In 2019, Lenny Rachitsky made a bold decision.
He left his position at Airbnb, with plans to embark on a new entrepreneurial journey.
Little did he know that a single article he wrote on Medium would completely change his path.
Instead of pursuing a startup, he was convinced to channel his energy into writing.
So, a remarkable journey commenced, and Lenny gradually transformed himself into a prominent figure within the realm of newsletters, with over 400k subscribers.
But Lenny's influence doesn't end there. He also hosts a captivating podcast, where he engages in conversations with esteemed product leaders and growth experts: Lenny’s Podcast.
The goal? To equip his audience with actionable insights for building and expanding their products, all with a touch of innovation and uniqueness.
And that’s exactly what he did with this episode.
The guest is Marily Nika, a true AI Product Leader who brings a wealth of expertise and experience from the world of artificial intelligence.
With an impressive background of eight years at Google and a PhD in Machine Learning, Marily stands as a true expert in her field. Currently serving as a product lead at Meta, where her focus lies on the exciting realms of Metaverse, avatars.
Beyond her professional achievements, Marily is deeply passionate about AI and its transformative potential. As an Executive at Harvard Business School, she actively shares her knowledge and insights through teaching engagements. Her highly sought-after course on AI Product Management has gained significant popularity on Maven, reflecting her commitment to empowering others in the field.
AI in Product Management: Ally or Menace?
Marily highlighted both the overhyped and underhyped aspects of AI, addressing potential and concerns.
“I was reading this article this morning where writers are complaining, and they're very, very fearful, and they think, "Oh, writing online is going to die. Everything we've been studying for is going to be replaced. They're going to take our jobs and so on." And I'm just like, "No, no, no. Technology is enhancing our work. It's enhancing us. It does not steal from us."
From her experience, technology enhances rather than steals from human creativity and work.
Marily discussed her daily experience using ChatGPT to craft compelling mission statements and create user segments.
She also stressed once again that AI will not steal anyone’s job, it is a tool that will make jobs easier.
“It will provide the motivations, it will provide the pinpoints, and YOU come up with ideas as you read it.”
Marily on User segments
If we start to see it as a tool or even an assistant, we will be able to transform first the industry and then society as a whole.
We are entering a scenario where AI becomes the default technology in almost everything, and it’s happening now.
“I believe that prod managers will be AI product managers in the future. And this is because we see all products needing to have a personalised experience, a recommender system that is actually good.”
Product Managers will transform into AI Product Managers, and this is due to the increasing need for personalised experiences across various domains ( just think about Netflix recommendations after you watch something!)
PMs can do this by incorporating AI tools and partnering with research scientists:
“People will have PhD researchers on their teams helping them build models into their product to make their product better. Is that what you're saying?”
“Correct. And from a product perspective, I can imagine three bubbles in my head. So you want to find the intersection of both something desirable by users, something that is going to be a viable business and something that is going to be feasible from a research scientist and technical perspective.
Whenever I say researcher, I mean research scientist that can produce an AI machine learning model.”
However, she also encourages PMs to learn how to code and train models themselves.
A deeper understanding of coding fosters confidence, a different mindset, and a unique approach to problem-solving.
By doing so, they gain the skillset to comprehend how the tools they rely on were developed and avoid blindly trusting them.
It is a transformation from Product Manager to AI Product Manager.
“The generalist PM helps their team and their company build and ship the right product.
But the AI PM helps their team or company solve the right problem.”
Marily also shared some invaluable knowledge on how PMs should approach AI:
- Start with a Problem: Identify a real pain point or problem that could be solved more effectively with AI. Don't pursue AI for its own sake; keep in mind that “Basically, everything that gets data about the user's behaviour, can be improved with AI.”
- Focus on Problem-Solving: Remember that the role of an AI PM is about solving the right problems. “Make sure there is a problem there. Make sure there is a pain point that needs to be solved smartly. Once you have identified what that problem is and what that very, very high-level solution is, then reach out and try to figure out how to implement it.”
- Prototype First: Instead of diving into full AI integration, create a prototype that simulates AI functionality. This allows you to gather feedback and validate the idea before investing significant time and effort.
- Leverage Existing Data: Focus your AI efforts where there is relevant data available. Look for opportunities to leverage data from adjacent products or within your organisation and try to experiment a bit.
In Marily’s words, “Just start thinking about it, where you could deal, get a data science intern and just see what they are going to do, there's just so much people can do.”
- Data Requirements: Understand that the amount of data needed depends on the task. Simple classifications may work with a small labelled dataset, but more complex applications like voice recognition or NLP require a significant amount of data.
- Diversify Data Sources: When building models, it's important to diversify data sources. Avoid relying solely on pre-existing datasets. Collecting and using your data can help differentiate your product and achieve higher quality.
- Data Availability Challenges: Acknowledge the challenges of data availability. If necessary, consider synthesising artificial data to train and test models.
- Determine Launch Readiness: As a PM, it is your responsibility to decide when the product's AI functionality is good enough for launch. Consider factors such as accuracy and user satisfaction when setting the bar.
Finally, according to Marily, PMs that want to enter the AI world should acknowledge:
- Uncertainty.
“You may have been working on all of these incredible research and ideas in hypothesis, but then when you train the model, the results you may be getting may not be optimal, may not be answering the questions or the hypothesis that you had in mind. You need to be able to encourage the teams throughout this process because you're like the captain of the ship; you need to be the one that's cheerleading the team, making sure everyone keeps going.”
- Data.
“But getting good data is hard. You may need to be creative, figure out ways for data collection that you never thought you'd do. You may get on the street and ask for people to contribute data for what it is you're doing. You need to be able to and willing to do everything.”
- Career trajectory.
“Usually, product managers get ahead the more they launch. But if you're in research or if you're not going to launch as often, you need to make sure to clarify with the hiring managers early on, "Hey, what does progress mean? How am I going to get a sense of a research work which is different than what I've been doing so far?"
As Marily said, “It's challenging, but I always encourage people to flex different muscles, and this is the zero-to-one muscle.”
Final words
That was the recap of Lenny’s podcast episode. There are many great resources for product managers to keep their knowledge sharp. You can also check out our articles on which product managers to follow on Linkedin or Youtube, for more great insightful content like this!
