EU AI Act Transparency Rules: What Product Teams Should Check
Article 50 transparency rules are now applying in the EU. This practical guide explains which AI interactions, synthetic media and public-interest text need disclosure checks.
EU AI Act Transparency Rules: What Product Teams Should Check
Article 50 transparency rules are now applying in the EU. This practical guide explains which AI interactions, synthetic media and public-interest text need disclosure checks.
AI Shopping Agents: A Readiness Checklist for Retailers
AI shopping agents are moving toward product discovery and checkout. Learn how retailers should prepare product data, pricing, authorisation, customer support and measurement.
AI Model Deprecation: How to Migrate Without Breaking Production
AI APIs change and models retire. Build a migration plan with a model inventory, representative evaluations, canary releases, rollback controls and clear ownership.
AI Latency: The Metrics That Make Products Feel Faster
Measure AI response speed with time to first token, completion time, tail latency and end-to-end task duration. Learn which optimisations help without damaging quality.
AI Assistant Memory: Design for Privacy, Accuracy and Control
AI assistant memory can make products more useful, but it also creates risks from stale, sensitive or wrongly attributed facts. Learn how to design controls and retention rules.
India’s AI regulation debate: what product teams should prepare for now
India’s AI policy discussion is evolving. Product teams can prepare with practical governance, documentation and incident controls.
Google’s Gemini agent for work: what businesses should verify before rollout
What buyers should test for permissions, privacy, auditability and measurable value before deploying workplace agents.
AI voice generation in Indian languages: consent matters as much as realism
Evaluate consent, disclosure, language quality and misuse response before launching synthetic voice features.
AI agents need an emergency stop: how teams should plan for failure
A practical guide to pausing actions, revoking access, preserving evidence and recovering safely.
How to run an enterprise AI agent pilot that proves real value
Measure real task outcomes, review effort, costs and failure severity before expanding an AI agent pilot.
A practical AI privacy checklist for Indian product teams
Map AI data flows, minimise personal data, review vendors and build privacy checks into product delivery.
AI agents need fewer permissions than you think
Use least privilege, scoped credentials, approval gates and audit trails to keep tool-using agents under control.
How to measure AI energy use without misleading comparisons
Understand workload boundaries, hardware, utilisation and useful output before comparing AI energy claims.
On-device AI or cloud AI? A practical product decision guide
Compare privacy, latency, cost, capability and reliability before choosing an AI architecture.
How to evaluate AI in Indian languages beyond a demo
Evaluate code-switching, mixed scripts, regional context, safety and real task success across Indian languages.
When AI fails, teams need an incident plan - not a blame game
A practical AI incident response playbook covering detection, containment, evidence, communication, recovery and lessons learned for AI-enabled products.
Before buying an AI tool, ask these 12 questions
A practical AI procurement checklist for product, security and business teams: evaluate real tasks, data handling, reliability, vendor evidence, costs and exit options.
AI search citations are useful. They are not proof.
How to verify AI search answers when citations may support only part of a claim. A practical guide to source checking, evidence quality and uncertainty.
AI data centers are becoming a power-and-real-estate problem
AI infrastructure depends on electricity, grid connections, cooling, networking and suitable sites. The technology build-out is increasingly an infrastructure story.
How schools can build practical AI rules instead of blanket bans
Schools need practical AI policies that explain permitted assistance, disclosure, privacy, assessment and independent work instead of relying only on blanket bans.
The Ninth Post pulse
A practical snapshot of the ideas shaping the stories we cover.
- 1Accountability becomes part of AI product design
- 2AI infrastructure meets the power grid
- 3Smaller models expand where AI can run
- 4Better evaluation matters more than one score
- 5Robots learn where human help belongs
Signal over noise.
Technology headlines move fast. Our job is to explain what changed, why it matters, what the evidence says and what remains uncertain.
- Primary sources before hype
- Practical context, not recycled announcements
- Clear corrections and editorial standards
Satya Prasad
Satya Prasad is the founder of Ninth Post, an independent publication focused on artificial intelligence, emerging technology, product thinking and the future of work.