Reviewing AI-written code, PR volume and review fatigue.
Companies rushing to deploy AI coding agents are experiencing increased outages and bugs because pull requests are being merged rapidly without human review. Code review quality has drastically dropped, leading to codebases falling apart faster than before.
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Why Software Factories Fail
Individual developers universally report feeling more productive and note improvements in codebase quality when using AI tools. However, this individual boost does not automatically translate to organizational stability or better delivery outcomes.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
The company wanted developer workstations to closely match their production environments, which have always run on Linux. Additionally, having the entire technical team use the custom distribution helps gather more hands and contributions for the open-source project.
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DHH’s new way of writing code
Slack combines search and AI to handle massive enterprise data without hitting token limits. Search retrieves relevant raw messages while the LLM synthesizes them with citations, balancing recall and summarization.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
Letting models autonomously build complex multi-service architectures leads to unmaintainable codebases filled with brittle integrations. After running this experiment for a few months, their product became increasingly slow and regression-prone. Consequently, they had to completely rebuild the product from scratch with simpler, cleaner data flows.
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Dexter Horthy: Why We Stopped Trusting AI to Write the Plan
Brian used Claude Code to break up a scalability lock in the Helios kernel without closing the loop or letting it build autonomously. He spent two hours instead of four, benefiting from the model's ability to read block comments and understand a custom codebase.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Hard skills ensure developers can properly steer AI tools and handle complex logic when models fail. Relying entirely on LLMs limits a developer's skill ceiling to whatever the models can achieve.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
Coding agents fail to maintain codebase health over time because they lack the ability to preserve architectural maintainability without constant human steering. Without oversight, systems quickly suffer from code smells like shotgun surgery where changing one part breaks others.
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Why Software Factories Fail
AI acts primarily as an organizational amplifier, magnifying both the good practices and existing flaws across the SDLC. Without a strategic focus on underlying systems, localized productivity pockets are simply swallowed by downstream chaos.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
Apple initially refused to approve the app unless 37signals paid a thirty percent toll fee, which could have been a death sentence for the mobile-dependent service. After a tense two-week standoff coinciding with WWDC, Apple allowed the app through and gave it massive publicity, resulting in tens of thousands of signups.
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DHH’s new way of writing code
Slack enforces security by running real-time permission checks on search results before passing any data to the LLM. They avoid training models on internal data, relying instead on permissioned context windows.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
Planning serves as a way to reduce rework by aligning intent and narrowing the outcome space before execution begins. Spending time upfront on a plan or spec decreases the chance that the model produces unexpected or incorrect implementations. However, developers must balance plan detail with expected pain, as over-specifying minor details is inefficient.
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Dexter Horthy: Why We Stopped Trusting AI to Write the Plan
Rain used an LLM as a pattern amplification machine to replicate map APIs and thousands of lines of doc tests across four different Rust map types. This reduced what would have been weeks of tedious manual work into less than a day of effort.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Programming may continuously evolve toward higher-order or more structured pseudo-languages used for prompting. While this broadens the pool of people who can write software, it also exponentially increases the amount of code that needs to be maintained.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
Model labs like OpenAI and Anthropic own both the model weights and the harness, allowing them to train the model directly against its deployment environment via reinforcement learning. Independent harness builders who cannot adjust model weights are always at a structural disadvantage.
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Why Software Factories Fail
While individual effectiveness increases alongside AI adoption, software delivery instability rises as well. Delivery instability tracks metrics like the frequency of rollbacks and the volume of unplanned rework required after deployment.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
Designers at the company are not restricted to just creating visual specs; they also act as product managers by figuring out what to build. Furthermore, they are responsible for implementing their designs directly using CSS, HTML, and sometimes JavaScript or Ruby code.
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DHH’s new way of writing code
The team built internal Model Context Protocol integrations to let coding assistants safely query internal databases and analytics. This helps developers navigate historical context and generate correct SQL queries without hallucinating schemas.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
Training models to maintain codebases requires a fast oracle to instantly verify maintainability, but code rot only surfaces months later. Without a fast feedback loop, reinforcement learning struggles to improve long-term code quality.
▶ 29:03
Dexter Horthy: Why We Stopped Trusting AI to Write the Plan
Reviewing AI code requires transitioning from a passive reading mode into a heightened state of alert, similar to running a pre-flight checklist. Developers must carefully scrutinize the output, as engaging deeply helps catch subtle mistakes and preserve software rigor.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Human developers possess an intrinsic care for the craft and the taste required to know when something is truly right. AI agents simply complete tasks to a given prompt without striving for continuous self-improvement or aesthetic excellence.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
Reinforcement learning generates multiple solution traces for a programming problem and scores them based on binary correctness—whether existing tests pass and new tests succeed without breaking anything else. The model weights are then updated to make the successful behaviors more likely and failed ones less likely.
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Why Software Factories Fail
Engineering talent should pivot from churning out features to investing in quality internal platforms and guardrails. These platforms establish trust, reduce operational friction, and safely enable non-engineers to solve problems.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
He argues that when something possesses the correct aesthetic quality, it reflects an underlying truth and correctness, much like in mathematics or physics. Surrounding people with beautiful, well-functioning objects and interior code quality directly increases human happiness and reduces everyday frustration.
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DHH’s new way of writing code
Teams can use scripts powered by models like Claude Code to incrementally migrate old code bases into modern languages like TypeScript. For instance, Slack converted over 50% of its legacy JavaScript to TypeScript in just six months. The trade-off is ensuring developers share and tune prompts so the agent matches internal coding standards.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
You can deploy deterministic linter rules and automated cron jobs running agents to clean up anti-patterns daily. Stopping bad code from entering the repository is vital because agents will mimic existing patterns.
▶ 35:10
Dexter Horthy: Why We Stopped Trusting AI to Write the Plan
Earlier models acted like lossy search engines requiring manual file selection, while newer autonomous tools dynamically pull in repository context. By living directly in the codebase, these agents gather sufficient texture to handle complex multi-file tasks without manual curation. This capability shifts workflows from rigid prompt-response queries to autonomous context-aware pairing.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Coding agents struggle with the balance of prompt specificity, often requiring too much back-and-forth rewriting. Developers frequently find it faster to learn tools like Tailwind and write the code themselves rather than constantly correcting agent outputs.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
Because reinforcement learning evaluates models purely on whether tests pass, there is no mechanism to penalize poor program design or architectural erosion. As a result, agents resort to hacks like unnecessary try-catch blocks or type casting just to satisfy the test runner.
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Why Software Factories Fail
DORA's research outlines seven capabilities that, when paired with AI adoption, produce the best outcomes. Key factors include establishing a clear stance, maintaining healthy data ecosystems, using strong version control, and working in small batches.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
The shift happened when terminal agent harnesses gave AI tools like bash and internet access, combined with Opus's surprisingly high-quality output. This transition moved AI from a conversational novelty to a reliable partner that produced mergeable code, leading to an agent-first workflow.
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DHH’s new way of writing code
Large AI-generated pull requests, such as a 5,000-line translation, place an immense burden on human reviewers and often lead to superficial reviews. Reviewers tend to run out of cognitive stamina and skip commenting on massive changes. The recommendation is to pace reviews and be mindful of willpower limits, since slow is smooth and smooth is fast.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
While code remains the source of truth for now, the future may shift toward managing high-level preferences and specs. However, human review will always be necessary to rise above undifferentiated median AI output.
▶ 43:53
Dexter Horthy: Why We Stopped Trusting AI to Write the Plan
Autonomous tools can successfully analyze stack traces and statically examine code to identify real bugs in unfamiliar languages. In one example, an engineer with no prior experience in Zig used an AI model to discover and report three valid bugs in a terminal emulator. This capability allows developers to investigate issues far outside their usual domain expertise.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Debugging requires deep context, intuition, and experience operating under uncertainty rather than basic text prediction. Because models rely on surface-level pattern matching, they often flag impossible security flaws or miss complex edge cases.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
Verifying code quality and maintainability is orders of magnitude harder than verifying tests because the true cost of bad architecture is measured in months and years. This massive time gap makes it extremely difficult to propagate a reward signal back to the training phase.
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Why Software Factories Fail
AI makes it easy to generate massive applications or huge pull requests with a single prompt, leading reviewers to rubber-stamp code without reading it. Breaking tasks into small batches ensures proper human feedback and maintains high code standards.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
Daily work now starts agent-first on almost everything, using terminal layouts running models like Kimi and Opus in parallel panes. Instead of writing code from scratch in the editor, agents generate drafts that get reviewed and selectively altered via version control diffs.
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DHH’s new way of writing code
Slack functions as a work operating system comprising interacting primitives like huddles, lists, canvases, and channels with deep native AI integrations. Using platforms like Agent Force, developers can build agents that dynamically generate canvases, create lists, or fetch enterprise search context. This positions Slack not just as an IDE, but as the core environment for running enterprise workflows.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
Developers can bypass expensive commercial code review tools by writing custom scripts that feed pull request diffs and descriptions into language models. While these reviews miss broader context like imports or passing CI tests, they excel at spotting inconsistencies, SQL migration mismatches, and text discrepancies. This approach offers a low-cost, automatable way to catch errors before human review.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Attackers can use LLMs to generate fake bug reports and flood open-source maintainers with noise, creating a denial of attention. This aggressive format introduces novel security challenges and malicious supply chain risks.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
New evaluation datasets are trying to address complex maintainability by testing extremely long tasks or un-trained open source repos. For example, Cognition's Frontier Code evaluates multi-PR tasks by penalizing models if they write tests that do not fail on pre-patch code.
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Why Software Factories Fail
Adopting any new technology introduces a productivity J-curve where output dips due to learning curves and verification taxes before recovering. Leadership must anticipate this dip rather than expecting immediate ten-fold gains from handing out AI licenses.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
Senior developers possess the long architectural insight and historical overview needed to critically examine agent output and ensure quality before deployment. Because they can effectively validate code and direct multiple agents, their individual productivity is amplified by five to ten times.
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DHH’s new way of writing code
When building features for agents, developers must expose tools as actions and design search APIs that deliver rich context rather than just single messages. For example, Slack's real-time search API provides full threads, relationships, and user roles so agents can reason effectively. This requires building dedicated endpoints alongside human-facing interfaces.
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How Slack AI Agents Accelerate Dev Productivity | Samuel Messing
LLMs make it tolerable and even advantageous to have slightly repeated code instead of over-indexing on complex macros. This allows developers to keep code readable and keep source links intact for code navigation.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
The vast majority of survey respondents report favorable sentiment and agree that increased productivity is the biggest benefit of AI tools. However, very few developers highly trust AI outputs, reflecting a healthy dose of skepticism.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
Teams should reintroduce structured upfront planning including product reviews, system architecture definitions, and detailed program design covering method signatures and call stacks. Spending a short amount of time on pre-planning saves hours of frustration during code review.
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Why Software Factories Fail
The industry approached AI backward by focusing on feature generation before code review. Using AI to review code changes first is a safer starting point that helps teams understand model capabilities and build trust before tackling riskier generative tasks.
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AI is making software delivery less stable. DORA's Nathen Harvey on the fix | EVOLVE 2026
Agents drastically lower the cost of exploring speculative ideas, allowing developers to process hundreds of pull requests or test edge-case hunches in minutes. This explodes the scope of potential improvements, enabling teams to tackle performance enhancements or features they would have otherwise skipped entirely.
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DHH’s new way of writing code
You can write detailed instructions and design docs—like an RFD—and feed them iteratively into an LLM to automate massive multi-service migrations. Rain describes iterating on a migration guide a couple dozen times, then using it to have the LLM successfully generate thousands of lines of code across multiple APIs in an hour.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Many models are trained on codebases that include GPL-licensed software without explicit rights. If regulations forced companies to open-source models that produced potential GPL code, it could trigger massive industry-wide panic.
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Will AI replace programmers? | ThePrimeagen and Lex Fridman
When teams use model-assisted planning and alignment, pull requests become a joy to review because the changes directly match what was agreed upon beforehand. This process eliminates AI-generated slop, makes code reviews faster, and ensures engineers retain ownership of the codebase.
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Why Software Factories Fail
Developers can have different AI models pingpong back and forth to formulate and critique a safe execution plan before touching any critical partitions. In this workflow, one model generates the initial plan while another critiques it until a solid blueprint is reached. This removes personal friction and operational risk from complex multi-step system tasks.
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DHH’s new way of writing code
Hard verification signals include compilation, passing test suites, and deterministic validation tools like OpenAPI document comparisons. For fuzzier requirements, developers can start a fresh context window, feed the guide back in, and ask the LLM to critically review the current implementation against the specs.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
The traditional high salaries and leverage of programmers stemmed from them being the primary implementation bottleneck in building software. As coding agents make implementation faster and more accessible, that scarcity-driven constraint is loosening. Consequently, the industry is shifting toward product management skills, communication, and overall system oversight.
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DHH’s new way of writing code
Writing clear design documents and technical guides gives LLMs precise instructions to follow, turning English into a programming language for complex tasks. Cultivating strong writing skills allows developers to multiply their rigor and automate tedious architectural migrations across foundational infrastructure.
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Oxide and Friends 1/12/2026 -- Engineering Rigor in the LLM Age
Employees should consistently show up and do their absolute best work even if they consider their current workplace suboptimal. Trying to coast or slack off hurts your own skill development and ruins future warm referral opportunities from colleagues who noticed your work ethic. True professional longevity requires practicing your craft relentlessly regardless of the environment.
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DHH’s new way of writing code
Working with responsive coding agents can trigger an intoxicating dopamine loop that tempts developers to overwork themselves. To combat AI-induced burnout, developers must protect their fundamental health habits, particularly by prioritizing a full eight hours of sleep. Treating AI productivity as a permanent capability rather than a limited-time sale prevents developers from running ragged.
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DHH’s new way of writing code
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