San Francisco: For decades, global services economy - from IT outsourcing in Bengaluru to management consulting in New York - was bound by a rigid arithmetic: to scale revenue, you had to scale headcount. If you were a BPO, a systems integrator or a creative agency, your output was capped by human hours. Growing the business meant growing the payroll.

But as we settle into 2026, a quiet revolution is dismantling this equation. We are entering an era of exponential leverage, where the defining characteristic of an 'AI-native' company is the decoupling of revenue growth from headcount growth. The metric of success is shifting from the size of your workforce to outcome per unit of intelligence. This shift is no longer theoretical. It is hitting the balance sheets of Global 2000.

For the last two years, we treated AI as a 'copilot', a tool to help individuals work faster. Now, we are seeing the shift to 'agents', systems that execute entire workflows. This is the moment white-collar labour gets disrupted not by replacement but by re-architecture. The AI platform shift that is underway is resulting in the cost of cognitive labour for routine tasks collapsing, forcing every enterprise to choose between clinging to labour arbitrage or pivoting to intelligence arbitrage.

While the promise of autonomous agents is intoxicating, the reality is far messier. On complex reasoning and benchmarks like SWE-bench (software engineering benchmark) and GAIA (general AI assistants), fully autonomous agents often fail to complete multi- step tasks without human intervention. In real-world enterprise environments - where regulatory compliance and brand safety are paramount - these failure rates are non-negotiable. This 'reliability gap' creates a permanent, high-value role for the human: the maestro.

What does a maestro do? They don't just 'use' AI; they govern it. They design the workflow, set guard rails, monitor outputs and intervene when systems drift, much like a portfolio manager overseeing volatile assets rather than executing individual trades. Value of the human shifts from doing the work to ensuring the outcome.

The advantage of AI-native companies isn't just that they are cheaper; it's that they are faster. In a traditional enterprise, the feedback loop - launching a campaign, gathering data, analysing results and making decisions - can take weeks. AI-native companies collapse this latency. They don't just automate tasks but also automate loops.

Faster experimentation Instead of A/B testing two marketing messages over a month, an agentic system can test 200 variations in an hour, analyse the sentiment and iterate the copy in real time.

Reduced decision drag By processing vast streams of data instantly, 'computational capital' allows organisations to make decisions at the speed of software, not the speed of scheduled meetings.

Winners of the next decade will be companies that use this speed to outlearn their competition. We are already seeing the scale of this shift. IKEA offers one compelling blueprint. By deploying their AI agent, 'Billie', to handle 47% of all customer support queries, they didn't just cut costs but also reallocated talent.

The company retrained 8,500 call centre workers to become 'interior design advisers'. They swapped the routine 'human capital' required for answering basic questions with 'computational capital' (the agent) and reinvested the human potential into high-value creativity.

This represents a fundamental transfer of value. Organisations are swapping salary overhead (operating expenses) for inference costs (cost of goods sold). While we may need fewer humans to generate the same output, the demand for compute scales aggressively. In this model, the efficient allocation of compute becomes just as critical as the efficient management of people.

It is easy to look at these efficiency gains and the commoditisation of intelligence and conclude a future of doom and gloom for human cognitive labour. But the future of work is not humans vs AI. It's humans amplified by the computational leverage that comes with AI.

Winners won't be companies that automate most tasks, but those that redesign work itself by placing humans where judgement matters most and machines where scale matters most. Efficiency gains are starting to become real and are reshaping enterprises. The question is no longer whether this transition will happen, but whether we are building organisations and careers designed to conduct intelligence rather than compete with it.

The writer is co-founder, Touring Capital