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My favorite weekly finds

🛠️ Tools

• CostGPT estimates software development cost, features, and time

• With Brainnote, a powerful AI voice-to-text to summarize your ideas in seconds

• Equals: A spreadsheet app designed for startups with AI tools.

• Lookie Summarizes YouTube videos and highlights key points.

• Save hours per week in PRD creation with a Free PDR Generator (beta)

• HeyGen: Create marketing and sales videos quickly.

📰 News

• Ben Goertzel predicts human-level AGI by 2029, with artificial superintelligence following shortly after.

• Check out this WILD launch video for ‘Pika Labs’ new “Pikaffects” tool

• Meet "Artificial Overthinking," an AI with anxiety.

👀 ICYMI

• How to be more strategic (Link)

• The risk of ‘just executing’ as a Product Leader (Link)

• Unpacking Data Products in the AI Age (Link)

8 Key Points for Designing an AI Product

Why should I care?

Hey Product Leaders!

As a product manager navigating the AI buzz and landscape, you’re under pressure to innovate and stay ahead of the competition, but without a clear strategy, AI can quickly become more of a liability than an asset. This article cuts through the hype, offering actionable insights to help you build AI products that solve real problems, drive value, and avoid common pitfalls.

Now what?

AI, Generative AI, GPT, and Machine Learning—these terms aren’t just circulating in our offices anymore; they’re now headline news, transforming industries and sparking conversations everywhere.

But for us in product management, AI isn’t new. We’ve been at it for years, whether developing AI products from scratch or integrating AI into existing ones.

From reducing repetitive tasks with Natural Language Processing (NLP) to building a comprehensive suite of AI tools for product managers, we’ve learned a lot along the way.

And if you’re working on an AI product now, you’ll want to keep these eight considerations in mind.

Let’s dive in.

1. Don’t Chase the Shiny Objects

When you’re working with AI, it’s easy to get distracted by the latest trends. But as with any product, what matters most is solving real user problems. Flashy AI features might grab attention, but they won’t build lasting user engagement or drive revenue.

Start with understanding your users' needs. Always.

AI’s sweet spot is often in making mundane tasks easier—not necessarily the glamorous stuff. So, while marketing pressures might push you towards the buzzwords, stick to creating features that deliver value.

Gimmicks fade, but real solutions last.

2. Find the Sweet Spot in the “Magic Valley”

In AI, you need balance.

Too much “magic” can make users uncomfortable—think of the uncanny valley, where AI feels a little too human and starts to creep people out.

But too little magic, and it feels like just another tool. You want AI to help your users get things done efficiently without overwhelming them with complexity.

Test and refine your product so the AI feels intuitive, not intrusive.

Users don’t want to fiddle with endless settings; they just want it to work. Keep the focus on delivering results in the easiest way possible.

3. Manage Expectations

It’s easy to get swept up in the potential of AI, but be careful not to overpromise. AI can do a lot, but it’s not magic. You need to be clear with your users about what AI can and can’t do. Eg AI tools that generate content often get you 60-70% of the way there—you’ll still need to edit the results. But that’s still a huge value!

Be transparent about these limitations, and your users will appreciate the honesty. It’ll prevent disappointment and build trust in your product.

Tools like Jasper and Copy.ai claim to generate marketing copy in seconds, but in reality, the results often need heavy editing. Is this what users really want when looking for AI-powered writing assistance?

4. Be Prepared for Ethical and Legal Challenges

When AI is involved, ethical and legal considerations are always around the corner. From data privacy issues to biases in algorithms, you need to stay ahead of regulations.

Different regions have varying standards—especially in places like the EU—so make sure your product aligns with the latest legal requirements.

And don’t forget about data security. After a major incident where sensitive information was fed into ChatGPT, many enterprises have become wary. Make sure you’ve got clear documentation to reassure users about how their data is handled.

Case study: AI in hiring has sparked debates over potential biases. Tools like HireVue promise efficiency in recruitment, but critics warn that AI can unintentionally reinforce discrimination. Is the convenience worth the risk?

5. Prepare for AI’s Unpredictability

Unlike traditional tech, AI can produce different results even with the same input.

This non-deterministic behavior might be great for creative applications, but it can confuse users expecting consistent results. Make sure you help your users understand what to expect, and guide them through how your AI works.

If your AI product generates inconsistent outputs, like when GPT models struggle to stick to a specific format, users need to know this upfront with some user cues

Otherwise, you risk frustration and dissatisfaction.

In high-stakes environments like healthcare, non-deterministic behavior could be a dealbreaker. Should we trust AI that doesn’t always deliver the same result when lives are on the line?