Heavy AI Adopters Show Higher Hiring Rates, Ramp Data Finds

·1 views

New data published by financial technology company Ramp suggests that businesses which heavily adopt AI tools tend to hire more employees, not fewer. The analysis challenges the common concern that artificial intelligence leads to widespread job displacement. Companies with greater AI usage appear to expand their workforces at a higher rate than those with lower adoption. The findings are based on spending and business activity data that Ramp collects through its corporate finance platform.

Read the full story at Hacker News

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Developer Releases Four Papers on Making AI-Assisted Work Independently Verifiable

A developer has published four short papers centered on a single concept: ensuring that AI-assisted work can be reliably re-checked. The papers introduce tools including EMET, a byte-level integrity checker; BuildLang, a compiler that seals verifiable receipts; and Witnessed Independence, a mechanism that tracks whether a verifier assessed its own output. A fourth concept, Proof Packets, wraps individual agent actions so that verdicts are always derived from external checks rather than self-reported claims. All source code and conformance tests for the four implementations are publicly available.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why Evaluating AI Models Is Far More Complex Than Testing Traditional Software

A deep dive into Chapter 3 of 'AI Engineering' highlights that evaluating AI systems is as challenging as building them, unlike traditional software where pass-or-fail tests are straightforward. AI models can produce multiple plausible answers to the same question, making it difficult to determine which response is genuinely better. Benchmarks like GLUE, introduced in 2018, became obsolete within a year as models surpassed them, prompting the creation of harder alternatives like SuperGLUE — illustrating how evaluation standards must constantly evolve. Metrics such as perplexity, cross entropy, and bits-per-byte offer ways to measure how well a model predicts text, but these figures are only meaningful when interpreted within the context of a specific dataset. The chapter also draws a distinction between exact evaluation, suited to tasks with definitive answers like math problems, and subjective evaluation, required for assessing qualities like helpfulness or creativity.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Claude vs ChatGPT for Coding: Strengths, Weaknesses, and Best Use Cases

A hands-on comparison of Claude and ChatGPT finds both are capable coding assistants but excel in different scenarios. Claude is better suited for complex, context-heavy tasks such as code review, debugging multi-step failures, and refactoring large codebases, as it reasons more carefully and preserves existing code patterns. ChatGPT, meanwhile, performs well for quick snippets, common framework tasks, and mixed workflows that span coding, documentation, and product writing. For code review specifically, Claude tends to give more precise, actionable feedback rather than generic compliments, while ChatGPT often requires a narrowed prompt to stay focused on critical issues. Ultimately, experienced developers working on specific engineering tasks may prefer Claude, while those seeking a versatile, all-purpose assistant may find ChatGPT more convenient.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

AI Agents Can Build Products Faster, But Finding Users Remains a Human Problem

AI coding agents have become significantly more capable, able to read repositories, run tests, browse documentation, and operate across connected tools via standards like MCP. This has removed much of the manual context-sharing that slowed developers down, allowing faster prototyping and shipping. However, the speed gains have made a deeper bottleneck more visible: finding the right people who actually need what has been built. Early-stage builders still rely on manual outreach through social media, forums, and personal networks to locate beta users and design partners. The article argues that demand discovery requires precise, targeted matching rather than broad public announcements, and that this remains a challenge AI tools have yet to meaningfully solve.

0 comments Read more at DEV Community