For months, the AI revolution felt like a story of hype versus reality. While tech leaders promised transformative productivity gains, hard data remained stubbornly silent. But new evidence suggests we’ve crossed a threshold – and the consequences are rippling through global markets with startling speed.
The Coding Productivity Explosion
According to analysis from the Financial Times, three key indicators now show clear inflection points coinciding with the launch of agentic coding tools like Anthropic’s Claude Code and OpenAI’s Codex. By the third quarter of 2025, coding activity on GitHub in the US was 30% higher than if it had continued on its pre-2025 trend. More tellingly, iOS app releases jumped 55% year-over-year in January 2026, while global website registrations surged 34% after years of stability.
“I think there are two distinct questions here,” notes Sarah in the FT analysis. “Are we now seeing a substantial uptick in productivity in software development thanks to agentic AI tools? The answer seems to be yes.” But she adds crucial context: “Quantity metrics don’t tell us anything about quality.”
From Developer Tools to Market Disruption
The implications extend far beyond coding environments. Anthropic’s Claude Cowork platform, designed to bring similar capabilities to non-coders, has already demonstrated its disruptive potential. As Boris Cherny, creator of Claude Code, noted on X: “Pretty much 100% of our code is written by Claude Code… I think most of the industry will see similar stats in the coming months.”
But what happens when these tools target established industries? The answer came swiftly in financial markets. Following Anthropic’s announcement of AI tools automating legal work and compliance workflows, billions were wiped off the market value of media and financial data companies. Relx, owner of LexisNexis, saw its share price drop 15%, while Thomson Reuters lost nearly 15% of its value in a single day.
Global Market Tremors
The selloff wasn’t isolated. According to The Guardian’s business live coverage, software stocks slid globally following losses on Wall Street. Relx plunged 14% in London, while Salesforce, Datadog, and Adobe each lost about 7% in New York trading. The pattern repeated across Asia, with Tata Consultancy Services down 6.8% and Infosys losing more than 8% in India.
Ipek Ozkardeskaya, senior analyst at Swissquote, captured the market sentiment: “The announcement spooked markets, triggering a sharp selloff in software companies that sell data analytics and decision-making tools to lawyers, banks and corporates, on fears that AI and new players are coming for their lunch – and at an accelerated pace.”
The Quality Question and Adoption Challenges
Despite the impressive productivity metrics, questions remain about output quality. Some critics argue that AI-generated code represents a new form of ‘slop’ – rapidly produced but potentially flawed work. However, the strongest counterargument comes from Anthropic’s own operations: Claude Cowork itself was coded entirely by Claude Code.
Adoption barriers also loom large. As Sarah notes in the FT analysis, “It’s worth taking a breath before extrapolating those software development productivity metrics to the economy at large… adoption will also be slower and more cautious in companies outside of tech for security and other reasons.”
The Broader Productivity Picture
Looking beyond software, evidence of AI’s productivity impact remains mixed. A Goldman Sachs compilation suggests an average productivity boost of 32% from AI adoption, while a Federal Reserve Bank of St. Louis study found industries where workers saved most time using AI saw unusually fast labor productivity growth.
Yet caution persists. As John observes in the FT analysis, “White-collar jobs are much larger and messier bundles of tasks, and even though Claude Code may have written all the code for Claude Cowork, it still needed to be told what to do by human engineers.”
What Comes Next?
The current moment represents a critical inflection point. We’ve moved from theoretical discussions about AI’s potential to measurable impacts on productivity and market valuations. But as with any technological shift, the full implications will unfold gradually.
For businesses, the challenge is clear: adapt or risk disruption. For workers, particularly in knowledge-intensive fields, the message is equally stark: understanding and leveraging these tools may become essential rather than optional. The AI revolution has moved from promise to measurable impact – and the markets are voting with their dollars.