Salesforce Spending Hundreds Of Millions On AI Coding Tools After Engineering Hiring Freeze

Salesforce CEO:

• Marc Benioff

has revealed that the company could spend around:

• $300 million

on:

• Anthropic AI tokens

during 2026, highlighting how major tech firms are increasingly investing in AI systems instead of expanding engineering teams.

AI Replacing Traditional Hiring Growth

Last year, Benioff announced that Salesforce would:

• stop hiring software engineers in 2025

after the company achieved:

• more than 30% productivity gains

through:

• AI coding tools
• Agentforce
• AI-powered engineering workflows.

Despite the hiring freeze, Salesforce says:

• engineers are still essential
• AI currently acts as an assistant rather than a full replacement.

How Engineers Are Working Now

According to Benioff:

• developers are increasingly supervising AI coding agents

instead of manually writing every line of code.

Salesforce engineers are reportedly using:

• Anthropic Claude
• OpenAI Codex
• Cursor
• Agentforce AI tools

to accelerate:

• coding
• debugging
• software delivery
• automation tasks.

Massive AI Spending Shift

The projected:

• $300 million AI token spending

shows how enterprise software companies are shifting budgets from:

• workforce expansion to:
• AI infrastructure and automation.

Salesforce has already invested:

• over $300 million into Anthropic

and reportedly owns a stake in the AI startup.

AI Driving Business Growth

Salesforce says:

• Agentforce

its AI-focused business platform, has now reached:

• $800 million in annual recurring revenue.

Meanwhile:

• Slack AI features powered by:
• Anthropic Claude

continue expanding across enterprise customers.

Benioff’s “Digital Labour Revolution”

Benioff described the shift as:

• a “digital labour revolution”

with AI now handling:

• 30% to 50% of Salesforce’s internal workload.

However, he also acknowledged:

• fully autonomous AI engineers do not yet exist.

Industry-Wide Trend

The move reflects a broader tech industry trend where companies are:

• reducing traditional hiring
• increasing AI investment
• prioritising AI-assisted productivity.

Many firms now prefer:

• smaller engineering teams working alongside:
• advanced AI systems.