Recent Posts
News•Feb 25, 2026
Planning for AI Induced Economic Volatility
Enterprise adoption of large‑language models is moving from pilot projects in 2025 to production in 2026 and full‑scale deployment by 2027, enabling multi‑step, agentic workflows that can cut head‑office labor by up to 30 %. While individual firms gain margin improvements, the collective reduction in knowledge‑based jobs threatens downstream demand for ancillary products and services. Government monetary and fiscal tools are unlikely to offset AI‑driven job loss, forcing companies to build financial resilience. Executives must act now to secure liquidity, adopt flexible OpEx models, and develop mutual‑aid structures for displaced workers.
By AI Time Journal
News•Feb 25, 2026
Nithin Mohan — Why AI Breakthroughs Depend on Supercomputing Discipline
HPE’s AI and supercomputing leader Nithin Mohan argues that enterprise AI is now limited by infrastructure rather than algorithms. He highlights how exascale computing, high‑speed data movement, and system reliability are essential to move AI from demos to production. The conversation...
By AI Time Journal
News•Feb 24, 2026
Joey Gilkey — Rebuilding Outbound Sales Around Phone Intent and Predictive Precision
TitanX CEO Joey Gilkey argues that outbound sales must abandon volume‑driven dialing for an intent‑driven model powered by Phone Intent™ and AI. By aggregating behavioral signals, Phone Intent predicts which prospects will answer, raising connect rates from industry‑low 3% to...
By AI Time Journal
News•Feb 10, 2026
AI Talent Mobility and the Institutional Logic of EB-1A and NIW
AI’s rapid development cycles and cross‑border collaborations are reshaping how talent moves, but U.S. immigration categories—EB‑1A (extraordinary ability) and EB‑2 NIW (national interest waiver)—still rely on stable, publicly verifiable records. The article argues that the core tension lies between AI’s...
By AI Time Journal
News•Feb 9, 2026
Baran Ozkan — Building the Operating System for Financial Crime Compliance
Baran Ozkan, CEO of Flagright, argues that financial‑crime compliance must evolve from rule‑heavy checklists to an AI‑native operating system that delivers real‑time risk decisions. By giving compliance teams controllable, transparent workflows, Flagright reduced false‑positive alerts from 99.1% to 15.3% and...
By AI Time Journal
News•Feb 5, 2026
The On-Device AI Revolution: 4 Ways It’s Transforming Inference Technology
On‑device AI is shifting computation from cloud servers to edge devices, delivering privacy, instant responsiveness, offline capability, and better battery life. By embedding dedicated AI chips, smartphones, laptops, and IoT gadgets can process data locally, eliminating network latency and data...
By AI Time Journal
News•Feb 2, 2026
From Algorithms to Advisors, Learn How Professionals Are Navigating Money in the Age of AI
AI is reshaping financial decision‑making by moving from pure automation to augmentation, but human judgment remains essential. Professionals are pairing AI‑driven insights—such as scenario modeling and stress‑testing—with personalized advice from financial planners, especially in high‑cost regions like California. This hybrid...
By AI Time Journal
News•Jan 27, 2026
How Automated NLP Pipelines Cut Oncology Data Abstraction From Weeks to Hours
Cognizant’s senior data scientist Abhijit Nayak explains why transformer models that shine on curated oncology NLP benchmarks falter in clinical settings. He highlights that real‑world pathology reports and clinical notes are highly heterogeneous, demanding modular extraction pipelines with robust validation,...
By AI Time Journal
News•Jan 20, 2026
The AI Stack Is Breaking. Outcome Platforms Are Replacing It.
Enterprises are overwhelmed by a fragmented AI stack of multiple specialized tools, leading to integration overhead and AI fatigue. Outcome platforms, like Famous.ai, consolidate these functions into end‑to‑end workflows that deliver ready‑to‑use assets. This shift reduces tool sprawl, shortens development...
By AI Time Journal
News•Jan 8, 2026
Sayd Agzamkhodjaev: “Users Don’t Trust that the System Never Makes Mistakes; They Trust that It Can Safely Recover.”
In 2025 enterprises are rapidly scaling generative AI, with 72 % planning higher investment. Sayd Agzamkhodjaev, founding engineer at Treater, built a multi‑layer LLM evaluation pipeline that reduced errors by roughly 40 % through deterministic checks, an LLM‑as‑a‑Judge, and user‑feedback loops. He...
By AI Time Journal
News•Dec 30, 2025
From Turbines to Algorithms: The Engineer Bridging Global Energy Gaps
Artificial intelligence is becoming a cornerstone of modern energy grids, a shift highlighted by the International Energy Agency’s call for smarter, self‑optimizing systems. Engineer Petro Bondar leverages 15 years of hands‑on experience to create the SAEO (Smart Adaptive Energy Optimization) framework,...
By AI Time Journal
News•Dec 29, 2025
Ishu Anand Jaiswal, Senior Engineering Leader — Owning Outcomes, Customer-Facing Systems, Trust Over Speed, Scaling Systems, AI with Guardrails, Lasting...
Ishu Anand Jaiswal, a senior engineering leader with 18+ years experience, transitioned from building isolated components to owning full‑scale, customer‑facing systems at Apple and Intuit. He led the Smart Sign platform—25,000 global endpoints with 99.999% availability—and introduced adaptive caching and AI...
By AI Time Journal