Observed Signal · Nov 3, 2026 · Analysis · Source: The Pragmatic Engineer · Impact: 2/5 · Sentiment: Neutral
AI Native Startups Boost PR Output Tenfold
In a detailed essay, software engineer Neil Kakkar compares his productivity across three companies: Bloomberg, PostHog, and Tano. At Bloomberg and PostHog, he shipped 8-20 pull requests per month, while at Tano, an AI-native startup, he now ships over 200 PRs monthly, an 11x increase. This surge is attributed to AI coding agents like Claude Code, which handle end-to-end feature development, automated code reviews, and continuous integration. Key trade-offs include loss of human code reviews, increased context switching, and 'thrashing' from multitasking. Tano operates without human PR reviews, relying instead on an automated reviewer agent. The author notes that AI is a speed multiplier, but requires culture shifts and guardrails to prevent burnout. The essay highlights that AI-native startups can iterate much faster, but pre-AI companies can catch up by adopting similar practices.
The article provides insights into AI's impact on software engineering productivity, relevant for AdTech tech infrastructure, but it is not directly about ad technology.
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Key Takeaways & Evidence Grounding
- Neil Kakkar's PR output increased 11x at AI-native startup Tano compared to PostHog.
- Tano uses Claude Code agents to build features end-to-end and an automated code review agent.
- At Tano, PR merge time is less than one hour, and the revert rate is 6x lower than at PostHog.
- Tano has no human code reviews; automated agent reviews are standard.
- Neil Kakkar's monthly PR count at Tano is over 200, versus 8-20 at PostHog.
Connected Companies & Entities
4 Entities mapped“Pre-AI startup where Neil Kakkar previously worked, shipping 8-20 PRs monthly.”
“Large fintech company where Neil Kakkar started his career, with lower PR output.”
“Company behind Claude Code, mentioned in relation to automated code reviews and CI constraints.”
“Neil Kakkar interned at Google.”
Ontology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
AI Coding Spend Soars 16x; PR Velocity Doubles
A new analysis by The Pragmatic Engineer, based on data from 27 companies plus CodeRabbit and Gitar, reveals dramatic increases in AI spending for software engineering. CodeRabbit data shows weekly PRs per developer doubled (113% increase), while Gitar reports AI-native teams shipping 60+ PRs per developer weekly. Spending patterns vary widely: a quant trading firm's AI spend ballooned from $500K to $8M monthly (16x), averaging $1,600 per developer monthly, while a two-person .NET shop spends $3,000 per developer monthly. High spenders like Meta and NVIDIA encourage heavy AI use, while Apple engineers remain cautious. Median spend is around $300-500 per developer monthly, with common spending caps. Notably, despite increased PR counts, several companies report flat or modest velocity gains, with one engineer noting that quality-focused teams see little speed increase. The article highlights challenges in measuring ROI and the shift to models like Claude Opus 5.5.
Coding Agents Boost Code Production but Not Project Delivery
A guest article by Markus Kirchmaier of LEAN-CODERS argues that while AI coding tools significantly increase code production, they do not proportionally accelerate software project completion. Citing a 2026 Management Science study showing a 26% increase in developer tasks with AI support, and an NBER study with over 500,000 GitHub developers showing up to 240% more coding activity with autonomous coding agents, only about 30% of that translates to actual releases. The article highlights that generated code still requires human review, testing, and maintenance, and that organizational factors like requirements gathering, approvals, and dependencies are not accelerated by AI. The author calls for measuring actual release speed rather than developer hours saved, citing a quote from Christoph Ott, founder of LEAN-CODERS, questioning if developers were ever the bottleneck.
AI Projects Close PRs, Deploy Agent 'Software Factories'
AI-native open source projects are increasingly shutting down external pull requests and using agent-based 'software factories' to manage contributions. Vercel deployed such a system for its AI SDK project, which now authors 25–35% of merged PRs and closes 70–80% of issues. The Astro web framework adopted agent-driven triage and regained control of its backlog. Flue and tldraw now automatically close external PRs, converting them into issues or discussions, partly to prevent 'drive-by AI slop PRs.' Maintainers say they trust internally optimized agents more than community-generated code, though they acknowledge risks for community onboarding. Mitchell Hashimoto predicts large open source projects will eventually close contributions completely, while projects still invite reporting, discussion, and perspective from outside contributors.
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