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The standing brief

Topics in the media

Five arguments the field is actually having as agents take over code production, the figures people are citing in them, and where those figures came from. Updated as the discourse moves.

ED

Review and trust

Engineering leadership

Has review become the constraint, and does adding reviewers help?

The loudest argument in the field. Generation got faster, review did not, and the queue moved. What is new in 2026 is that the effect is now measured rather than described, and the measurements are worse than the anecdotes were.

5.3x longer pickup time on agentic pull requests than unassisted ones LinearB 2026 benchmarks, via CIO ↗
+441.5% median review duration LinearB 2026 benchmarks, via CIO ↗
+31.3% pull requests merged with zero review LinearB 2026 benchmarks, via Codacy ↗
40% to 29% developer trust in AI-generated output, year on year Stack Overflow Developer Survey 2025 ↗
Where we stand

Trust fell because people looked closely; unreviewed merges rose because looking closely does not scale. Both are rational and they point in opposite directions. Adding reviewers treats a structural property as a staffing gap: review needs context you cannot hire quickly, parallelises badly, and degrades silently rather than loudly.

Our writing on this

PD

Specs and context

Product & acceptance criteria

Is spec-driven development a real shift, or waterfall with better tooling?

The premise that code is the primary asset is being openly questioned, with the specification proposed as the new centre. Alongside it, "context engineering" has become a named discipline: structured context files, external knowledge, tool-constrained access.

up to 50% error reduction from human-refined specs, in controlled studies arXiv 2602.00180, spec-driven development ↗
context blindness the named failure: output internally coherent but incompatible with the repository, referencing APIs that do not exist or violating local conventions arXiv 2604.05278, Spec Kit Agents ↗
orchestration explicit repository-grounded orchestration improves reliability, but does not eliminate context blindness arXiv 2606.27045 ↗
Where we stand

The waterfall comparison is lazy. Waterfall failed because the specification was written once, by people distant from the work, and could not respond to what building taught them. A criterion attached to a ticket, revised in refinement and checked on every change, is the opposite of that. The real question is not whether to specify but who does it and whether a machine can settle it.

FD

Debt and craft

Platform economics & tooling

Is AI-generated technical debt a distinct category, or the old problem faster?

A remediation market is being predicted before most organisations have measured the problem. The interesting disagreement is whether generated code is intrinsically worse, or whether the issue is that nobody read it.

~41% of production code generated by AI agents, by early 2026 Industry reporting, 2026 ↗
a new market Gartner expects specialised tools and consulting to audit and refactor AI-generated technical debt Gartner, via industry reporting ↗
61bn workdays estimated global technical debt CAST report ↗
Where we stand

Generated code is not intrinsically unmaintainable. Unread code is. The distinct category is comprehension debt: a system being modified faster than anyone reads it, where the cost surfaces during an incident rather than during maintenance. A global workday figure is unactionable; your own unread-merge rate is not.

Our writing on this

DD

Careers and teams

Delivery & program intelligence

What happens to the junior pipeline, and what should teams hire for now?

The apprenticeship path ran through exactly the work that automated first. The discourse swings between "software engineering is dead" and "nothing has changed", and the useful reporting sits between them.

-40% entry-level developer postings Industry reporting, 2026 ↗
counter-trend some large enterprises increased junior hiring while startups cut it Industry reporting, 2026 ↗
raised bar entry-level hires expected to produce what was previously senior-level output Industry reporting, 2026 ↗
Where we stand

A junior who accepts output they do not understand creates debt at machine speed, which is a training problem rather than a hiring mistake. The pipeline broke because the work juniors learned on automated, and no interview technique fixes an absent apprenticeship. Someone has to rebuild it deliberately.

Our writing on this

GD

Regulation and proof

Governance, security & compliance

What does the AI Act actually require of a team shipping AI-written code?

Enforcement began on 2 August 2026. Most engineering teams noticed nothing, because the obligations land on classification, logging and traceability rather than on anything that changes a developer workflow on the day.

2 Aug 2026 EU AI Act enforcement begins, with transparency obligations and active enforcement powers Help Net Security ↗
Article 12 automatic logging of events relevant to risk and traceability; logs must be tamper-evident EU AI Act, via Teleport ↗
6 / 24 months minimum log retention, the longer figure for biometric and law-enforcement uses EU AI Act, via Teleport ↗
the gap native provenance attribution exists for generated images, with no documented equivalent for generated code Augment Code, EU AI Act guidance ↗
Where we stand

The provenance gap is the most consequential item on this page. There is an established way to attribute a generated image and no equivalent for a generated function. "Tamper-evident" is also a stronger word than most teams register: a log written by the same pipeline that performed the action is the component attesting to its own behaviour, which is the arrangement controls exist to avoid.