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When Anthropic CEO Dario Amodei declared that AI would write 90% of code within six months, the coding world braced for mass extinction. But inside Salesforce, a different reality has already taken shape.
“About 20% of all APEX code written in the last 30 days came from Agentforce,” Jayesh Govindarajan, Senior Vice President of Salesforce AI, told me during a recent interview. His team tracks not just code generated, but code actually deployed into production. The numbers reveal an acceleration that’s impossible to ignore: 35,000 active monthly users, 10 million lines of accepted code, and internal tools saving 30,000 developer hours every month.
Yet Salesforce’s developers aren’t disappearing. They’re evolving.
“The vast majority of development — at least what I call the first draft of code — will be written by AI,” Govindarajan acknowledged. “But what developers do with that first draft has fundamentally changed.”
From lines of code to strategic control: How developers are becoming technology pilots
Software engineering has always blended creativity with tedium. Now AI handles the latter, pushing developers toward the former.
“You move from a purely technical role to a more strategic one,” Govindarajan explained. “Not just ‘I have something to build, so I’ll build it,’ but ‘What should we build? What does the customer actually want?’”
This shift mirrors other technological disruptions. When calculators replaced manual computation, mathematicians didn’t vanish — they tackled more complex problems. When digital cameras killed darkrooms, photography expanded rather than contracted.
Salesforce believes code works the same way. As AI slashes the cost of software creation, developers gain what they’ve always lacked: time.
“If creating a working prototype once took weeks, now it takes hours,” Govindarajan said. “Instead of showing customers a document describing what you might build, you simply hand them working software. Then you iterate based on their reaction.”
‘Vibe coding’ is here: Why software engineers are now orchestrating AI rather than typing every command
Coders have begun adopting what’s called “vibe coding” — a term coined by OpenAI co-founder Andrej Karpathy. The practice involves giving AI high-level directions rather than precise instructions, then refining what it produces.
There’s a new kind of coding I call “vibe coding”, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It’s possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper…
— Andrej Karpathy (@karpathy) February 2, 2025
“You just give it a sort of high-level direction and let the AI use its creativity to generate a first draft,” Govindarajan said. “It won’t work exactly as you want, but it gives you something to play with. You refine parts of it by saying, ‘This looks good, do more of this,’ or ‘Those buttons are janky, I don’t need them.’”
He compares the process to musical collaboration: “The AI sets the rhythm while the developer fine-tunes the melody.”
While AI excels at generating straightforward business applications, Govindarajan admits it has limits. “Are you going to build the next-generation database with vibe coding? Unlikely. But could you build a really cool UI that makes database calls and creates a fantastic business application? Absolutely.”
The new quality imperative: Why testing strategies must evolve as AI generates more production code
AI doesn’t just write code differently — it requires different quality control. Salesforce developed its Agentforce Testing Center after discovering that machine-generated code demanded new verification approaches.
“These are stochastic systems,” Govindarajan explained. “Even with very high accuracy, scenarios exist where they might fail. Maybe it fails at step 3, or step 4, or step 17 out of 17 steps it’s performing. Without proper testing tools, you won’t know.”
The non-deterministic nature of AI outputs means developers must become experts at boundary testing and guardrail setting. They need to know not just how to write code, but how to evaluate it.
Beyond code generation: How AI is compressing the entire software development lifecycle
The transformation extends beyond initial coding to encompass the full software lifecycle.
“In the build phase, tools understand existing code and extend it intelligently, which accelerates everything,” Govindarajan said. “Then comes testing—generating regression tests, creating test cases for new code—all of which AI can handle.”
This comprehensive automation creates what Govindarajan calls “a significantly tighter loop” between idea and implementation. The faster developers can test and refine, the more ambitious they can become.
Algorithmic thinking still matters: Why computer science fundamentals remain essential in the AI era
Govindarajan frequently fields anxious questions about software engineering’s future.
“I get asked constantly whether people should still study computer science,” he said. “The answer is absolutely yes, because algorithmic thinking remains essential. Breaking down big problems into manageable pieces, understanding what software can solve which problems, modeling user needs—these skills become more valuable, not less.”
What changes is how these skills manifest. Instead of typing out each solution character by character, developers guide AI tools toward optimal outcomes. The human provides judgment; the machine provides speed.
“You still need good intuition to give the right instructions and evaluate the output,” Govindarajan emphasized. “It takes genuine taste to look at what AI produces and recognize what works and what doesn’t.”
Strategic elevation: How developers are becoming business partners rather than technical implementers
As coding itself becomes commoditized, developer roles connect more directly to business strategy.
“Developers are taking supervisory roles, guiding agents doing work on their behalf,” Govindarajan explained. “But they remain responsible for what gets deployed. The buck still stops with them.”
This elevation places developers closer to decision-makers and further from implementation details—a promotion rather than an elimination.
Salesforce supports this transition with tools designed for each stage: Agentforce for Developers handles code generation, Agent Builder enables customization, and Agentforce Testing Center ensures reliability. Together, they form a platform for developers to grow into these expanded roles.
The company’s vision presents a stark contrast to the “developers are doomed” narrative. Rather than coding themselves into obsolescence, software engineers who adapt may find themselves more essential than ever.
In a field where reinvention is routine, AI represents the most powerful compiler yet—transforming not just how code is written, but who writes it and why. For developers willing to upgrade their own mental models, the future looks less like termination and more like transcendence.
