How Vibe Coding Became a Hiring Opportunity for This Observability GCC

For much of the AI boom, the conversation has centred on productivity gains and job displacement. AI coding, or vibe coding, can generate software in seconds, while AI agents increasingly automate development tasks that once required teams of engineers. 

As a result, the dominant narrative has been that coding copilots and autonomous agents will reduce the need for software engineers. As AI takes over larger portions of software development, many have questioned whether enterprises will need fewer developers in the years ahead. 

Yet, a different story is emerging. The more code AI generates, the more organisations need people and tools to monitor, govern and troubleshoot that code once it reaches production. This is where India enters the picture. 

As the country’s growing network of global capability centres (GCCs) moves beyond execution and into product ownership, AI strategy and engineering leadership, they are becoming natural hubs for observability functions. 

According to Ashan Willy, CEO of New Relic, an observability company headquartered in San Francisco, AI is not reducing the need for software talent as many expected. 

Instead, it is changing the nature of work, increasing demand for observability, and pushing more engineering ownership towards India, where multinational companies already run large technology operations.

Why Observability?

According to staffing firm Xpheno, active tech job demand in India stood at 1,06,000 openings in July, up 14% from June after hitting a 28-month low. However, overall demand remained 8% lower than a year earlier, highlighting the uneven nature of the recovery.

Within that broader slowdown, GCCs continued to outperform. 

The July outlook showed active tech demand from GCCs reaching 18,000 openings, accounting for 17% of India’s total tech hiring market. While GCC hiring grew 6% month-on-month, it also recorded a 29% year-on-year increase, making it one of the strongest-performing cohorts even as hiring across IT services and other technology segments remained below last year’s levels.

At this time, when GCC hiring continues to outpace broader tech hiring in India, AI is reshaping hiring trends rather than simply replacing the human-in-the-loop.

Willy tells AIM that New Relic is increasingly seeing CTOs and observability leaders based in Indian GCCs making decisions for global operations. New roles are emerging around observability, AI governance, reliability engineering and product-led innovation. 

“There’s no question that there is more code being written now,” Willy says. “Humans are more abstracted from the code.”

The rise of AI coding assistants and agents has accelerated software development across industries. Yet enterprises are finding that speed comes with a trade-off. More code means more systems to track, more failures to investigate, and greater governance requirements.

According to Willy, companies are seeing new issues emerge once AI-generated code reaches production environments.

“When the code goes into production, what they’re finding is more issues with the code that’s being written,” he adds. “So observability becomes really important there.”

Observability refers to the ability to monitor applications, infrastructure and digital systems to understand how they perform and where failures occur. As enterprises move from experimenting with AI to deploying it at scale, observability platforms are becoming central to the process.

https://www.youtube.com/watch?v=JNOvfNIZNLA

More Code, More Questions

The challenge now extends beyond software quality. Enterprise leaders want visibility into the AI models their teams use, the costs they incur and the risks they introduce.

“What I get asked by both CIOs and CTOs is, interestingly, which AI is out there, what my engineers are using, what models and what type of code.”

The complexity increases further as organisations deploy AI agents. Unlike traditional software, agents interact with other agents, tools, databases and cloud services. That creates multiple points of failure and makes troubleshooting more difficult.

Willy adds that agents actually get very complicated because they call out to other agents, they call out to tools, and further. He believes this trend will drive significant growth for observability vendors.

“Observably is going to just explode with the whole AI observably piece,” he states.

That growing demand has also become a tailwind for New Relic, which competes in a market that includes vendors such as Datadog. Willy argues that enterprises increasingly want a complete view of applications rather than visibility into a single AI model.

“What we have found in practise is that these LLMs and foundational models are part of an application, not the whole application,” he says.

As AI becomes embedded into business software, companies need visibility across databases, cloud providers and application layers. That requirement aligns closely with New Relic’s core business.

The GCC Opportunity

The observability boom is also strengthening India’s position in global technology operations. Willy says he noticed a major shift during his visits to India over the past few years: GCCs weren’t just execution centres but were becoming innovative centres. 

When he joined New Relic, Willy contacted many of the company’s largest customers. He was surprised by how often technology leadership roles sat inside Indian GCCs.

“The number of customers whom I ended up talking to are CTOs in India or CTO minus one in India, running a GCC and observably strategy from India, for multinational companies,” he says.

That trend prompted New Relic to build a dedicated GCC strategy.

According to Willy, many observability leaders now operate from India while supporting global businesses. He believes AI will strengthen that shift by lowering the barriers to experimentation.

That allows teams to build prototypes faster and test ideas more quickly. As a result, innovation can originate from GCCs rather than waiting for direction from headquarters.

“When I was doing some product reviews with the team here, the number of ideas coming out of India that were unsanctioned by anyone else was coming out,” he says. “I just think you’re going to get more things being created here.”

How Jobs Might Differ

The widespread assumption around AI is that coding automation will reduce software engineering jobs. Willy sees a more nuanced outcome. While AI improves productivity, he believes organisations still need engineers to oversee systems, validate outputs and manage increasingly complex software environments.

“We also know that there are productivity losses as a result of that,” he says. “A productivity loss means you need more overseers for what’s happening, and you need to double-check things.”

He points to hiring trends that continue to favour software talent.

“We’re adding net jobs, both in India and in the US,” he adds.

More importantly, he notes that hiring is increasingly coming from non-technology companies that now view software as a strategic function. For India, that could mean sustained demand for engineering talent, especially within GCCs.

“I think that the GCCs, and India in particular, are going to write the software for the world, or be part of the software for the world,” Willy says. “And you still need software engineers to go do that.”

As AI generates more code, the value may no longer lie only in writing software. It may increasingly lie in understanding, monitoring and governing it. For India’s GCCs, that shift could move them closer to the centre of global technology decision-making.