Software engineers may be paying the price for Silicon Valley's tokenmaxxing frenzy.

As companies push developers to use AI coding tools in the hopes of driving productivity up, a growing divide is emerging inside some engineering teams, says Deedy Das, a partner at venture capital firm Menlo Ventures, where he invests in AI and enterprise software companies.

First are what he calls the "lazy" engineers — workers who rely heavily on AI to write code, answer questions, prepare updates, and complete tasks with minimal engagement.

Then there are the "craftsmen," experienced engineers who bear the burden of understanding, reviewing, and fixing the growing flood of AI-generated code.

"Most software engineers are facing an identity crisis bordering on depression," Das said in an X post over the weekend.

'The craft they loved is dead'

Das' comments tap into a growing debate over how AI coding tools are reshaping software engineering, a theme at the center of Business Insider's latest series, The Great Coding Reset.

As companies encourage developers to generate more code with AI, some engineers say their role is shifting from writing software to reviewing, managing, and validating machine-generated work — a change that is forcing some to rethink where their value lies.

The broader problem is what Business Insider's Amanda Hoover recently described as "AI sprawl": workers juggling multiple AI tools, duplicating work, and generating ever-increasing volumes of output without clear evidence that companies are becoming substantially more effective.

"The craftsmen are tired. Very tired," Das wrote. "The entire burden of review falls on the craftsman. The burden of understanding."

As AI makes code generation easier, reviewing and maintaining that code has become the new bottleneck.

The shift echoes the rise of "botsitting" — supervising AI, fixing its mistakes, and validating its work.

Das, who previously said many software engineers feel "their life's skill is no longer useful" amid the AI boom, said some engineers now often find themselves buried under pull requests while bugs increasingly slip into production.

"The craft they loved is dead," he wrote.

While Das said many companies are successfully integrating AI into software development, he said the tension is common at large organizations where AI-generated output is growing faster than teams' ability to evaluate it.

"It tends to happen in bigger companies that are 10+yrs old with a higher talent variance," he said. "But it happens. A lot."