Big tech is still hiring software engineers despite claims AI will replace them – and Marc Benioff says that’s the ‘canary in the coal mine’ for whether the technology is up to scratch

Salesforce CEO Marc Benioff still believes AI is nowhere near capable of replacing software engineers, and the proof is in the fact that many major industry players are still hiring for roles in this domain.

Speaking during a recent podcast appearance, Benioff said that AI is undoubtedly having a positive impact on software engineering, enabling teams to ramp up production and unlock significant productivity gains.

The technology isn’t a silver bullet and an excuse to reduce headcount, however.

Human workers remain critical and many big tech companies leading the charge in AI are still hiring in areas like software engineering – a trend he said acts as the “canary in the coal mine” for AI’s current capabilities.

“The models still cannot operate autonomously,” he said. “We’re not at that level yet of AI.”

“Our engineering organisation is probably more than 30% productive, but I wouldn’t call it 100% more productive, and that’s why even in the top AI companies, if you go to their job boards, you’ll see they are still hiring a lot of engineers,” Benioff added.

“Even though these top AI companies … have these unbelievable models, they need a lot of humans. That’s probably the canary in the coal mine that we know that the models are not at that level yet.”

Statistics from TrueUp, a platform that tracks open positions in the tech sector, shows engineering job openings have been trending upward slightly over the last two years.

Yet despite continued demand - albeit mild compared to pre-2023 levels - concerns about the impact of AI on entry-level roles have been rising.

Analysis from IDC in November 2025 warned entry-level roles will be among the hardest hit by AI adoption, and developers have been firmly in the crosshairs with this trend.

The changing face of software engineering

The influx of AI tools in software engineering has precipitated significant change in the profession over the last two years. In late 2024, for example, Gartner warned anywhere up to 80% of engineers will be forced to upskill to accommodate for changing skills requirements.

In January last year, Benioff himself proclaimed that the company “might not hire any software engineers” due to the advances in generative and agentic AI tools.

Despite that bold claim, Benioff said the CRM giant still has over 15,000 engineers globally, which has given the company a vital insight into how the technology is changing daily workflows.

When asked about whether AI would mean engineers need to become “generalists”, Benioff noted that many are now acting in a supervisory capacity managing agents working away in the background.

“The key thing about these 15,000 engineers, they’re all out there all over the world, all of them can now be hugely augmented with these coding tools,” he said.

“It could be Anthropic 4.6, it could be OpenAI Codex, it could be Cursor,” Benioff added. “But they start to use these models, they’re now working not only with the AI, but agents to help them code and they can even become somewhat supervisory over these agents.”

The use of AI still requires humans in the loop to ensure security and safety standards are maintained, however. This has become a recurring talking point with the rise of AI coding over the last 18 months. Indeed, a slew of studies highlighting AI-generated code flaws and the need for more hands-on supervision by developers.

Research from CodeRabbit in December, for example, found that although AI is helping improve developer productivity, the efficiency gains are being offset by flawed code and security issues, with many facing increased levels of manual remediation.

The situation with agents is no different. Workers across a range of professions are now managing these autonomous bots and encountering higher workloads.

Six in 10 knowledge workers who manage agents told Asana their roles are made more difficult due to “confidently wrong outputs”, highlighting the need for robust safeguards and supervision.

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