In March 2026 the independent research institute METR faced an unusual problem. It was to evaluate a new AI model. Standard procedure: set tasks, measure performance, record the result.
The model had reached the ceiling of the test system. The length of task it could complete autonomously stood at at least sixteen hours of human working time — with a confidence interval reaching fifty-five hours. METR had to attach a note to the report: Our own tasks are no longer sufficient to measure the model's capability.
That is not marketing copy. That is a measurement institute having to rebuild its instrument because the technology outran the research.
The number you need to know
Anyone wanting to understand the development needs to internalise one figure: the length of tasks AI agents reliably complete on their own doubles every seven months — consistently, over six years. Since 2024 that rhythm has accelerated to between four and five and a half months.
This is not a linear improvement but an exponential curve with a rising gradient. Extrapolate it over two years and you reach a result most annual plans do not price in: tasks that cost a human several weeks could fall into the autonomous range.
The paradox most people miss
Speed up one part of a process and the next-slowest part becomes visible. The term comes from computer science: Amdahl's law, formulated in 1967. The overall gain from speeding up a system is limited by the share that cannot be sped up.
Applied to organisations: When AI radically accelerates execution, the part requiring human judgement becomes the new bottleneck. Not because it gets worse — but because it is suddenly so much slower by comparison.
GitHub shows this at scale: in 2025 the platform recorded a billion code contributions in a single year for the first time. At the same time a well-known open-source maintainer estimates that only one in ten machine-generated contributions is actually usable. More speed, more output — and human judgement as the bottleneck.
Three observations from more than twenty years of transformation work
AI makes organisational failure visible — faster than any audit
When a company introduces AI-supported processes and the results disappoint, it is almost never the technology. It is whatever was not working before and now becomes visible faster. Unclear goals produce unclear results more quickly with AI. Decision paths that used to take weeks still take weeks — because the culture of alignment has not changed, only the tool.
McKinsey puts numbers on it: only six per cent of companies worldwide count as AI high performers. Ninety-two per cent plan to increase their AI investment — but only one per cent considers itself genuinely AI-mature. The gap between willingness to invest and organisational maturity is not small. It is structural.
Responsibility does not delegate itself to algorithms
An analysis of several US studies shows: seventy-two per cent of respondents said they had received better advice from an AI system than from their manager. Almost half found it more emotionally supportive.
I read that not as a triumph of technology but as a finding about the quality of leadership — and as a warning. AI fills a vacuum. Where orientation is missing, where decisions are not communicated clearly, the machine steps in. Relieving in the short term, dangerous in the medium term: the capacity for human leadership atrophies when it is no longer needed.
Prioritisation is the new bottleneck skill
Modern systems can develop a solution starting from an unspecified problem. Humans supply the goal, no longer the method. The remaining performance gaps sit where someone must decide which goals should be pursued at all.
That is the new core skill: not execution, not even method — but the ability to ask the right questions. What are we trying to achieve? Which problems are worth solving? And, less comfortably: What do we stop doing? Without settling that, AI mainly serves to work faster on the wrong problem.
What leadership must deliver now
There is a temptation I observe in many companies: waiting for clarity. For better systems, for mature governance, for the right moment. It does not come. The curve does not wait.
Give orientation. Not as an instruction, but as a clear picture of how success will be recognised. AI systems cannot compensate for ambiguity in the goal. Leadership must supply that, more precisely than before.
Locate accountability. Especially when systems prepare decisions. Whoever implements a recommendation without understanding it still carries the consequence.
Enable learning loops. In an environment where capabilities double every four to five and a half months, an organisation's speed of learning is no longer a characteristic. It is the condition of survival.
Conclusion
Introducing tools without strengthening orientation, accountability and the capacity to learn merely accelerates the old system. An accelerated old system is not a transformation. It is a faster failure.
Self-improvement as a concept presupposes that the learning system is itself capable of learning. That holds for AI. It holds just as much for organisations.
Sources
- METR Time Horizon Study 1.1 — January 2026, measuring the length of autonomously solvable tasks
- McKinsey & Company: The State of AI — survey across 105 countries, share of AI high performers
- GitHub: Octoverse figures 2025 — volume of code contributions
- Resume Now: AI Trends — analysis of several US studies on AI advice and leadership
- Gene Amdahl: Validity of the Single Processor Approach — 1967, the basis of Amdahl's law
This article offers a professional assessment and does not replace legal or management advice.
