Your AI Is Moving Back Onto the Machine
The future of AI inference is not cloud versus device. The shift is hierarchy: cloud for frontier work, devices for the everyday intelligence layer close to private context.
Independent research on enterprise AI strategy, governance, and operational risk.
The future of AI inference is not cloud versus device. The shift is hierarchy: cloud for frontier work, devices for the everyday intelligence layer close to private context.
Why governance frameworks built for generative AI fail for agentic systems.
Maintaining context and knowledge across AI system transitions and human-agent handoffs.
Moving past prompt engineering to systematic AI integration.
The small fraction of organizations getting real value from AI — and what they do differently.
Hidden costs of integrating AI into existing workflows and systems.
Top-down AI mandates vs. bottom-up adoption — which actually drives results.
Why organizational structure determines AI adoption outcomes.
How enterprises measure AI ROI and where standard metrics fail.
Quantifying the cost of AI hallucinations and mitigation strategies for production systems.
Why enterprise AI safety frameworks fail when agents act autonomously.
Live market data: 8,618 AI/ML engineering roles across 513 companies, $213k median, 599 new this week. OpenAI leads with 336 open roles.
Live audit: 5,578 agent-ready sites indexed, only 575 (10.3%) pass a live JSON-RPC handshake. Category breakdown, newly-verified servers, and the regulated verticals still waiting to be built.
Research roles pay a $42k premium over generative-AI roles ($274k vs $231k avg), even though generative-AI has 2.5x more openings. Top-paying skill tags, most in-demand tags, sweet-spot skills, salary distribution across 3,402 salary-disclosed roles.
Live workplace data: hybrid AI/ML roles pay a ~$35k premium over remote+onsite ($253k vs $218k). 55% of AI engineering still requires full onsite attendance. Onsite-heavy and remote-friendly companies, hybrid-premium analysis.
Live experience-level data: only ~7% of AI/ML engineering roles are open to juniors. For every entry-level role, ~10 senior-plus roles -- the tightest junior-to-senior ratio in tech. Why the squeeze exists, companies still hiring juniors, and the career-switcher playbook.
A researched white paper on why agentic businesses need self-testing, self-improving systems: artifact scoring, feedback loops, evaluator calibration, audit trails, and regression infrastructure.