Until recently, Guillaume led product at Synthesia, a text-to-video SaaS platform. I once interviewed with their recruiter, and the first question they asked was: “How many research papers on Generative AI did you read last month?” In all fairness, I had read none. So here my story with them ends.
We met after he left and agreed to record a podcast.
Personally, I think it’s very insightful and demystifies the role of an AI Product Manager quite a bit.
But give it a listen and let me know what you think!
As always, here are my main takeaways from this conversation.
On the AI Research Product Manager role
The goal of AI Research Product Manager is to make sure that researchers work on something that will ultimately drive business outcomes.
Researchers want to research, a PM wants to create impact.
AI Research Product Manager should be comfortable to read and understand scientific papers and should have a good idea on what might come next.
The challenge of the role is often to find the intersection of user and customer needs and the things where the research space is pushing towards.
It is also extremely challenging for a PM to build credibility and interact effectively with scientists. After all, you are not a scientist.
To be efficient in this role you should be comfortable building prototypes by yourself.
Why text-to-video products generate the same or very similar output
I tried Synthesia, Collossyan, Elai, HeyGen and all of them gave me a similar output. Here is why:
Everyone copies everyone. Your technological advantage is temporary.
It normally takes a few months for competition to copy your breakthrough.
Your technology, so far, cannot be a moat—unless you have infinite engineering resources.
Which leads us to the next question - what are the moats in AI products?
Moats for AI Products
Weirdly, the moats are very similar to traditional companies.
Your understanding of users, their problems, and go-to-market strategies could be a moat.
One of the moats Synthesia has is that they seem to be the only text-to-video company that has learned how to sell to enterprises.
Your positioning can be a very strong moat.
On having your personal thesis
Picking your next gig is a bit like picking a stock to buy.
Every Product Manager needs a personal thesis; otherwise, you'll just hop from one random opportunity to another. Exactly what I did early in my career - whatever looks cool and pays decently.
If you’re chasing something that’s already trending, it’s usually too late to jump on it.
Your professional conviction should come from your deep experience in the domain or with a particular technology.
Advice to junior PMs who want to become AI Product Managers
Try to build simple solutions to the annoying problems your colleagues are experiencing. Use tools like Cursor to quickly create simple products. Use this experience to pitch yourself as someone knowledgeable about AI.
What else did we discuss?
Synthesia: From Building New Technology to Finding Product-Market Fit
What Differentiates AI PMs from Conventional Software PMs? The Role of a Research PM
Synthesia’s Product Process
What’s the Moat in AI?
Why Positioning Is Important
Why Scaling Compute Isn’t the Answer to LLM Challenges
How to Stay on Top of the AI Hype
Why Leave Google to Join a Startup?
Why Developing a Personal Thesis Is Important for PMs
How to Become an AI Product Manager
Advice for Junior PMs
Share this post