"Last week, OpenAI and Anthropic dropped their respective coding models-GPT-5.3-Codex and Claude Opus 4.6-both of which represented significant leaps in AI coding capabilities. GPT-5.3-Codex showed markedly higher performance on coding benchmarks than earlier models, while Opus 4.6 introduced a feature that lets users deploy autonomous AI agent teams that can tackle different aspects of complex projects simultaneously. Both models can write, test, and debug code with minimal human intervention-even iterating on their own work and refining features before presenting results to developers."
"Shumer said that "something clicked" following the model releases and described AI models now handling the entire development cycle autonomously-writing tens of thousands of lines of code, opening applications, testing features, and iterating until satisfied, with developers simply describing desired outcomes and walking away. He proposed that the advances meant that AI could disrupt jobs more severely than the COVID-19 pandemic."
"The essay drew mixed reactions. Some tech leaders agreed, including Reddit co-founder Alexis Ohanian, but others, including NYU professor Gary Marcus, criticized it as "weaponized hype." (Marcus noted that Shumer provided no data supporting claims that AI can write complex apps without errors.) Fortune's Jeremy Kahn also argued that it was coding's unique characteristics-like automated testing-that made it easier to fully automate, while the automation of other knowledge-work fields may be more elusive."
OpenAI's GPT-5.3-Codex and Anthropic's Claude Opus 4.6 mark notable advances in AI coding. GPT-5.3-Codex outperforms earlier models on coding benchmarks, while Opus 4.6 lets users deploy autonomous AI agent teams to work on complex projects simultaneously. Both models can write, test, and debug code with minimal human intervention and can iterate on their own outputs. Some claim these models can manage the full development cycle autonomously, producing and refining large codebases from high-level descriptions. Reactions are mixed: some endorse the potential, while others call the claims hype and point to limited supporting evidence. Coding’s testability is cited as a factor that could make automation easier than in other knowledge-work fields.
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