Reading The Great Engineering Leader Career Break Through Metabolism and a New CTO Role — A Response to Six of Pragmatic Engineer's Reasons

I read Gergely Orosz’s Headed for the Exit: the Great Engineering Leader Career Break on The Pragmatic Engineer. The piece starts from the observation that a rising number of CTOs, VPEs, and Heads of Engineering are stepping out of high-status positions without lining up their next role, and breaks the drivers down into ten reasons. I’m currently working as a hands-on CTO in a fully remote Japanese organization, and I want to react to six of those reasons from that seat.

Up front, my overall take: what the piece describes is largely accurate. What each individual does about it is a life choice. Facing the same landscape, some people will take a career break and others will stay and re-negotiate. My response is written from the “stay and re-negotiate” side, along two axes. One is organizational metabolism. The other is a new CTO role that binds AI agents and humans together.

The Six Reasons This Post Addresses

Quoting the six reasons from the article’s table of contents.

The job got (much) worse
The startup is “losing” and becoming worthless
Not being AI-native enough for other skills to be relevant
Their predecessor saw the “writing on the wall”
Long hours – rarely decisive
Smaller teams mean less need for leaders

Below, one section per reason.

1. The Job Got (Much) Worse — Leaving and Staying Are Both Metabolism

The article lists a set of unrealistic expectations from CEOs and founders: a “magical” transformation to AI-native, 20–50% engineering cost cuts, “do more with less,” business pressure as AI coding bills pile up, and founder slop where wonky AI prototypes get demanded as full-blown products in weeks. It also captures the pattern of hands-on founders with “AI psychosis” merging 60,000-line PRs while the CTO is left cleaning up accountability, quality, and tech debt.

I follow the description. The combination is genuinely rough. That said, I think both “so I’m leaving” and “so I’m staying” are legitimate answers.

Leaving is rational. Once a CTO realizes their role design and the company’s expectations no longer overlap, the only options are to re-negotiate the overlap or to walk. What happens to the business after the CTO walks is up to that business. If the company fails because the CTO left, that is one more turn of metabolism. The next CTO, or the founder themselves relearning the craft, takes the next step; or the company ends there. Either way, the market and the leadership team make that call, not the CTO who left.

Staying is also rational. Rebuilding a workable operating model inside a set of unrealistic expectations is exactly where a CTO earns their keep. Turning founder-shipped 60,000-line PRs from “already merged” into “reviewable chunks,” designing budget guardrails for AI coding bills, redesigning CI/CD so founder pushes to production have to pass real gates: the list is long. CTOs who can redesign this layer are scarce.

What matters, whether the choice is to leave or to stay, is understanding that the choice is one piece of the company’s overall metabolism. My stance is: don’t load the outcome of the whole business onto a single CTO’s decision to leave or stay.

2. The Startup Is “Losing” and Becoming Worthless — Also Metabolism

The article classifies startups into three buckets (building and selling AI-native products, threatened by AI-native competitors, and largely unaffected by AI) and points out that most VC-funded startups sit in the first two. A CTO’s equity, mostly common shares, easily gets zeroed by the preference stack. The worked example: even a very generous 2% grant means basically nothing when a $10M Seed at $50M valuation and a $100M Series A at $500M valuation with 2x preference sit above you, so common shareholders see $1 only past a $210M exit.

The description is fair. Sticking around at a losing startup has a low expected payoff. Still, this too is just metabolism at work.

VC-backed startups get re-priced constantly against the gap between capital-market expectations and actual revenue and growth. Getting eaten by an AI-native competitor, failing to convert to AI-native and fading, or opting to sell rather than pivot the whole business (the way Bending Spoons bought Airtable) are all shapes that metabolism can take from the market’s angle.

Assuming the company’s survival hinges on whether the CTO leaves is a bit self-important. The organizational damage from a departing CTO is real, of course. But inside the metabolic flow, the next CTO or the founder-turned-technical-lead picks up the situation. The departed CTO takes their next risk at the next stage; the surviving company plays its next move. Each choice keeps metabolism moving overall.

3. Not Being AI-Native Enough for Other Skills to Be Relevant — Non-AI-Native Environments Are the Scarce Asset

The article notes that top-paying engineering leadership postings increasingly assume experience leading AI-native organizations, and staying at a role where AI workflows never take hold makes your market value shrink fast. An ex-engineering director is quoted saying they had to “pull out into the fast lane” to stay relevant, mixing academia and AI consulting. Charity Majors’s line, “You’ve got to get AI on your resume,” is also cited.

From my own experience, starting from AI-native is actually not that hard. Firing up Claude Code, Codex CLI, or Cursor in a personal environment or a greenfield project takes a few hours of following the docs to be productive. Wire in MCP servers, start filling out AGENTS.md and CLAUDE.md, and by next week you have the skeleton of a workflow.

The scarce skill is the ability to turn a non-AI-native environment into an AI-native one.

Embedding agents into an existing CI/CD pipeline and designing the guardrails
Redesigning review flow, release flow, and on-call rotations with AI agents in the loop
Redirecting the energy of an executive team caught in “AI psychosis” toward use cases with real business impact
Shaping company-wide docs, knowledge, and decision logs into a form agents can read and write cleanly
Building the mechanism for making cost-vs-quality trade-off decisions

None of these come up in an organization born AI-native. Conversely, an organization with an existing business trying to steer AI-native is a pile of exactly this kind of conversion work. If you are currently a CTO or VPE in a non-AI-native environment, moving to an AI-native environment is one path, but the more differentiating experience is having actually converted a non-AI-native environment to AI-native.

That is why I think a non-AI-native environment where you can practice AI-native conversion is a valuable seat. Quitting to enter the “fast lane” is one choice; laying the fast lane down under your own feet is another. The people who can pick the second option are few, and CTOs and VPEs are precisely those people.

4. Their Predecessor Saw the “Writing on the Wall” — This Is Where You Show Your Chops

The article notes that after a predecessor CTO leaves, three questions usually determine whether the successor also leaves: has AI made the role worse, is equity on course to be worthless, and is AI-native transformation actually possible in this org? A specific case is described where the successor also resigned after a six-month tenure. The predecessor’s read of the writing on the wall gets confirmed by the successor.

My response here is simple: that is where you show your chops.

Taking a role after a predecessor CTO left means the initial conditions include a bundle of organizational issues, tech debt, and expectation gaps with leadership. There is almost no honeymoon; the job is to work through the bundle. Whether you leave in six months or turn the situation around in six months is largely a question of a leader’s move count.

At the same time, leaving is again part of metabolism. When a company sees successive short tenures at the CTO seat, that company is forced to re-examine itself. Can the CTO position hold as designed, does the role itself need to be redefined, should the founder reclaim technical leadership? Kicking the organization into that re-examination is, sometimes, the value the departed CTO leaves behind.

Whether you stay or leave, both return something to the organization. As long as you don’t stop at “my predecessor saw the same thing I saw” and instead convert that observation into decision inputs for what comes next, neither choice is wasted.

5. Long Hours – Rarely Decisive — Mix Hard and Familiar Work, Run a Marathon Alongside AI Agents

The article positions long hours as never decisive on their own. Business struggling, equity vanishing, and a CEO or founder ignoring the leader’s input have to combine before long hours push someone over the edge. In a thriving business, delegation and rest remain possible; in a struggling one, every waking hour feels like it needs to go into turning things around. That contrast is well captured.

From my experience, right now a mix of hard work and familiar work is the actual requirement. Fill a day only with hard decisions and fatigue compounds fast. Fill it only with familiar work and you fall behind on how AI agents are evolving. Designing the mix at the day, week, and month levels is becoming the precondition for continuous operation.

The other observation I want to plug in is that AI agents can, functionally, work forever. Humans cannot outrun agent uptime by sprinting harder. So I frame the day not as a sprint but as a marathon run alongside AI agents. I wrote about this framing in more depth in my earlier post Why I’m Skeptical of the AI Vampire 3-to-4-Hour Workday.

Group hard-decision sessions into the shorter windows where I can focus
Place familiar work and agent orchestration around those windows
Check in on async agent tasks during breaks and transitions
Budget fatigue over weeks and months, not just single days

Long hours are not the problem in themselves; the design of the mix is what breaks first.

6. Smaller Teams Mean Less Need for Leaders — The New CTO Runs HR for AI Agents and Humans

The article walks through the flattening of team size: the mainstreaming of fullstack engineers, the pattern at Anthropic where a single project usually has one or two engineers running many agents each, Bluesky launching web, iOS, and Android with a single engineer, Paul Frazee, using React Native and Expo, and SignalFire data showing frontend and iOS/Android engineer demand trending down while AI/ML engineer demand trends up. Smaller teams don’t need a VPE, so director-and-above positions shrink.

I think this is where the CTO role itself is being redefined. The shift is from “the technology owner” to “the leader who runs AI agents and humans together.”

Decide which work goes to humans and which goes to AI agents
Design the handoff protocols, responsibility split, and audit logs between humans and AI agents
Run the “hiring” of AI agents (model selection, agent selection, tool selection) and their onboarding (AGENTS.md / CLAUDE.md / MCP server setup)
Continuously decide on the “reassignment” and “termination” of AI agents (swap, retire)
Redesign human skill development on the assumption that humans always work with AI agents

This is not just the T of Technology; it looks a lot like company-wide HR for AI agents and humans. What used to be split between the CHRO and the CTO is starting to overlap because AI agents have entered the picture.

The article’s observation that smaller teams don’t need VPEs is accurate as a data point. But the CTO role that designs the org with AI agents inside it is, if anything, an area where demand is growing. Headcount may drop; the skill set expected of the seat gets wider. As long as you can step into the redefinition from “the T lead” into “the lead who also does HR for AI agents and humans,” the CTO seat stays around in a different shape.

Summary

Pragmatic Engineer’s Headed for the Exit accurately captures the structural pressure pushing CTOs and VPEs toward career breaks
On top of that, whether to leave or to stay is an individual life choice, and either choice functions as part of the company’s overall metabolism
My response to the six reasons is:
1. Even when the job gets worse, both leaving and staying are shapes of metabolism
2. A losing startup is metabolism playing out at the market level
3. Starting AI-native is easy; converting a non-AI-native environment to AI-native is the scarce, differentiating experience
4. Whether the successor can re-grip what the predecessor saw is where a leader shows their chops
5. Design a mix of hard and familiar work, and run the day as a marathon alongside AI agents rather than a sprint
6. Redefine the CTO seat as a role that runs HR for both AI agents and humans, and it stays around in a new shape

That’s all from reading The Great Engineering Leader Career Break through the lenses of organizational metabolism and a new CTO role, from the Gemba.

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