Cursor vs JetBrains

SpaceX's AI business beats expectations with $4.4B profit

SpaceX reported Q3 2026 revenue of $14.6 billion, with net income of $4.4 billion, driven by its AI-focused 'Super Intelligence' segment. This segment, which includes the Grok bot, the acquired Cursor coding tool, and neocloud services (1.7 GW compute), contributed $7.5 billion in revenue and $431 million in profit, excluding $1.2 billion in one-time stock-based compensation. AI now accounts for 52% of company income. The company continues heavy investment in data centers (Colossus II) and Starship, with capex of $19.6 billion in Q3 and plans for $35-40 billion in Q4. Starlink subscribers grew to 13.6 million. CEO Elon Musk projects annual recurring revenue of $500 billion to $1 trillion by end of 2027.

• SpaceX reported Q3 2026 revenue of $14.6 billion and net income of $4.4 billion, a turnaround from a $1 billion loss a year earlier.
• The 'Super Intelligence' business line generated $7.5 billion in Q3 revenue, including $431 million in profit (excluding $1.2 billion in one-time stock-based compensation from the Cursor acquisition).
• AI now accounts for 52% of SpaceX's income.

Clay's Kareem Amin Discusses Rise of GTM Engineer

Clay co-founder and CEO Kareem Amin will speak at TechCrunch Disrupt 2026 about the emergence of the 'GTM engineer' role. This new discipline uses AI, data, and automation to build repeatable revenue systems, replacing manual go-to-market processes. Clay coined the role in 2023 and reports about 100 GTM engineering job listings each month. The company has seen rapid growth, with ARR tripling to $100 million in a year, a $115 million Series D round at a $7.1 billion valuation, and a $1 million scholarship fund to train GTM engineers. Amin's session will explore how AI is transforming go-to-market strategies and what this means for company growth.

• Clay coined the GTM engineer role in 2023.
• Approximately 100 GTM engineering job listings appear each month.
• Clay tripled annual recurring revenue to $100 million in a year.

Advanced Evals Guide: Finding Hidden AI Failures

This article is a comprehensive guide on advanced evaluation (evals) practices for AI products, authored by Hamel Husain and Shreya Shankar. It emphasizes the importance of error discovery before writing metrics, contrasting it with product discovery. The authors outline a three-step process for effective error discovery using coding agents like Codex or Claude, highlighting the pitfalls of automation bias and criteria drift. They introduce an open-source 'evals skills' plugin that assists in trace review and clustering. The article cites real-world examples from companies like Shopify, Cursor, Ramp, and Harvey, demonstrating how evals have led to significant product improvements. The piece also discusses synthetic data generation and the importance of human-in-the-loop annotation, recommending a target of 100 traces for meaningful analysis.

• Shopify used evals to build an AI workflow builder that was 2.2x faster and 68% cheaper than the frontier-model system it replaced.
• Cursor developed its Auto Balance routing performance with evals, reducing costs by 41%.
• Ramp increased its automatic receipt collection product's accuracy from 35% to 83% after investing in evals.

A New Agentic Experience: JetBrains Air in IDEs – EAP Now Open

Last week, we shared our point of view on where we think agentic development is headed and introduced JetBrains' answer to it: JetBrains Air, an open system of products for agentic development. To…

How AGI Became a Marketing Department Case

The article discusses how AI, particularly coding agents, is transforming the role of software developers. It highlights that 90% of professional developers use such tools weekly, with 68% daily, and about a third are 'agentic coders' who generate 84% of their code via AI. This shift is changing the developer's role from generalist to specialist, and now to a 'Forward Deployed Engineer' who works closely with clients. The article emphasizes that the real value of developers is shifting from pure coding capacity to understanding business context, identifying problems, and making sound technical decisions. It draws on examples from Google Research and the hiring practices of OpenAI and Anthropic, and suggests that businesses should focus on teams that can quickly build domain context rather than just counting developers.

• 90% of professional developers use coding agents weekly, 68% daily (JetBrains survey).
• One third of developers are 'agentic coders' generating 84% of their code via AI.
• Google Research is investigating whether software agents understand standards and collaborate with developers.

AI Agents Redefine Software Developer Role Toward Context

This opinion piece examines how AI coding agents are reshaping software development, shifting developers' value from implementation to understanding business context and making architectural decisions. JetBrains' survey shows 90% of professional developers use such tools weekly (68% daily), with about a third now 'agentic coders' who let AI generate an average of 84% of their code. This transformation has popularized the 'Forward Deployed Engineer' role at OpenAI and Anthropic, which focuses on client collaboration. The author argues that while AI simplifies coding, experienced developers' true worth lies in contextual understanding, risk identification, and solution validation. The piece advises clients to prioritize contextual insight over mere technical capacity.

• 90% of professional developers use coding agents at least weekly, 68% daily (JetBrains Developer Ecosystem Survey).
• About one-third of developers are 'Agentic Coders', with AI generating 84% of their code on average.
• OpenAI and Anthropic are expanding 'Forward Deployed Engineer' roles, with Anthropic hiring in Munich.