Why Does AI Tech Look so Bad?
AI‑driven products often appear unfinished because the underlying stack, incentives, and rapid‑release cycles prioritize model performance over user experience. Six core reasons—engineer‑centric development, probabilistic interaction models, raw developer scaffolding, undefined value units, speed‑driven iteration, and honest‑looking interfaces—explain the visual and functional roughness. Deeper structural issues such as non‑composable design, compounded iteration mess, and subjective feedback further hinder polish. As AI reliability improves and conventions solidify, the UI will evolve from a cockpit‑style wrapper to a seamless, consumer‑grade experience.
Will AI Replace Mainframe Systems?
Enterprises are eyeing AI to retire legacy COBOL and PL/I mainframes, but full replacement remains unrealistic. The prevailing strategy is modernization, leveraging generative AI tools such as IBM WatsonX Code Assistant and GitHub Copilot to translate and test code at...
Tokenmaxxing: When Compute Activity Masquerades as Productivity
The article warns that many firms are treating raw token counts as a proxy for AI productivity, a practice the author dubs “tokenmaxxing.” By tracking every token generated by large language models, companies create a vanity metric that rewards higher...
The Human Advantage in the Age of Generative AI
A new study comparing Stable Diffusion, GPT‑4o and human creators finds that AI trails humans in visual creativity unless steered by human ideas. When humans provide prompts, AI approaches the output quality of non‑expert creators, but alone it performs worst....
Native-AI Companies Need a New Approach to Governance
Native‑AI firms are confronting a governance gap as their models continuously learn and adapt, rendering static, post‑deployment checks obsolete. The article argues that governance must be woven into the product stack—real‑time observability, data provenance, and runtime guardrails become essential. It...
RSAC 2026: Reflections on a Security Show That Became an AI Showcase
RSA 2026 turned into an AI‑centric event, with security serving as the backdrop. Attendees heard concrete announcements on runtime guardrails, red‑team testing, and lifecycle controls, signaling that AI security is moving from theory to operation. Vendors touted unified integrations of...
The White House AI Framework: Growth Engine, Guardrails, and Contradictions
The White House released a National AI Framework that balances rapid innovation with targeted safeguards, avoiding a new centralized regulator. It emphasizes sector‑specific oversight, child protection, and treats AI as essential economic infrastructure, including data‑center expansion and small‑business grants. The...
The White House AI Framework: Growth Engine, Guardrails, and Contradictions
The White House released a National AI Framework that seeks to boost U.S. innovation while imposing targeted safeguards. The plan relies on sector‑specific oversight, leveraging existing agencies rather than creating new regulators. It positions AI as a growth engine for...
Secure by Default: Why Security That Assumes Failure Is Winning
At RSA 2024, the cybersecurity community is pivoting from the long‑standing "secure by design" mantra to a more pragmatic "secure by default" approach. The new model assumes misconfigurations, rushed deployments and human error, building safeguards that work even when users...