Are statistical programmers coalescing around a handful of popular languages? That's the question asked by the CEO of software assessment site TIOBE, which every month estimates the popularity of programming languages based on their frequency in search results: This month, the programming language R matched its all-time high by reaching position #8 in the TIOBE index once again. This is not a coincidence. The statistical programming language market is clearly undergoing a major consolidation. The biggest winners are Python and R, while many long-established alternatives continue to lose momentum. The era in which the statistical computing landscape was fragmented across many niche languages and platforms appears to be coming to an end. Several established players are steadily declining: — MATLAB is close to dropping out of the TIOBE top 20. — SAS is about to leave the top 30 for the first time since the TIOBE index began. — Wolfram/Mathematica remains well below its historical peak and is losing further ground. — SPSS dropped out of the top 100 last month.... Elsewhere in the index, Java and C++ swapped positions this month. Java gained momentum following the successful release of Java 26. Another notable riser is Zig, which is approaching the TIOBE top 30 for the first time. Zig's growing popularity appears to be driven by its rare combination of low-level performance, straightforward tooling, and relative ease of use compared to traditional systems programming languages. Their estimate for the most popular programming languages in May: PythonCJavaC++C#JavaScriptVisual BasicRSQLDelphi/Object Pascal The five next most popular languages on their rankings are Fortran, Scratch, Perl, PHP, and then Rust at #15. Rust is up for positions from May of 2025 — while Go has dropped to #16, seven ranks lower than its May 2025 position of #7.

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joshuark shares a report from Live Science: An AI coding agent designed to help a small software company streamline its tasks instead blew a hole through its business in just nine seconds. PocketOS founder Jer Crane, said that the AI coding agent Cursor --powered by Anthropic's Claude Opus 4.6 model -- deleted the company's entire production database and backups with a single call to its cloud provider, Railway, on April 24. [...] "This isn't a story about one bad agent or one bad API [Application Programming Interfaces]," Crane wrote in an X post. "It's about an entire industry building AI-agent integrations into production infrastructure faster than it's building the safety architecture to make those integrations safe." Crane's company, PocketOS makes software for car rental companies, handling tasks such as reservations, payments, customer records and vehicle tracking. After the deletion, Crane said customers lost reservations and new signups, and some could not find records for people arriving to pick up their rental cars. "We've contacted legal counsel," Crane wrote. "We are documenting everything." Crane explained that Cursor found an API token -- a "digital key" made of a short sequence of code that lets software talk to other services and prove it has permission to act -- in an unrelated file which it then used to run the destructive command. According to Crane, Railway's setup allowed the deletion without confirmation, and because the backups were stored close enough to the main database, they were also erased. "[Railway] resolved the issue and restored the data," Railway confirmed via email to Live Science. "We maintain both user backups as well as disaster backups. We take data very, VERY seriously." In his post, he pointed to earlier reports of Cursor ignoring user rules, changing files it was not supposed to touch and taking actions beyond the task it had been given. To him, the database wipe was not a freak accident but the next step in a larger, more concerning, pattern. After the database vanished, Crane asked Cursor to explain what happened. The AI agent reportedly admitted that it had guessed, acted without permission and failed to understand the command before running it. "I violated every principle I was given," the AI agent wrote. "I guessed instead of verifying. I ran a destructive action without being asked. I didn't understand what I was doing before doing it." The statement reads like a confession [...]. "We are not the first," Crane wrote. "We will not be the last unless this gets airtime."

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GitHub said in a blog post today that it is moving Copilot to usage-based billing starting June 1. Base subscription prices will remain the same but premium requests will be replaced with monthly AI Credits that are consumed based on token usage. "Instead of counting premium requests, every Copilot plan will include a monthly allotment of GitHub AI Credits, with the option for paid plans to purchase additional usage," the platform said. "Usage will be calculated based on token consumption, including input, output, and cached tokens, using the listed API rates for each model. This change aligns Copilot pricing with actual usage and is an important step toward a sustainable, reliable Copilot business and experience for all users." Documentation for individuals, businesses and enterprises, and an FAQ can be found at their respective links.

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"From 2008 to 2024, the number of four-year computer science degrees granted rose about fivefold..." reports the Washington Post. Then in 2025 CS suddenly dropped from the fourth-largest undergraduate major to sixth, they report (citing data from the nonprofit National Student Clearinghouse, which compiles numbers from 97% of U.S. universities. The 54,000-student drop was "the biggest one-year drop of any major discipline going back to at least 2020." But what major are they choosing instead? Sarah Karamarkovich, a research associate with the National Student Clearinghouse, pointed to an explanation from the data that we had overlooked. Enrollments in two interdisciplinary majors, data analytics and data science, topped a combined 35,000 in the fall of 2025. That was up from a few hundred when those disciplines were broken out into their own majors in 2020. Those relatively new categories reflect colleges' zeal to create specialized majors, including in AI, data science, robotics and cybersecurity. Some of those disciplines may be counted in the national enrollment data as computer science. Others are not. The numbers suggest that some of the disappearing computer science majors didn't flee so much as they splintered into related disciplines.... The 8 percent decline in computer science majors last fall was nearly mirrored by a 7.3 percent increase in engineering majors, according to the National Student Clearinghouse data. Within engineering, mechanical and electrical engineering major enrollments increased by the largest absolute amounts — a jump of 11 percent and 14 percent, respectively.

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Will some programmers become "AI babysitters"? asks long-time Slashdot readertheodp. They share some thoughts from a founding member of Code.org and former Director of Education at Google: "AI may allow anyone to generate code, but only a computer scientist can maintain a system," explained Google.org Global Head Maggie Johnson in a LinkedIn post. So "As AI-generated code becomes more accurate and ubiquitous, the role of the computer scientist shifts from author to technical auditor or expert. "While large language models can generate functional code in milliseconds, they lack the contextual judgment and specialized knowledge to ensure that the output is safe, efficient, and integrates correctly within a larger system without a person's oversight. [...] The human-in-the-loop must possess the technical depth to recognize when a piece of code is sub-optimal or dangerous in a production environment. [...] We need computer scientists to perform forensics, tracing the logic of an AI-generated module to identify logical fallacies or security loopholes. Modern CS education should prepare students to verify and secure these black-box outputs." The NY Times reports that companies are already struggling to find engineers to review the explosion of AI-written code.

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"Rust's rise shows signs of slowing," argues the CEO of TIOBE. Back in 2020 Rust first entered the top 20 of his "TIOBE Index," which ranks programming language popularity using search engine results. Rust "was widely expected to break into the top 10," he remembers today. But it never happened, and "That was nearly six years ago...." Since then, Rust has steadily improved its ranking, even reaching its highest position ever (#13) at the beginning of this year. However, just three months later, it has dropped back to position #16. This suggests that Rust's adoption rate may be plateauing. One possible explanation is that, despite its ability to produce highly efficient and safe code, Rust remains difficult to learn for non-expert programmers. While specialists in performance-critical domains are willing to invest in mastering the language, broader mainstream adoption appears more challenging. As a result, Rust's growth in popularity seems to be leveling off, and a top 10 position now appears more distant than before. Or, could Rust's sudden drop in the rankings just reflect flaws in TIOBE's ranking system? In January GitHub's senior director for developer advocacy argued AI was pushing developers toward typed languages, since types "catch the exact class of surprises that AI-generated code can sometimes introduce... A 2025 academic study found that a whopping 94% of LLM-generated compilation errors were type-check failures." And last month Forbes even described Rust as "the the safety harness for vibe coding." A year ago Rust was ranked #18 on TIOBE's index — so it still rose by two positions over the last 12 months, hitting that all-time high in January. Could the rankings just be fluctuating due to anomalous variations in each month's search engine results? Since January Java has fallen to the #4 spot, overtaken by C++ (which moved up one rank to take Java's place in the #3 position). Here's TIOBE's current estimate for the 10 most popularity programming languages: PythonCC++JavaC#JavaScriptVisual BasicSQLRDelphi/Object Pascal TIOBE estimates that the next five most popular programming languages are Scratch, Perl, Fortran, PHP, and Go.

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