Opinion #1: I believe LeetCode-style interviews will stay because they self-select tolerance of corporate nonsense. The existence of data structures and algorithm (DSA) interviews is a bit of a head scratcher because these skills are rarely used at work. However, a candidate who’s willing to grind for weeks or months to prepare for an interview which bears little resemblance to the job, is likely to be someone who understands that sometimes it’s necessary to do pointless work.

This suggests they’ll probably have a much better time in Big Tech than someone who refuses to engage with meaningless tasks. It’s one reason I’ve observed for companies retaining LeetCode-style interviews. Of course, AI solves the puzzles with ease these days, and I expect larger companies to move back to in-person interviewing – all while keeping DSA interview questions.

Opinion #2: MCP became industry standard partly because Anthropic wasn’t a threat – but it couldn’t pull this off today. When MCP launched in November 2024, Anthropic wasn’t yet considered the leading AI lab. GPT-4o was seen as the top-performing multimodal model, followed by Claude 3.5 Sonnet and Gemini 1.5 Pro. At that point, Claude 3.5 Sonnet was seen as the best coding model, but it wasn’t understood how advantageous being good at coding would be for AI in general.

Therefore, OpenAI, Google, Microsoft, and other players could adopt MCP without fear of lock-in, as it came from a promising, but not a dominant lab. When Google launched its Agent2Agent protocol a few months later, no major lab adopted it due to concerns about Google’s dominant position. Today, Anthropic is the leading frontier lab and I reckon this would discourage adoption if MCP was launched in the present climate, for the same reason as nobody adopted the Agent2Agent protocol.

My answer to subscribers with questions about how to create standards is that I see them as emerging somewhat coincidentally, following a technically strong approach and with the right external conditions in place, which are impossible to entirely predict or control.