The careful, coded corporate language executives once used in describing staff cuts is giving way to blunt boasts about ever-shrinking workforces. Gone are the days when trimming head count signaled retrenchment or trouble. Bosses are showing off to Wall Street that they are embracing artificial intelligence and serious about becoming lean. After all, it is no easy feat to cut head count for 20 consecutive quarters, an accomplishment Wells Fargo’s chief executive officer touted this month.

AI assistants optimize for making tests pass and errors disappear. Without clear direction, they’ll take the path of least resistance. Common shortcuts to watch for: TypeScript any types appearing when proper typing gets complex Tests getting commented out or skipped when they’re hard to fix Quick fixes that address symptoms rather than root causes Great write up on a using claude code cli. And this was in July before 3.

So from an executive perspective, lighting comically large piles of money on fire trying to teach graphics cards how to read is, surprisingly, the logical play. The rest, well, that’s all just creative marketing. It’s very difficult to show up to a quarterly shareholder meeting and tell your investors you just vaporized another $10 billion for absolutely no return-on-investment. At least, that is, without them questioning if you’ve completely lost your mind.

AI is increasingly automating many coding tasks, accelerating software development. As models and tools improve, we see the automation of more complex coding tasks under developers’ orchestration (like the ones we interviewed). This is already reality and no longer a future trend. I think the reality is no one ever cared how nice my lines of code were. Anyone outside of software just wants the damn thing to work. SO using AI is just another tool.

I care about what gets merged into the codebase. I don’t care how the code got in your IDE. I want you to care. I want people to care about quality, I want them to care about consistency, I want them to care about the long-term effects of their work. LLMs are engineering marvels, and I have the utmost respect for the people who’ve created them. But we still need to build software, not productionize prototypes.

These patterns are particularly striking in technology sectors, where workers might expect AI to augment rather than to replace their roles. Software developers, data analysts and other tech professionals are finding that AI tools can indeed accelerate certain tasks, but potentially at the cost of overall employment demand. Our results suggest we may be witnessing the early stages of AI-driven job displacement. Unlike previous technological revolutions that primarily affected manufacturing or routine clerical work, generative AI can target cognitive tasks performed by knowledge workers—traditionally among the most secure employment categories.

This 1.34 percentage point increase represents more than just a statistical noise; it reflects a significant shift in how the economy is absorbing newly educated workers. The magnitude of this change becomes even more striking when compared with that of other demographic groups. Noncollege-educated workers in the same age range have seen only a modest 0.47 percentage point increase in unemployment, while older college graduates have experienced a 0.38 percentage point rise.

“When we talk about acceptance rate, a lot of the metrics that were popularized early on were metrics that were meant to show whether or not the tools were fit for purpose, not to measure the impact of them across an organization,” she explained. Seeing lots of “acceptance” rate and “% of code written” here’s the deal. we solved the problem. its outcomes. how many shippable units that delight your customers.

Some voices within the industry began to wonder if the A.I. scaling law was starting to falter. “The 2010s were the age of scaling, now we’re back in the age of wonder and discovery once again,” Ilya Sutskever, one of the company’s founders, told Reuters in November. “Everyone is looking for the next thing.” A contemporaneous TechCrunch article summarized the general mood: “Everyone now seems to be admitting you can’t just use more compute and more data while pretraining large language models and expect them to turn into some sort of all-knowing digital god.

SUMMARY • 66% of respondents have adopted AI tools in production. • 85% are focused on internal engineering use cases. • 59% of respondents feel AI has increased productivity. The tooling landscape Whether overzealous leaders have mandated adoption or allowed engineers to discover these tools themselves, it’s safe to say that AI coding assistants and large language models (LLMs) are firmly part of the software developer’s tool belt today. Two-thirds (66%) of respondents have adopted AI tools or models for at least some use cases, with 20% at a pilot stage, and 13% still exploring.