Newsletters
Two years ago, much of the public treated generative AI as an impressive but unreliable novelty. In 2026, that framing no longer holds. The technology has advanced rapidly enough to unsettle white-collar industries, attract infrastructure spending, and spark a broader debate about who benefits, who is exposed, and who writes the rules.
The question is no longer whether AI matters. It is what kind of force it is becoming.
For most of the last decade, artificial intelligence advanced at a manageable pace. This changed rapidly between 2022 and 2024, as large language models progressed from basic arithmetic to passing bar exams and developing functional software. Global AI investment reached $252.3 billion in 2024, with U.S. private investment rising over 75% year over year, according to Stanford University's AI Index 2025 report.
By late 2025, leading software engineers reported delegating most coding tasks to AI systems. METR, an organization that tracks real-world AI performance, found that models capable of performing 10-minute tasks in early 2024 could complete nearly 5 hours of expert work by November 2025. This capability has roughly doubled every 7 months.
On February 5, two major AI labs simultaneously released flagship models. Tech founders and industry observers describe this as a qualitative shift, with AI systems now demonstrating judgment rather than just pattern recognition. A widely read essay by OthersideAI founder Matt Shumer, viewed 80 million times, warned that white-collar workers are "next" after significant disruption among tech workers.
Anthropic CEO Dario Amodei predicted that AI could eliminate 50% of entry-level white-collar jobs within 1 to 5 years. Microsoft AI chief Mustafa Suleyman stated that most tasks performed by lawyers, accountants, and marketing professionals "will be fully automated by an AI within the next 12 to 18 months."
Research published by Anthropic in March 2026 found that occupations with higher AI exposure are projected by the U.S. Bureau of Labor Statistics to grow less through 2034. Customer service representatives, data entry workers, financial analysts, and computer programmers rank among the most exposed,and notably, these workers tend to be better educated and higher paid than average, reversing the pattern seen in earlier automation waves.
The IMF estimates 40% of global employment is exposed to AI-driven change. Unlike factory automation, AI improves across all domains simultaneously, leaving no obvious sector to retrain into.
Enterprise adoption is shifting from pilot projects to rigorous evaluation of returns. HSBC reports detecting two to four times more financial crimes using AI while reducing false positives by 60%. However, analysts note that technology accounts for only 30% of successful AI implementation; people and processes account for the remaining 70%.
The political response is accelerating. In the U.S., AI-related job displacement is emerging as a midterm campaign issue. A December 2025 executive order created a DOJ AI Litigation Task Force, directed agencies to withhold funding from states with regulations deemed hostile to AI, and introduced federal legislation to preempt conflicting state laws.
This reflects the administration's concern that fragmented regulation could hinder a strategic industry. Congress faces pressure to act by summer 2026. At the same time, the focus is shifting from scaling to economic challenges. Deutsche Bank projects data center spending could reach $4 trillion by 2030, but energy constraints and uncertain returns are making the AI infrastructure race more selective.
Türkiye's National AI Strategy for 2021-2025 set ambitious goals, but the country currently ranks 48th out of 195 nations on the Oxford Insights Government AI Readiness Index, falling short of its target to reach the top 20. Asst. Prof. Dr. Hüsrev Kastaci (PhD), an AI researcher, patent developer, and consultant, emphasized the stakes: "If we manage this inevitable process correctly, a very good future awaits our country and new generations. If we cannot manage it, very difficult days await us as a society."
Kastacı, who works on AI-based certification and testing systems, fields he describes as technically demanding and legally regulated, where human judgment remains essential, said the pace of change has already exceeded expectations, even in these protected areas. He noted that a compliance analysis, once requiring a specialist team for two or three days, can now be completed, sourced, and argued in hours.
"The white-collar risk is real, but Türkiye's economic structure buys some time," Kastaci said, citing the country's SME-heavy economy and preference for face-to-face business as a short-term buffer. "But there is nothing to guarantee that the buffer will not erode in the medium term."
Technology Management PhD researcher and lecturer Akif Emrah Buyuksomer identified the lack of a domestic regulatory framework as Türkiye's most urgent structural gap and its most overlooked opportunity. "Whoever contributes to shaping this infrastructure will be building a long-term position in the sector," Buyuksomer said.
He highlighted brain drain, with IT graduate emigration rates reaching 6.7%, and dependence on foreign cloud infrastructure as compounding risks. However, he noted that Türkiye's defense AI achievements, such as the Bayraktar TB2 and KIZILELMA autonomous drone, demonstrate the country's ability to innovate at the frontier. "The opportunity window is still open," Buyuksomer said. "But it is narrow."
Türkiye has a young population, an R&D base, and visible strengths in defense technology, yet it also faces dependence on foreign computing, pressure to align with the EU AI Act, a major skills challenge, and a risk of brain drain. Kastaci warns that “panic is not needed, but practical urgency is,” while Buyuksomer argues that the country is “not ready enough” but still has time to act. In 2026, that may be Türkiye’s real AI question: not whether the transformation is coming, but how much of it the country will shape for itself.
That may be the clearest answer to what is happening in AI in 2026. The field is not slowing into irrelevance, and it is not yet delivering the total white-collar collapse imagined in some viral essays. Instead, it is entering a harder phase in which markets want proof, companies want results, regulators want leverage, and workers want clarity.
The next stage of AI will likely be defined less by dazzling demos than by whether systems become dependable, cheap, and governable enough to reshape daily work at scale.
Set as preferred source
Set as preferred source
3 min read
