Opendoor, the San Francisco-based online home-buying platform, is shutting down its India operations less than two years after expanding its presence in the country. The decision has become a flashpoint in the debate over whether AI is starting to alter the economics of offshore work.

In announcing the decision on Wednesday, CEO Kaz Nejatian cited a push to bring operational work back to the U.S., where Opendoor’s customers are, and a shift toward smaller AI-native teams. The company did not respond to requests for comment on how many employees were affected or how much of the decision was driven by AI efficiency. But the announcement quickly gained traction across Silicon Valley, where founders, investors, and outsourcing experts see it as an early example of how AI is reshaping the economics that made India a global hub for back-office operations.

To understand why they care, it helps to know what’s at stake for India. It has evolved far beyond its roots as a destination for outsourced back-office work. The country is now the world’s largest Global Capability Center market — a term for dedicated offshore units multinationals set up to handle everything from IT and finance to R&D — with more than 2,100 centers employing about 2.36 million people and generating nearly $100 billion in annual revenue.

Opendoor had built a large team in India to handle manual workflows across fragmented systems, Nejatian said. The company had nearly 250 employees in India when it opened offices in Chennai and Bengaluru in 2024. But the entire company has been scaling back in recent years. Securities filings show Opendoor employed 1,042 people globally at the end of last year, compared with 1,470 a year earlier. Similarly, its non-U.S. workforce declined to 184 employees at the end of last year, compared with 342 employees at the end of 2024.

Those broader workforce reductions make it difficult to view the India closure solely through the lens of outsourcing. Opendoor has been cutting costs across the business after a difficult period for the U.S. housing market that hit online home-buying companies especially hard. Still, the language Nejatian used to explain the move resonated with investors and outsourcing analysts who see AI reshaping how companies organize operational work.

Some investors viewed the decision as a sign of what AI could mean for India’s vast outsourcing workforce. “As manual work gets replaced by AI, a lot of jobs will be lost in India,” wrote Sheel Mohnot, co-founder of Better Tomorrow Ventures.

Others viewed Opendoor as evidence of a larger shift in how companies are organized. Keshav Lohia, a venture capitalist at Emergent Ventures, described the decision as a “watershed moment” for AI-driven operations, arguing that advances in AI are beginning to challenge the cost-arbitrage model that made India a popular offshoring destination.

Phil Fersht, chief executive of HFS Research, an advisory firm that tracks the global outsourcing and business services industry, told TechCrunch that the development should not be viewed simply as jobs moving from India to the U.S. The more important shift, he said, is that AI is reducing the amount of operational labor companies require in the first place, allowing firms to run leaner organizations regardless of location.

“This is not an isolated restructuring,” Fersht said. “It is part of a much broader pattern we are starting to see as companies redesign operations around AI, automation, and much leaner workflows.”

Fersht argued that the winners would be companies that combine AI, software, and human expertise to deliver outcomes without continually adding headcount, a model he described as “services-as-software.” While Opendoor may be one of the first high-profile examples, he said it is unlikely to be the last.

Some investors are already extrapolating beyond individual companies. Varun Rekhi, a venture capitalist at Speedinvest, argued that if AI reduces demand for labor-intensive services, it could eventually pressure one of India’s most important export industries, which is built around supplying talent and expertise to global corporations.

For now, Opendoor remains a complicated case study — a company that has been cutting headcount broadly for years, and whose India exit may say as much about its own struggles as it does about the future of AI and offshore work.

CBA chief executive Matt Comyn used the phrase ‘work slop’ to describe the low-quality AI output now flowing through corporate workflows, as token-billed AI costs scale with task complexity.

Matt Comyn, chief executive of the Commonwealth Bank of Australia, used a speech on Monday to flag two AI-adoption problems large corporate buyers have been working through quietly for several months.

The first is that the cost of running generative AI inside corporate workflows is rising substantially faster than most companies budgeted for as task complexity scales.

The second is what Comyn called “work slop”, the low-quality AI-generated text, code and analysis that flows through internal company systems when employees use AI without sufficient quality control.

The cost framing is the part that will resonate with the corporate-IT-buyer audience. Token-based pricing, the per-character billing model the foundation-model labs use to charge enterprise customers, has scaled in the past 18 months from a modest line item into a meaningful operating-expense category.

Comyn’s point is that the cost compounds faster than expected because token consumption per task rises non-linearly with task complexity: a simple summarisation task might consume 1,000 tokens, but a multi-step reasoning task with tool use can consume 100,000-plus tokens for the same output value. Companies that priced their AI rollouts on the simple-task baseline are now seeing bills that scale on the complex-task curve.

This problem is not specific to CBA. Morgan Stanley doubled its European-banking-AI-job-loss forecast last week partly on evidence that AI cost-benefit ratios are tightening at exactly the moment large institutions had hoped they would loosen. The token-cost-scaling problem Comyn described is the underlying mechanic: the same AI deployment that worked at pilot-stage volumes can produce 10-100x the costs at production-stage complexity.

The result is the corporate-AI procurement squeeze that Comyn predicted will tighten through 2026: businesses tightening scrutiny of AI-related spending as pressure mounts to demonstrate returns on investment.

The “work slop” framing is the more colourful but equally substantive half of the speech. The category Comyn was naming, low-quality AI-generated output that nominally completes a task but actually degrades downstream workflow, is the corporate-knowledge-work analogue of the social-media “AI slop” problem that emerged in 2024 with image-generation tools.

The bank version looks like this: an employee uses ChatGPT to draft a customer email, the email is technically grammatical but factually imprecise, the recipient takes the imprecision as a commitment, and the bank deals with the resulting complaint three weeks later at a substantially higher cost than the original work would have generated unaided.

The CBA-specific context is significant. The bank announced 90 job cuts earlier this year and a further 120 cuts in May explicitly attributed to AI-driven productivity gains, alongside a A$90m AI-workforce reskilling commitment.

Comyn’s remarks therefore land inside a CBA strategy that has visibly committed to AI substitution at scale: the “work slop” framing is not a defensive critique of AI from a bank that has rejected the technology but a sharper inside-baseball read on AI deployment from one of Australia’s largest current adopters.

The wider Australian-bank context is also worth noting. Sam Altman has been arguing over the past month that an AI jobs apocalypse is unlikely at the macro level, and the labour data through March 2026 has so far supported the conservative read.

Comyn’s remarks complicate that picture: the macro labour data does not yet show large-scale displacement, but the operating-margin data inside large corporates is starting to show the AI-cost-and-quality tradeoffs CBA is now naming explicitly.

The substantive implication is that the 2024-2025 AI cost narrative, that token prices were falling so quickly that the deployment-economics question would solve itself, has structurally inverted.

Falling per-token prices have been overwhelmed by rising per-task token consumption as enterprises move from pilot deployments to production use cases. The procurement-discipline phase Comyn is forecasting through 2026 is, on this evidence, the predictable consequence.