Jason’s prediction was that by sometime in 2025 AI tools would hit a tipping point in adoption such that many organizations achieve substantial labor arbitrage (one worker effectively doing the work of several / replacing large chunks of lower‑skilled knowledge work).

What we do see by late 2025

• Multiple surveys show very broad individual adoption of AI at work. Microsoft’s Work Trend Index data, summarized by Worklytics, reports that about 75% of global knowledge workers are now using AI tools regularly, with usage roughly doubling in the prior six months. (worklytics.co) A Clutch survey finds 74% of full‑time workers regularly use AI on the job. (lifewire.com)
• Microsoft’s 2025 Work Trend Index for Switzerland reports that 52% of Swiss organizations already use AI agents to automate business processes, and 72% of leaders plan to deploy AI agents as “digital team members” to expand workforce capacity over the next 12–18 months—clear signs of serious organizational adoption and intent to use AI as a capacity multiplier. (news.microsoft.com)
• McKinsey’s 2025 "AI in the workplace" report finds that 94% of employees and 99% of C‑suite leaders have some familiarity with gen‑AI tools, and about 13% of employees already use gen‑AI for more than 30% of their daily work. The report explicitly describes the situation as “beyond the tipping point” in terms of awareness and early use. (mckinsey.com)
• Field and survey studies show real productivity effects: a 6‑month randomized experiment across ~6,000 knowledge workers found that those with an integrated gen‑AI assistant spent about 25% less time on email and finished documents somewhat faster, demonstrating that some employees can handle more output in the same hours. (arxiv.org) A systematic review of practitioner studies reports that many professionals delegate routine tasks to gen‑AI and sometimes bypass peers/subordinates in favor of AI, indicating nascent task‑level substitution. (arxiv.org)
• Executives increasingly talk in explicitly labor‑arbitrage terms. A BearingPoint survey reports that around half of executives believe their firms are already 10–19% overstaffed due to automation and AI and expect overcapacity could reach up to 50% in three years, especially in back‑office, customer service, and entry‑level finance/HR roles. (techradar.com) This shows that some organizations perceive AI as enabling fewer people to cover the same workload.

What we do not clearly see yet

• The same McKinsey 2025 report emphasizes that, despite high familiarity, most organizations are still moving slowly from pilots to scaled deployment; 47% of C‑suite leaders say their organizations are releasing gen‑AI tools too slowly and are still building or refining roadmaps. (mckinsey.com) That is more consistent with early transformation than a completed tipping point in structural labor arbitrage.
• Research on job exposure finds that nearly all jobs have some generative‑AI exposure but only a minority are heavily affected so far. The GAISI study for the UK shows widespread exposure but relatively limited heavily affected roles, and early evidence that displacement effects may be emerging, not yet dominant. (arxiv.org)
• Macro‑level analyses from McKinsey estimate that half of current work activities could be automated only between about 2030 and 2060, even under favorable adoption scenarios, implying that the full labor‑substitution effects unfold over decades rather than having clearly arrived by 2025. (mckinsey.com)
• Several worker surveys report that AI often adds oversight and correction work and can increase workload, rather than simply letting one person replace multiple others outright, at least with current tools and governance. (news.com.au)

Why the outcome is ambiguous rather than clearly right or wrong

• On the “right” side: by 2025, AI is indeed widely deployed; many knowledge workers use it daily; some firms are automating entire processes; some executives explicitly see staff overcapacity from AI; and individual‑level studies show meaningful productivity gains that plausibly allow fewer staff to cover the same work in certain functions. These facts support Jason’s intuition that AI would start enabling labor arbitrage by 2025.
• On the “wrong” side: the prediction implied a broad, clear tipping point where “a lot” of labor arbitrage occurs across many organizations, with one worker routinely doing the work of several or large portions of lower‑skilled knowledge work being systematically replaced. Available evidence shows early and uneven adoption, modest but real task substitution, and mostly hybrid "human + AI" workflows, not a well‑documented, economy‑wide flip to large‑scale labor replacement in 2025.
• Crucially, terms like “tipping point,” “a lot of labor arbitrage,” and “many organizations” lack precise thresholds, and existing data does not cleanly map onto them. Different reasonable observers could look at the same mix of high adoption, early job restructuring, but still‑limited measured displacement and disagree on whether Jason’s bar has been met.

Because substantial evidence supports partial realization of his claim (especially in some sectors and early‑adopting firms), but there is no clear, quantitative demonstration that a broad 2025 tipping point in labor arbitrage has occurred, the prediction cannot be judged cleanly true or false. Hence the evaluation: ambiguous.