Articles
- AI Stopped Being a Feature. It Became the Plumbing.
AI's integration into core workflows as infrastructure rather than standalone features necessitates a shift in product roadmaps towards data layers and consolidation, impacting speed and strategic acquisition decisions.
- Your Next Inference Bill Might Not Go to the Cloud
AI models are now efficient enough to run locally on devices, offering cost savings and enhanced privacy, prompting a reassessment of cloud dependency for inference tasks.
- Your AI Feature Runs on a Grid That's Running Out of Room
Rising energy costs and grid constraints are increasing the operational expenses of AI features, requiring product leaders to reassess the economic viability and sustainability of their AI-driven offerings.
- Your AI Just Banned 8,000 People Who Did Nothing Wrong
AI moderation's missteps highlight the critical need for robust human oversight and rapid correction mechanisms to prevent customer alienation and maintain trust in automated systems.
- The chatbot is describing your brand from Reddit, not your homepage
AI-driven chatbots are shaping brand narratives based on external sources like Reddit and Wikipedia, prompting companies to prioritize generative-engine optimization to ensure accurate representation and maintain brand integrity.
- AI Made the Making Cheap. Now Sell the Deciding.
As AI reduces the cost of production, the value of human judgment in deciding what to create and ensuring its effectiveness becomes crucial, requiring design leaders to shift focus from production to strategic decision-making.
- Ad Hoc Prompting Is Now the Slow Way to Work
Structured AI workflows with harnesses, plugins, and retrieval-augmented generation are replacing ad hoc prompting, enabling scalable, auditable, and efficient solutions that enhance team collaboration and product reliability.
- Four models dropped this week. Your default just got expensive.
The release of new AI models like Grok 4.5 and GPT-5.6 offers opportunities to reduce costs and improve performance, but requires careful evaluation to ensure quality and cost-effectiveness for specific tasks.
- Your AI Feature Ships Fast. Your Quality Loop Is Now the Job.
AI feature deployment demands a shift from traditional quality checks to continuous evaluation of outputs, emphasizing human judgment and iterative improvements to maintain user satisfaction and product integrity.
- Microsoft Is Building Its Own Models to Duck the AI Bill. Here's What That Means for You.
Microsoft's shift to in-house AI models highlights the need for companies to strategically balance cost and functionality by using premium models for innovation and cheaper alternatives for routine tasks, mitigating financial and operational risks.
- The Frontier Model Is No Longer Your Default
Shifting from large, general AI models to specialized, fine-tuned small models can significantly reduce costs and improve efficiency, especially for narrow tasks, while enhancing privacy and compliance for sensitive data handling.
- The Unicorn Class of 2026 Wants to Do Your Job, Not Sell You a Tool
The rise of outcome-based pricing models among AI-driven unicorn startups challenges traditional product strategies, urging teams to focus on delivering measurable results and rapid revenue generation rather than just providing tools.
- AI Made Design Faster; Now Prove It Made Design Better
AI's ability to rapidly generate design outputs shifts the focus from creation to evaluation, necessitating deliberate review processes to ensure quality and alignment with business goals, rather than just speed.
- The creative AI tools got fast enough to ship. Now what?
Recent advancements in AI tools, including partnerships and improved processing speeds, enable studios and product teams to shape and deploy faster, more reliable creative workflows, impacting timelines and enhancing user experiences.
- The Users You Never Booked for Research
Designing products with the needs of users juggling care responsibilities in mind can unlock new markets, as demonstrated by recent investments in AI tools for households and insights into creative mothers' challenges.
- Your Team's Real AI Question Isn't Which Tool, It's How They Work
AI-driven coding agents are reshaping product development by drastically reducing build times, prompting leaders to reconsider decision-making processes and focus on strategic judgment rather than routine tasks.
- Cheap Pixels Are Here. Now Decide What They're For.
Cheap image and video generators just landed. Ray on which AI tools belong in your workflow versus your product, and how to tell them apart.
- AI Does What You Meant, Not What You Wanted
AI in product management helps clear workflow friction, but it won't fix a broken team. Here's where it earns its keep and where it hides the rot.
- Your Agent's First Reply Is a Retention Decision
Default prompts, agent tone, and eval scores predict AI feature retention. Here's what the data shows and what to fix on your team first.
- Your AI Product Doesn't Behave Like Your Old One. Your Metrics Should Change Too.
AI product OKRs should measure what users do, not model accuracy. Here's how to write behavior-based key results and version-control your PM workflow.
