the offshore labor arbitrage is over
The offshore knowledge-work boom was built on selling labor cheaper than the West could afford. AI has made labor the wrong thing to be selling.
For the past two years, the dominant anxiety in knowledge work has been about AI replacing jobs. The conversation has focused almost entirely on workers in high-income countries, the American paralegal, the British copywriter, the German software developer, watching AI eat into their task bundles and wondering what survives. That framing misses the more interesting disruption, and the more consequential one.
In April 2025 I wrote that the real AI threat was not AI alone but AI combined with offshore labor. A knowledge worker in Manila or Karachi earning $12,000 a year, equipped with the same AI tools as a $95,000 professional in New Jersey, was no longer just cheaper. They were faster, more scalable, and increasingly capable of work that geography and institutional complexity, what I referred to as “the context barrier”, had previously kept local. The cost advantage of offshore labor was always real. AI turned it into something closer to an unfair fight. The piece argued that this combination, AI x offshoring, was the disruption American professionals and policy-makers were not prepared for.
That argument was right, but it was incomplete in a way that changes everything for the offshore labor markets.
The same dynamic that made an AI-equipped offshore worker dangerous to a mid-career American professional operates with equal force in the other direction. AI does not compress the value of codifiable, repeatable cognitive work only in markets where that work is expensive. It compresses it everywhere. When a workflow goes from requiring a hundred hours of human attention to requiring two hours, the maximum dollar you can save by moving those two hours to a cheaper geography collapses. The friction costs stay constant: timezone gaps, security reviews, quality variance, management overhead. The savings shrink. At some point the arithmetic tips, and the case for offshoring the residual human task disappears, not because the offshore worker got worse but because there isn’t as much to save by offshoring.
What survives this compression is not the affordable, competent, English-speaking generalist who can execute a well-defined brief faster and cheaper than a domestic equivalent. That is precisely the profile AI renders redundant first, because it is defined entirely by the ability to perform codifiable work at lower cost. What survives is work that cannot be codified: judgment calls with professional liability attached, client relationships where trust is the product, domain expertise embedded so deeply in specific institutional or market context that no model trained on public data can reliably replicate it. That work tends to be senior, credentialed, and local. The affordable generalist in the middle, the layer that grew fastest during the offshore boom and that the original argument crowned as the rising competitive threat, is the layer the current AI frontier hits hardest.
The evidence is not speculative. Entry and mid-level knowledge-work postings across high-income economies fell between 14 and 41 percent from 2022 to 2024 across 42 independent studies. McKinsey found in 2025 that 51 percent of organizations were already reducing their need for entry-level roles. HFS Research, which tracks enterprise services contracts at scale, declared in April 2026 that the labor arbitrage model had passed its shelf life. Everest Group reached the same conclusion independently. These are observations of what enterprise buyers are already doing with their services budgets.
The offshore knowledge-work model exists at scale in various geographies with meaningfully different exposure profiles.
Model 1: India built a mature IT services pyramid over three decades, with deep institutional client relationships and enough seniority concentration to absorb significant junior-layer compression without existential sector damage.
Model 2: The Philippines built a BPO economy employing 1.9 million people and accounting for 8.5 percent of GDP, concentrated in voice and back-office functions that sit directly in the path of agentic AI.
Model 3: Pakistan built something younger, faster-growing, and more freelance-dependent than either, with an export base that grew from $2.6 billion in FY23 to $3.8 billion in FY25, and a national development strategy built entirely around continuing to grow it.
I’m going to focus on Model 3: Pakistan here, because it is the most exposed and the least diversified. I’m seeing it first hand because I happen to be visiting right now. The gap between what its official numbers show and what the underlying model can sustain is wider here than anywhere else in the offshore knowledge-work world. Understanding why requires looking carefully at what those numbers actually measure, and what they are structurally blind to.
The Scoreboard Is Lying
Pakistan’s recent IT export numbers, on their face, appear to be a success. After years of sclerotic growth, the sector generated $2.6 billion in FY23, $3.2 billion in FY24, and $3.8 billion in FY25, growth of 24 percent and then 18 percent in consecutive years. Freelancing earnings tracked by the State Bank of Pakistan ran from $408 million in FY24 to $779 million in FY25, with the current fiscal year already at $1.06 billion through eleven months. The IT Minister stated in the National Assembly as recently as May 2026 that the government’s target is $15 billion in IT exports by 2030. Three separate official targets are currently in circulation, the IT Minister’s $15 billion by 2030, the Prime Minister’s $25 billion over five years announced in November 2024, and the Uraan Pakistan national economic plan’s $10 billion ICT target by FY29, none of them identical, but all pointing in the same direction. The story the numbers tell is of an industry compounding rapidly toward a destination that justifies the ambition.
The problem is what the numbers cannot see.
Pakistan’s IT export statistics are denominated in dollars and measure inflows. They do not distinguish between a dollar earned by licensing software to a foreign buyer and a dollar earned by billing an offshore hour to a foreign client. A firm that writes proprietary code and sells access to it globally, and a firm that supplies developers by the month to a US enterprise that tells them what to build, appear identically in the export ledger. This is not a minor accounting detail. It is the difference between an industry building durable, compounding IP and an industry selling time, and the two have entirely different exposure profiles as AI compresses the value of billable hours.
The listed company data is instructive precisely because it is the most transparent slice of the sector available. Systems Limited, Pakistan’s largest listed IT company by revenue at $286 million in FY25, does not report a product or IP revenue line in its investor disclosures. Its R&D expenditure in FY25 was PKR 82 million against PKR 80.4 billion in total revenue, less than 0.1 percent. The company’s revenue breakdown in public filings is by geography and by vertical, not by whether the underlying work is services delivery or IP licensing. That omission is not incidental. It reflects what the business actually is: a large, sophisticated, well-run services firm whose revenue is predominantly generated by deploying human expertise into client workflows. There was nothing wrong with that model but there is now a question of such models perform as AI reduces the volume of human expertise those workflows require.
The one meaningful counterexample in Pakistan’s listed universe is NetSol Technologies, a Lahore-based company focusing on the global asset finance and leasing industry. Its U.S. SEC filings, which are more granular than Pakistani exchange disclosures because US listing requirements demand it, show subscription and SaaS revenue at roughly 50 percent of its $66 million FY25 total, built on licensed IP developed over two decades from its Lahore technology center. NetSol is the most documented case of a Pakistan-origin company with genuine recurring product revenue. It is also a $66 million company in a sector reporting $3.8 billion in annual exports. As a share of the total, it is a rounding error.
The freelancing numbers carry their own complication. The 90 percent jump in SBP-reported freelancing earnings from FY24 to FY25, from $408 million to $779 million, attracted significant official celebration. It has also attracted significant scrutiny. Industry insiders and Federal Board of Revenue officials have raised questions about whether the figures reflect genuine gig-economy growth or systematic misclassification of salaried remote employees as freelancers to access Pakistan’s 0.25 percent preferential income tax rate for IT exporters. The dispute has not been resolved. What it means in practice is that even the sector’s best-performing sub-metric is of uncertain provenance, and the scoreboard may be flattering itself on the line item it most wants to highlight.
None of this means Pakistan’s IT sector is failing. It is growing, by any available measure, faster than many comparable offshore markets. The failure is at the level of the metric. A national strategy optimized for a dollar figure that counts billed hours and licensed software identically is not a technology strategy but purely a headcount strategy disguised within technology vocabulary. The targets, $15 billion, $25 billion, $10 billion, differ from each other and share one feature: none of them are grounded in a disclosed model of how an export base that is overwhelmingly services-and-labor transitions to one that is meaningfully IP-and-product. Topline Securities assessed that hitting the Uraan Pakistan FY29 target alone requires 27 percent annual growth against the 18 percent achieved in FY25. The target assumes the current model accelerates. The current model is the one under structural threat.
What the Pyramid Looks Like When the Base Disappears
Sitting with a friend who is the founder of an offshore IT services firm in Karachi, the conversation returned to the same metric: how many developers they could place with US clients this quarter, which verticals were still adding headcount, where the next staff augmentation contract was coming from. The AI revolution didn’t really factor into this conversation. It seemed remote, like something that was happening to professionals in high-cost countries. The possibility that it was also happening to them didn’t come up.
The clearest evidence of what is coming for offshore knowledge work is already in the hiring data of India’s Infosys.
In FY23, Infosys hired more than 50,000 college graduates. In FY24 it hired 11,900, a 76 percent collapse, in a year when the company was not in financial distress. Revenue held. Client relationships held. The pyramid shrank because Infosys discovered it could produce the same output with a structurally smaller junior layer. The work that used to require fifty thousand entry points into the organization required fewer, because the tools handling the entry-level task bundle had changed. FY25 brought a partial recovery in headcount, a net addition of 6,388 employees, but ‘fresher’ hiring remained at a fraction of its prior level. The pyramid did not restore itself. It confirmed a new, lower base. NASSCOM’s sectoral analysis of the Indian IT and BPM industry, drawing on task-level data from over 10,000 roles, found 30 percent fresher hiring cuts and 20 to 25 percent entry-level consolidation across the sector. Infosys is the individual proof point for a structural shift that is industry-wide.
The mechanism matters for understanding what this means beyond India. Infosys is not an offshore vendor in the traditional sense. It is one of the primary buyers of offshore-style labor at scale, a company whose business model has for decades rested on recruiting large cohorts of trainable junior engineers, absorbing them into client delivery pyramids, and billing their hours to enterprises in the US and Europe. When Infosys cuts its fresher intake by 76 percent during a period of stable revenue, it is telling you something very precise: the volume of junior human input required per unit of client output has fallen sharply. That reduction in demand does not stay inside Infosys. It travels down the supply chain to every smaller vendor, every staff augmentation firm, every freelancer whose work feeds into the same delivery model. The demand erosion won’t show up as a reshoring decision or a policy change. It will be visible, if you look closely, in fewer purchase orders, shorter contracts, and narrower scope. And this is already in motion.
The Philippines illustrates the same dynamic at national scale, and with the added clarity of a sector that has always been explicit about what it is selling. The IT-BPM industry reported $38 billion in revenue and 1.82 million employees in 2024, growing to above $40 billion and 1.9 million in 2025. By the headline numbers, the sector is still expanding. But IBPAP president Jack Madrid, the industry’s own chief spokesman, described the sector’s origin in February 2026 in language that reads as a structural diagnosis: the industry began, he said, by hiring people at scale, and that was labor arbitrage. The capability that built the Philippine BPO industry, the ability to supply large numbers of English-speaking workers into voice and back-office workflows at a cost that justified the distance, is precisely the capability that agentic AI is designed to replace. A Filipino call-center worker interviewed for a 2024 Reuters Foundation report put it with more economy than most analysts: multinational companies came here because of our skill in customer care, and that is the first to be displaced.
The aggregate numbers have not yet turned negative because volume growth is currently outrunning per-unit headcount reduction. More work is being outsourced even as less human input is required per unit of that work. That arithmetic has a limit. As AI-native tooling matures inside client organizations, the volume of work that requires offshore human handling will begin to fall, not just the headcount required per unit but the total demand. The ascending disruption, where AI adoption by clients in high-income countries quietly reduces offshore order volumes without any formal reshoring decision being made, is the channel that matters most for markets like Pakistan and the Philippines, and it is the channel least visible in current headline data.
Pakistan sits at the intersection of both problems. Its export base is more heavily weighted toward staff augmentation and freelance delivery than either India or the Philippines, and it has less institutional depth, fewer decades of embedded client relationships, shallower balance sheets, and a younger ecosystem, to absorb the coming compression. The two floors of the knowledge-work building are pulling apart. The floor below, junior cognitive work, codifiable tasks, staff augmentation, basic software delivery, is the floor AI addresses most directly, and it is the floor Pakistan’s export economy predominantly occupies. The floor above, judgment-bearing work, IP ownership, domain-specific product development, requires a different kind of capital: R&D investment, patient venture funding, distribution infrastructure, and the kind of institutional trust with global buyers that takes years of delivered product to build.
The distance between those two floors is not unbridgeable. NetSol spent two decades building a subscription revenue base on licensed IP from its Lahore technology center and now earns roughly half its revenue from software that clients pay to access rather than humans they pay to direct. Motive, founded by a Pakistani entrepreneur and running its AI research team out of Lahore, built a fleet management and physical-economy automation platform that serves over 120,000 businesses globally, a case of genuine IP at scale built with Pakistani engineering talent at its core. The counter-case exists. AI does lower the minimum team size required to build a software product, and a small team in Karachi with current tools can attempt things that five years ago required a much larger organization.
The constraint is not access to tools. The same tools are available to a team in San Francisco with better distribution, deeper enterprise sales infrastructure, and an existing relationship with the buyer. The sustainable advantage for an emerging market product team is domain asymmetry, knowing something about a specific market or operational context that a Silicon Valley team does not and cannot easily acquire. Vertical software built on that asymmetry, priced in dollars, sold to global buyers, is the model that survives what is coming. It is not, however, what the current ecosystem is predominantly building, and the current export metric gives no one any particular incentive to change that. A billed hour and a licensed product look identical on the scoreboard. The scoreboard is the problem.
Adil Husain is a competitive strategist who advises CEOs on how to compete and grow in contested markets. He is the Founder and Editor-in-Chief of business media company The Intelligence Council, and Managing Director of the global advisory firm Emerging Strategy. He has spent 25 years advising C-level executives at global companies on competitive strategy, market entry, and international growth, with on-the-ground experience across China, Southeast Asia, and major emerging markets.
You can reach him here for a conversation: ahusain@emerging-strategy.com