Ryan Roslansky

Microsoft

Executive Vice President

LinkedIn

Chief Executive Officer

Chief Product Officer

Intuit

Member Board of Directors

The Paley Center for Media

Member Board of Trustees

GoDaddy

Member Board Of Directors

Glam Media

Responsible for all consumer, advertiser, publisher and developer products across the Glam Media Network.

Grew Glam Media from a top 100 global Comscore property to a top 10 in 18 months.

SVP Products & Content

Housing Media

Co-founded and grew one of the first internet real estate & rentals classified directories.  Leveraged the internet for finding real estate buyers, selling properties, and bringing efficiency to the home buying process through multiple real estate brokers across CA and FL.

Co-Founder / CEO

Yahoo

Various General Management and Global Product Management roles across Commerce, Classifieds, Directory and Search.

General Management & Product Management

As CEO of LinkedIn, I am passionate about connecting the world’s professionals to make them more productive and successful. In my years with LinkedIn, I've been fortunate to work alongside talented and innovative colleagues, and together we have developed the world's leading professional networking platform. As we look to grow and evolve LinkedIn in the years to come, I'm excited to continue driving our vision of creating economic opportunity for every member of the global workforce.

Click the bell, never miss an update from someone you want to hear from... Great reminder from Gyanda.

Back with a Friday update. This time, instead of talking about a new product, I want to highlight one that has existed for a while but you may not have used it yet. It's the subscribe bell. Check out the video on how to use it - give it a try and let me know what you think.

Excellent AI product making advice from Lenny Rachitsky and Chip Huyen 👇

Deeply researched product, growth, and career advice

My biggest takeaway from Chip Huyen: 

1. Most AI product problems aren’t AI problems. When companies think they have an AI performance issue, it’s usually a user experience problem, an organizational communication gap, or a data quality issue. One company thought their AI lead scoring system was broken, but the real issue was that the marketing team wasn’t asking the right questions to get useful data.

2. Your best performers benefit most from AI tools. In a controlled experiment, the highest-performing engineers got the biggest productivity boost from AI coding assistants, not the lowest performers. Senior engineers who already knew how to solve problems used AI to work even faster, while low performers often just copied and pasted code they didn’t understand.

3. How you prepare your data matters more than which database you choose. Companies see their biggest AI performance gains from better organizing and preparing their information—breaking content into the right size chunks, adding summaries, converting content into question-and-answer format—rather than agonizing over which technical infrastructure to use.

4. The biggest improvements to your AI product come from talking to users and understanding their feedback, not from adopting the latest models or staying glued to AI news. Many companies waste time debating which technology to use, when the real wins come from better user experience and data preparation.

5. Fine-tuning should be your last resort. Before investing in fine-tuning a model, try simpler solutions first: improve your prompts, add basic post-processing scripts, or fix your data pipeline. One company caught 90% of its model’s mistakes with a simple script. Fine-tuning creates ongoing maintenance headaches and should only be used when everything else has been maxed out.

6. You don’t need to be perfect to win. Many successful companies choose “good enough” over perfect when implementing AI systems. They calculate whether investing two engineers to improve accuracy from 80% to 85% is better than using those same engineers to launch an entirely new feature. Often, the new feature provides more value.

7. AI productivity is nearly impossible to measure. Companies invest heavily in AI coding tools but can’t clearly prove they work. When forced to choose between expensive AI subscriptions for their team or hiring one additional person, many managers choose the person, not necessarily because AI doesn’t help but because headcount feels more tangible.

8. Many people don’t know what to build despite having powerful tools. Even with AI tools that can build almost anything, many employees face an “idea crisis”—they simply don’t know what to create. The best approach: spend a week noticing what frustrates you in your daily work, then build small tools to solve those specific pain points.

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