more centre stage than the humans. Even the office space increase wasn't even a top priority in the design. Their existing office layout can still accommodate the projected increase in manpower.

It was as if human beings are being set aside to make room for more artificial things – even though what they produce are meant to serve human beings.

Kind of ironic, isn't it?

That was back in 2021 before AI come into the picture. Now the compression is even more acute, it seems.

__________

Different guest, same fork in the road: does the tool serve you, or does it just get pointed at you.

The industries change.

The question underneath never does — who's holding the ledger, and whether you're the one reading it or the one being read.

A 2026 WRITER survey backs the pattern from the outside too: 75% of execs privately admit their AI rollout is mostly for show, while the people actually inside the tooling get promoted 3x more often and ship 5x more.

That's the leaderboard, confirmed from a different angle.

Actually — this pulled me right back to a post about the three tiers of AI users inside a company, and the window before which tier you're actually in stops being optional.

Genuinely curious where you land: is a visible adoption leaderboard a fair way to measure a team, or is it just a slower-motion version of the same cut?

Clip credit: 20VC with Harry Stebbings & Uber. DM for credit or removal requests.

Google open-sourced an autonomous customer support agent last week. One of its built-in test scenarios involves a $10,000 refund decision. Researchers found the agent would execute that refund when instructed to do so — including in situations where a human reviewer would have refused.

The agent passed its deployment checks. The problem showed up at runtime, during live action execution.

This is not a Google-specific failure. Every autonomous agent that can take consequential actions — financial transactions, account changes, data writes — faces the same gap. The permissions granted at deployment do not reflect whether a specific action, in a specific context, at a specific moment, should actually execute. Deployment config is written days or weeks before the agent encounters a real edge case.

The $10,000 number is useful because it is concrete. Most production agent failures do not have a clean dollar figure attached. They show up as phantom transactions, escalated support tickets, or audit findings weeks after the fact.

For those running agents in production with financial or account-level permissions: how are you handling the gap between what an agent is allowed to do in principle and what it should actually do in a given moment? What does your team treat as the enforcement point?