He used chess as an example: 15 to 20 years ago, a human checking AI's output could beat an AI or a human playing alone. Now, AI can beat people without that layer of human supervision. Amodei, who cofounded AI lab Anthropic in 2021, added that the same transition would happen in software engineering. "We're already in our centaur phase for software," Amodei said. "During that centaur phase, if anything, the demand for software engineers may go up. But the period may be very brief."
Over the past seven years, I've worked for multiple companies shipping blockchain integrations and production-grade financial infrastructure in Lagos, Amsterdam, and now Toronto. I started my career in Nigeria, where I was born and raised, and moved to Amsterdam in May 2023 after a short career break for a job as a senior software engineer. I returned to Nigeria after a year and worked until I received my Canadian work visa through my wife in October 2024.
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"This has been said a thousand times before, but allow me to add my own voice: the era of humans writing code is over," Dahl wrote. "Disturbing for those of us who identify as SWEs, but no less true. That's not to say SWEs don't have work to do, but writing syntax directly is not it."
Just as software finished eating the world, zero interest rates ended. Companies optimized for cash and slowed hiring. The market didn't shrink, but stopped growing at the breakneck pace we all expected. The result: a glut of entry level talent groomed for jobs that never materialized. This would explain a more competitive entry level market. But it doesn't explain the entry-level market shrinking, despite overall industry growth. In short: demand for senior talent is rising, but has fallen off a cliff for juniors.
"The vast majority of Codex is built by Codex," OpenAI told us about its new AI coding agent. In interviews with Ars Technica this week, OpenAI employees revealed the extent to which the company now relies on its own AI coding agent, Codex, to build and improve the development tool. "I think the vast majority of Codex is built by Codex, so it's almost entirely just being used to improve itself," said Alexander Embiricos, product lead for Codex at OpenAI, in a conversation on Tuesday.
I moved to the US from India in 2021 to attend Amherst College, where I triple-majored in computer science, mathematics, and statistics. During my freshman year, I developed a support system for statistical programming that became part of an introductory statistics course. Opportunities to talk about my work on and off campus started coming up, which led to different perspectives, insights, and connections. I thought about how I could scale this up to a broader audience.
"While businesses aren't investing, it's hard to make much progress in the software world," Fowler said. "And so we have this weird mix of no investment, pretty much depression in the software industry, with an AI bubble going on."
IT development has been around for more than 60 years and it has undergone radical transformations from the emergence of the first programming languages and OS development to the internet boom and the current AI era. Although programming tools and approaches are constantly changing, one thing remains constant: Only those developers who can adapt and master new knowledge and skills survive.
Your competitors aren't building perfect code either. If you spend 6 months building the theoretically perfect architecture, they'll ship something "good enough" in 2 months and eat your lunch. You'll have beautiful code that nobody uses. Tech debt is the cost of moving fast enough to win. The biggest risk in software isn't technical debt-it's being irrelevant. Markets move fast. User needs evolve. Competitors iterate. While you're refactoring for the third time to achieve "clean architecture," your competitor is talking to users, learning what actually matters, and shipping features that solve real problems.
One of the most notable innovations is support for compaction, a technique that enables the model to seamlessly manage multiple context windows and perform tasks involving millions of tokens within a single session. This enables large-scale refactors, extensive debugging rounds, and work cycles lasting several hours. GPT-5.1-Codex-Max is available immediately within the Codex environment in the CLI, IDE extension, and cloud; API access will follow later.
For Andrew Wang, a 33-year-old software engineer, AI has become a part of his daily routine - but it's come at the cost of some of the casual conversations he used to have with colleagues. Wang said the growing reliance on large language models has, at times, made teams feel more "siloed." In the past, coworkers would regularly hash out ideas and design options in impromptu conversations, he said. Now, much of that back-and-forth happens with AI instead - leaving formal meetings for design or code reviews.
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"I made this prediction that, you know, in six months, 90% of code would be written by AI models," Amodei said. "Some people think that prediction is wrong, but within Anthropic and within a number of companies that we work with, that is absolutely true now."
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Gill's prediction has come true: developers are now regularly using AI coding assistants to generate code, but the output is often buggy, forcing engineers to spend a lot of time on corrections. CodeRabbit can help catch some of the errors. The business has been growing 20% a month and is now making more than $15 million in annual recurring revenue (ARR), according to Gill.
CEO Marc Benioff said during a Labor Day podcast that the company has already cut 4,000 customer support roles after deploying its own AI agents. "I've reduced it from 9,000 heads to about 5,000 because I need less heads," Benioff said, calling the past eight months "the most exciting" of his career. (A Salesforce spokesperson later clarified that many of the affected jobs were support engineers who were shifted into other roles.)
I got my Master's in Computer Science in 2011, and like others, I got tracked into coding as a software engineer. I started my career as a Java engineer developing software applications. Six or seven years later, I came across the profile of machine learning. Machine learning was not in a boom at that moment. The projects we got were almost always software engineering; machine learning projects were really, really hard to get.
