Before buying an AI tool, ask these 12 questions
AI procurement is not just a feature comparison. Test the workflow, data handling, real cost, vendor evidence and exit plan before the team depends on it.
AI agents are taking on real work. Who should sign off?
How teams can define human accountability, approval boundaries and escalation before AI agents act.
Why smaller AI models can be better products
When on-device AI can improve privacy, latency and cost—and what teams give up in capability.
What AI regulation means for product teams in 2027
A practical look at changing AI rules, product obligations and the questions teams should track.
Humanoid robots are reaching factories. The real test is reliability
Why real-world reliability, safety and useful task performance matter more than impressive demos.
AI benchmarks are useful. They are not the whole story
How benchmark contamination and narrow tests can make a single score misleading.
AI pricing is changing because software usage is changing
What usage-based AI pricing means for budgeting, predictability and vendor comparisons.
Who is responsible when an AI system causes harm?
Responsibility can depend on the product, deployment, contract and jurisdiction.
The best warehouse robot may be the one that knows when to ask for help
Reliable automation needs a safe way to recognise uncertainty and ask for human help.
Open weights, open source and AI licenses are not the same thing
Weights, source code and licenses can grant very different rights. Here is how to check.
AI data centers are becoming a power-and-real-estate problem
AI infrastructure depends on power, cooling, networking and suitable sites.
How schools can build practical AI rules instead of blanket bans
Practical school policies can define acceptable assistance, disclosure and independent work.
Drone delivery has solved part of the journey. The last 100 meters are different
The final delivery handoff introduces people, obstacles, regulation and uncertainty.
Before buying an AI tool, ask these 12 questions
A grounded procurement checklist for capability, privacy, costs, vendor evidence and exit plans.
When AI fails, teams need an incident plan—not a blame game
How to detect, contain, investigate and learn from AI incidents.
AI search citations are useful. They are not proof.
How to verify claims when an answer links to sources that may only partly support it.
RAG vs fine-tuning: which approach does your AI product need?
A practical guide to retrieval-augmented generation and fine-tuning, including costs, updates and evaluation.
An AI data privacy checklist for product teams
A framework for deciding what data an AI feature can use, how it is retained and what users should know.
How to evaluate an AI feature before launch
Build a realistic evaluation set, measure failure severity and decide what evidence is enough for release.
AI customer support needs a reliable human handoff
How support teams can use AI without trapping customers in loops or obscuring accountability.
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.