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When Anthropic CEO Dario Amodei declared that AI would write 90% of code inside six months, the coding world braced for mass extinction. However inside Salesforce, a distinct actuality has already taken form.
“About 20% of all APEX code written in the last 30 days came from Agentforce,” Jayesh Govindarajan, Senior Vice President of Salesforce AI, instructed me throughout a current interview. His workforce tracks not simply code generated, however code really deployed into manufacturing. The numbers reveal an acceleration that’s not possible to disregard: 35,000 energetic month-to-month customers, 10 million traces of accepted code, and inner instruments saving 30,000 developer hours each month.
But Salesforce’s builders 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 traces of code to strategic management: How builders have gotten expertise pilots
Software program engineering has all the time blended creativity with tedium. Now AI handles the latter, pushing builders towards the previous.
“You move from a purely technical role to a more strategic one,” Govindarajan defined. “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 different technological disruptions. When calculators changed handbook computation, mathematicians didn’t vanish — they tackled extra advanced issues. When digital cameras killed darkrooms, pictures expanded quite than contracted.
Salesforce believes code works the identical method. As AI slashes the price of software program creation, builders achieve what they’ve all the time lacked: time.
“If creating a working prototype once took weeks, now it takes hours,” Govindarajan mentioned. “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 right here: Why software program engineers at the moment are orchestrating AI quite than typing each command
Coders have begun adopting what’s known as “vibe coding” — a time period coined by OpenAI co-founder Andrej Karpathy. The apply includes giving AI high-level instructions quite than exact directions, then refining what it produces.
There is a new sort of coding I name “vibe coding”, the place you absolutely give in to the vibes, embrace exponentials, and neglect that the code even exists. It is potential as a result of the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Additionally I simply speak 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 mentioned. “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 method to musical collaboration: “The AI sets the rhythm while the developer fine-tunes the melody.”
Whereas AI excels at producing easy enterprise functions, 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 brand new high quality crucial: Why testing methods should evolve as AI generates extra manufacturing code
AI doesn’t simply write code in another way — it requires totally different high quality management. Salesforce developed its Agentforce Testing Heart after discovering that machine-generated code demanded new verification approaches.
“These are stochastic systems,” Govindarajan defined. “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 builders should grow to be consultants at boundary testing and guardrail setting. They should know not simply learn how to write code, however learn how to consider it.
Past code era: How AI is compressing your entire software program growth lifecycle
The transformation extends past preliminary coding to embody the total software program lifecycle.
“In the build phase, tools understand existing code and extend it intelligently, which accelerates everything,” Govindarajan mentioned. “Then comes testing—generating regression tests, creating test cases for new code—all of which AI can handle.”
This complete automation creates what Govindarajan calls “a significantly tighter loop” between thought and implementation. The quicker builders can check and refine, the extra formidable they’ll grow to be.
Algorithmic considering nonetheless issues: Why pc science fundamentals stay important within the AI period
Govindarajan regularly fields anxious questions on software program engineering’s future.
“I get asked constantly whether people should still study computer science,” he mentioned. “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 adjustments is how these abilities manifest. As an alternative of typing out every answer character by character, builders information AI instruments towards optimum outcomes. The human supplies judgment; the machine supplies pace.
“You still need good intuition to give the right instructions and evaluate the output,” Govindarajan emphasised. “It takes genuine taste to look at what AI produces and recognize what works and what doesn’t.”
Strategic elevation: How builders have gotten enterprise companions quite than technical implementers
As coding itself turns into commoditized, developer roles join extra on to enterprise technique.
“Developers are taking supervisory roles, guiding agents doing work on their behalf,” Govindarajan defined. “But they remain responsible for what gets deployed. The buck still stops with them.”
This elevation locations builders nearer to decision-makers and farther from implementation particulars—a promotion quite than an elimination.
Salesforce helps this transition with instruments designed for every stage: Agentforce for Builders handles code era, Agent Builder allows customization, and Agentforce Testing Heart ensures reliability. Collectively, they kind a platform for builders to develop into these expanded roles.
The corporate’s imaginative and prescient presents a stark distinction to the “developers are doomed” narrative. Quite than coding themselves into obsolescence, software program engineers who adapt might discover themselves extra important than ever.
In a discipline the place reinvention is routine, AI represents probably the most highly effective compiler but—remodeling not simply how code is written, however who writes it and why. For builders prepared to improve their very own psychological fashions, the long run seems much less like termination and extra like transcendence.
