AI already writes code, ships it, and pages itself when things break. If that trend keeps sprinting, we might wake up to an org chart where the only humans left are the managers. What does that world feel like, and would you still want to lead in it? Grab a coffee, stroll through this quick thought experiment, and see where you land.

Monday Stand-Up, No Humans

It’s 10:00 a.m. on Monday. You join the team stand-up, coffee in hand. The familiar grid of faces isn’t there. Instead, every tile shows the same neutral avatar and a label: Frontend-Agent, Backend-Agent, DevOps-Agent, QA-Agent. You’re the only human present on the call.

The meeting still runs. Frontend-Agent posts yesterday’s commit summary in chat. DevOps-Agent shows a status report: latency is green, cost is amber, security scan found one high-severity issue. Backend-Agent already opened a patch. There is an eerie silence on the call, but work moves.

You clear your throat and ask if anyone needs help. Four chat bubbles appear at once – perfectly formatted, each ending with a polite “request complete.” No jokes, no small talk, no “weekend was great.” Just data.

You feel two things at the same time: relief that everything is handled and a strange tug of loneliness. The stand-up ends in four minutes, records itself, and drops the notes straight into Slack. As you close the tab, one thought sticks: If the team no longer needs pep talks or kudos, what exactly does it need from me?

The Zero-Headcount Org Chart

Picture the org chart pinned on the wall outside the office. The top row looks normal: CEO, CTO, VP Engineering. Under the VP sits you and two other EMs. Everything below that line is different.

Instead of squads of people, you see stacks of named agents:

• Frontend-Agent Pod – handles React, design tokens, bundle size.

• Backend-Agent Pod – owns APIs, data models, migrations.

• DevOps-Agent Pod – watches infra, autoscaling, cost flags.

• QA-Agent Pod – writes tests, fuzzes endpoints, files bugs.

Each pod is really a swarm of smaller models: one plans work, another writes code, a third reviews, a fourth rolls back bad deploys. The org chart looks clean – just a handful of boxes. But under every box sits a cloud of compute you pay by the second.

Lines on the chart are strict. Pods talk to pods through versioned contracts. No hallway chats, no ad-hoc favours. You can spin up a new pod – say, Data-Science-Agent Pod in minutes: pick a template, set guardrails, give it access tokens, and order it to join the graph. Off it goes.

The surprise isn’t how much work gets done. It’s how little space the chart needs. What once took a dozen rectangles now fits in a single slide. Fewer names, fewer arrows – yet the system runs 24/7, waiting for your next prompt.

Managing Bots: A Typical Day

06:30 – Dashboard coffee. You wake the laptop before you wake yourself. Overnight, Backend-Agent shipped two minor features and rolled back a flaky PR without paging you. DevOps-Agent posts a cost spike alert, already tagged with a savings plan.

09:00 – Sprint planning, prompt style. Instead of a meeting, you drop a prompt into the planning channel: “Goals: cut checkout latency by 20%, add audit logs, clean up feature flags.” In seconds the agents break it into tickets, estimate effort in compute hours, and line up a pull-request sequence.

13:00 – Outage drill. A new model deploy collides with an old schema. QA-Agent fails a test, Backend-Agent proposes a hotfix, DevOps-Agent stages it in a blue-green slot. Your job: review the diff, check the guardrails, hit approve. No yelling on Zoom, just a quiet thread of JSON messages.

16:00 – One-on-one with metrics. You used to coach humans; now you coach reward functions. You tweak the latency budget, raise the fraud-detection weight, and rerun evaluation. The agents retrain on the fly and publish fresh baselines.

18:30 – Status memo. You paste three Grafana snapshots, add two sentences of context, and send the weekly report up the chain. The real story lives in logs, but leadership still wants a human voice.

Day over. No surprise pings, no last-minute PTO requests – just a humming stack of silicon teammates, waiting for the next line of instructions.

Wins to Brag About, Risks to Fear

The Wins

• Speed on demand. Ideas ship the same day you type them.

• Infinite scale. Need ten more micro-services? Clone a pod. Costs rise linearly, not exponentially.

• No sick days or vacations. Coverage is 24/7, holidays included.

• Predictable overhead. Payroll turns into a clear compute bill; finance loves the line item.

• Quiet focus. No stand-up tangents, no office politics, no “quick sync” meetings.

The Risks

• Accountability fog. When a bug hits prod, the blame trail ends at a JSON log.

• Model drift. Agents learn from fresh data – sometimes the wrong kind. Quality can slide without warning.

• Security blind spots. A clever prompt injection can jailbreak the whole pod before alerts fire.

• Ethics at scale. Bias or bad training data propagates faster than you can draft a policy.

• Human skill decay. Fewer engineers writing code means slimmer benches for on-call disasters.

• Culture void. High output, low soul. Teams bond over problems; agents don’t.

The EM Role in an AI-First World

Your title stays the same, but the job description flips.

From coaching people to curating agentsYou no longer unblock careers; you unblock pipelines. You spend mornings tuning reward functions and evenings pruning under-performing models. Performance reviews turn into accuracy dashboards. Promotions become larger GPU quotas.

From headcount planning to compute budgetingThe old question was “How many engineers do we hire?” Now it’s “How many tokens can we afford?” Finance still wants predictability; you translate feature roadmaps into cloud-credit forecasts.

From culture builder to guardrail authorHumans learn values from stories; agents learn them from rules. You write the policies that keep models inside legal, ethical, and brand boundaries. When edge cases pop up, you patch the playbook – fast.

From meeting host to signal wranglerInstead of stand-ups, you skim metrics: latency, drift, spend, bias score. Your reflex is to ask why a graph moved, not who dropped the ball. Root-cause analysis is part log-digging, part prompt interrogation.

From people manager to storytellerExecutives, customers, and regulators still need a human voice. You turn walls of JSON into a narrative they trust – what shipped, what broke, why it matters. In a room full of bots, the soft skills don’t vanish; they just aim upward.

In short, you’re still managing people and code. Only now the “people” are silicon, and the code writes itself.

Conclusion

The all-agent org chart is still a thought experiment, but the pieces already exist. OpenAI Codex or Claude Code merges code while you sleep, LLMs write design docs in seconds, DevOps bots patch infra before humans notice. Slide those pieces together and Monday’s stand-up feels a lot like the one we imagined.

The EM job isn’t going away; it’s changing shape. Less herding people, more steering systems. Less pep talk, more guardrails. The managers who thrive will be the ones who stay curious, learn to read model telemetry the way they once read team mood, and keep the human story front and center – even when the keyboards type themselves.

So ask yourself: if the tiles on tomorrow’s Zoom were all avatars, would you still lead with purpose? If the answer is “yes,” start small – pilot an agent pod, write a test playbook, translate metrics into a tale your execs can trust. The future is arriving line by line; the prompt is yours to write.

This post is a thought experiment, not a product roadmap. I’m not predicting or cheering for an engineering workforce with zero humans. Real teams need human judgment, empathy and creativity, and I don’t support replacing people with bots wholesale. The scenarios here are meant to spark discussion about where AI could take us and how we can prepare. All views are my own, not those of my employer or any company mentioned.