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Doby Baxter argues that the technology sector may not recover from the next downturn the way it recovered from earlier ones, and the reason is not AI capability alone. His case is a chain: a set of organizational and technical dependencies that held in past cycles, each of which could weaken once AI-led substitution reaches entry-level work and undocumented system knowledge. The argument is a conditional opinion about mechanisms, not a measured forecast, and it is worth reading link by link.
What the old recovery loop assumed
Baxter’s essay, published on DEV Community on September 29, 2026, starts from a simple observation: past technology downturns were followed by recovery because several things were true at the same time. He groups them into what he calls the old recovery loop. It rests on five assumptions:
- Roles cut because demand fell would return when demand came back.
- The knowledge needed to run systems survived among people who could be rehired or who could teach others.
- Junior training kept restarting, so each new cohort learned on real work.
- Excessive cuts produced visible failures, which prompted correction.
- System change moved at a speed people could still follow and explain.
None of these is a law of nature. Each is a condition that happened to hold, and the essay’s concern is that they may hold less reliably when they are tested together. The seven links below describe how that could happen.
The seven links in the chain
Baxter presents the links in sequence, and each one depends on the conditions before it. He argues that the chain can fail at several points at once, which is why he describes the risk as systemic rather than as a single bad decision.
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1. Substitution replaces the demand explanation
In past downturns, a cut was usually read as a response to temporary low demand. Baxter argues that when a role is described as permanently automated, it may not return when demand improves. He is describing how employers may frame these decisions, not reporting a measured labor-market trend, and the essay does not quantify how often this framing occurs.
2. Automation reaches the junior tasks
The tasks Baxter names are boilerplate, small fixes, test writing, documentation updates, ticket triage, and first-draft configuration. These look like low-value work from the outside. His point is that they also form an apprenticeship layer: a junior engineer who writes a test or triages a ticket learns how a system behaves, which fields matter, and how failures show up. If those tasks move to agents, the apprenticeship can disappear even if the output still ships.
3. Undocumented system knowledge leaves with people
The essay points to context that rarely lives in code: why a setting is unusual, what happened during the last major incident, and which constraints apply to a specific customer. When the people who carry that context leave, the knowledge goes with them. Baxter’s argument is that tools cannot recover what was never written down, so a team that relies on an agent to make changes may be making them without the reasons behind the old ones.
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4. Change volume rises while understanding shrinks
Automated agents can produce code, configuration, and fixes faster than people can review them. Baxter argues that at the same time, fewer people may hold a working model of the system. The essay does not quantify this gap. The concern is about the ratio: more change per person who understands the consequences.
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5. Plausible errors pass shape checks
Baxter separates two kinds of wrong output. Malformed output, such as a missing field or a wrong type, can be rejected by a schema or type check. A well-formed but contextually wrong answer passes those checks. In a multi-step workflow, a later step can accept the earlier mistake as fact, so the error gains credibility as it moves through the pipeline. The essay does not measure how common such errors are; it treats them as a known category of risk.
6. Quiet drift replaces visible outage
The failure mode Baxter describes is not usually a crash. It is slightly wrong records, customers served the wrong answer, or reports that drift away from reality without triggering an alert. Because nothing breaks loudly, the signal that would normally reach budget decision-makers is weaker. The argument is that organizations may not feel the problem until it has been running for some time.
7. Correction arrives late and costs more
The final link is that drift is often caught later, by an audit, a regulator, or a customer. By then, the people who understood the system may be harder to replace, and the junior engineers who would have built comparable understanding have not had time to do so. Baxter does not supply cost data for this step. He presents it as the point where the earlier links become expensive to reverse.
How the old and current paths compare
The essay compares the two recovery models along five axes. The table below uses Baxter’s own characterizations of the historical side, which he describes as largely held or as held after a dip. These are his judgments about past cycles, not independently measured historical figures.
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| Axis | Historical recovery (author’s characterization) | Current path the essay says is threatened |
|---|---|---|
| Workforce reductions | Framed as temporary demand cuts; roles expected to return | Framed as permanent substitution; roles may not return |
| Knowledge | Held in people who could be rehired or could teach | Held in people who may leave, with little written context |
| Junior training | Largely held; training restarted with each cohort | Threatened, because the tasks that trained juniors are automated |
| Failures | Visible, which prompted correction; described as held after a dip | Quiet, with drift that does not trigger an outage or alert |
| Pace of system change | Moved at a speed people could understand | Faster than understanding, as agents produce change at volume |
What would prove the author wrong
Baxter names specific conditions that would weaken his argument. These are separate from his recommendations below. He says the case would be weaker if:
- Organizations document implicit knowledge, so that intent behind unusual settings and incident history survives staff turnover.
- Companies deliberately keep hiring junior engineers and give them meaningful work, not only supervised clean-up.
- Failures in AI-assisted systems are made visible by design, so that drift produces signals before an audit does.
The essay therefore presents a set of dependencies that can be broken, not an inevitable outcome.
Where engineers can repair the chain
The article’s recommendations target the links rather than the headline trend. Each one is a practical response to a link described above.
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Baxter recommends that juniors review agent-proposed changes, with senior engineers reviewing that review. The purpose is to keep the apprenticeship that routine tasks used to provide. A junior who reads a proposed change and asks why it was made learns more than one who only accepts it.
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Write down intent
The proposed tools are decision records, runbooks, and comments that explain why something is the way it is, not only what it does. The test for a useful comment is whether a new engineer could tell, without asking anyone, why a setting exists and what would break if it changed.
Validate meaning, not only shape
Deterministic validation should surround probabilistic components. Schema and type checks catch malformed output, so they remain necessary. The essay adds that teams should also check whether an answer is correct in its context, because a well-formed wrong answer will pass a shape check. Preflight checks before a change is applied are one way to catch this earlier.
Gate consequential actions behind human approval
Actions with large consequences, such as changes to production infrastructure or to financial records, should require a human to approve them. The point is not to slow every change but to place a person at the points where an error would be expensive to reverse.
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Baxter asks teams to make failures visible through clear error messages, logged reasons for decisions, and alerts. If drift is designed to be visible, it becomes a signal that reaches the people who fund and staff the system, rather than something discovered later by an auditor.
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How much weight the argument can bear
The essay contains no named statistics or quantitative studies. It does not measure the scale of substitution, the number of junior roles lost, how often plausible errors occur, or what delayed correction costs. Its claims about labor markets and operations are the author’s reasoning about mechanisms, and they should be read with that attribution in place.
Baxter is candid about this. In his own words: “This is my opinion, and I would rather name where it could be wrong than pretend it is certain.” That qualification is the most useful guide to how far the argument should be taken. It is a structured case for what to check, not a verdict on what will happen.
Further reading
For adjacent reading on how technology teams measure and improve software delivery, Accelerate: The Science of Lean Software and DevOps by Nicole Forsgren, Jez Humble, and Gene Kim covers measuring delivery performance and the capabilities teams can invest in. It does not study AI workforce substitution, so it supports the engineering practices in this article rather than the essay’s central claims.
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