Andrew Newton

If you're not using AI for product work in 2025, we need to talk 👀 Here's the thing - most PMs are using AI wrong. They're treating it like a fancy Google instead of actually integrating it into their workflow. What if AI could handle the tedious parts of product development so you can focus on the strategy and decisions that actually matter? Here's exactly how AI can handle your entire feature lifecycle: 1️⃣ Research phase → Perplexity for market analysis and competitor research. 2️⃣ User insights → Dovetail to analyze user feedback and spot patterns I would've missed manually. 3️⃣ Ideation → Miro + AI for brainstorming sessions that actually generate useful ideas instead of sticky note chaos. 4️⃣ Design mockups → Uizard by Miro Labs turns my terrible sketches into actual UI designs. 5️⃣ User testing → Maze to validate concepts before we build anything stupid. 6️⃣ Documentation → Notion AI writes my PRDs while I focus on the strategy stuff. 7️⃣ Development → Cursor and GitHub Copilot handle the coding. My developers are obsessed. 8️⃣ Analytics setup → Amplitude creates tracking events I didn't even know I needed. 9️⃣ User research → Typeform generates survey questions that actually get responses. ______________ Is this the future of product management? Probably. Am I here for it? Absolutely ⚡ What AI tools are you using in your product workflow?

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iOS users cost 4x more to acquire. They also generate 68% more repeat purchases. The math isn't what you think. AppsFlyer's new eCommerce report analyzed $4.2B in ad spend. It confirmed that iOS acquisition costs are brutal. But those users convert 39% faster than Android users. And they reach their second purchase 1.3 days sooner. What changes everything is how iOS users aren't just buying more. They're buying habitually. The report shows iOS loyalty rates hit 4.7% versus Android's 2.8%. That 68% gap sure compounds over time. I've watched brands resist iOS investment for months because the Android CPI is one quarter the cost. Makes sense on paper. Six months later, the iOS cohort generates 2.4x the revenue per user. The "expensive" channel becomes the profit center. Keep in mind the psychology is different... iOS users expect premium experiences. And when you deliver, they reward you with loyalty Android rarely matches. All this data from AppFlyer's report is clearly telling us: Don't optimize for install costs, rather optimize for second purchase velocity. The premium platforms charge premium prices for premium customers. Sometimes the math is that simple. 👉 See the full data that is reshaping how smart brands think about platform strategy: https://shorturl.at/qi3sV

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Product management for AI agents is easily the wildest form of product management in history. Typical product management is trying to figure out how to design interfaces and software for people to interact with deterministic systems. The user generally knows all the context to do their work successfully, so it’s generally a matter of nailing the underlying business logic and surrounding UX. But with AI agents, the user you care about most is the agent, and they don’t know anything by default. They’ll happily run in any direction to perform the task, often without success. So as a PM (or engineer) you basically spend your time trying to reverse engineer “what would a human need as context to perform this task”, and then figure out how to design systems to get the agent that data in the right sequence, with the right tools, and instructions. Some of these systems are entire invisible to the human user, but part of the craft is equally how the end-user will interact with the agent to supply this context. Then, it’s often unending trial and error working to eke out incremental points of quality at each stage. This is especially why people with deep domain expertise, or those that can acquire it quickly, will do extremely well building AI agents. The ability to anticipate the context that the agent would need to be successful is a huge determining factor in how effective the agent will be. This partly explains why coding agents have worked so well out of the gate; because its builders deeply understand the domain that they’re working to automate. But clearly we’re going to quickly see this same outcome across every field - legal, healthcare, finance, etc. - as context engineering and a new crop of product managers emerge.

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Most PMs are stuck at the bottom of the AI adoption curve. But look at what happens when you move up. 🙅Here’s how most PMs stuck at the bottom use AI: • Write emails • Summarize documents • AI-powered search ✅ Here’s how Top 1% PMs use AI: • Write requirements • Create personas • Analyze customer feedback What matters is treating AI literacy as part of the job. When you shift from "AI Novice” to “Top 1%" - that's when you hit the hockey stick. That's when 1-2 hours saved becomes 10-15 hours saved per week.

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