Ai Product Strategy

# AI Product Strategy - All Guest Insights

*94 guests, 179 mentions*

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## Alex Hardimen
*Alex Hardimen*

> "We're training algorithms on specific data sets, like editorial important scores that actually come from our journalists. What that allows us to do is actually scale editorial judgment to a large group of readers. Those algorithms... they're trained on editorial signal and then they can still work towards driving towards outcomes like reach, engagement, conversion, et cetera."

**Insight:** AI strategy should focus on using algorithms to scale human expertise and judgment rather than just optimizing for engagement.

**Tactical advice:**
- Train algorithms on proprietary 'expert' data sets (e.g., editorial scores)
- Use AI to scale human judgment to a larger audience
- Balance expert signals with traditional engagement outcomes

*Timestamp: 01:04:25*


## Adriel Frederick
*Adriel Frederick*

> "When you are working on algorithmic heavy products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions."

**Insight:** The core role of a PM in AI products is defining the boundary between automated algorithmic decisions and human judgment.

**Tactical advice:**
- Identify which decisions require long-term strategic intent that algorithms cannot yet grasp.
- Create a framework that specifies the responsibilities of the machine versus the human operator.

*Timestamp: 00:00:00*

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> "It's more about giving people the information that they can use for decisions that they alone are good at and giving machines the power to amplify a person's intent... I think about it as designing an interface and make it an extension of yourself rather than a black box."

**Insight:** AI should be designed as a tool that amplifies human intent rather than a standalone black box that operates without human constraints.

**Tactical advice:**
- Design interfaces that provide humans with the necessary context to make strategic choices.
- Use ML to optimize for specific objectives while allowing humans to set the strategic constraints.

*Timestamp: 00:38:15*


## Albert Cheng
*Albert Cheng*

> "Behind the scenes, we're running chess engines to basically spit out evaluations for every move that you make. And then we translate that and make that approachable to the user using their native language and plain approachable style... that part is LLMs."

**Insight:** The best AI products use the right technology for the right task: specialized engines for logic/calculation and LLMs for human-friendly communication.

**Tactical advice:**
- Use LLMs to translate complex technical data (like engine evaluations) into natural, encouraging language for the user.

*Timestamp: 00:49:07*


## Alexander Embiricos
*Alexander Embiricos*

> "One of our major goals with Codex is to get to proactivity. If we're going to build a super system, has to be able to do things. One of the learnings over the past year is that for models to do stuff, they're much more effective when they can use a computer. It turns out the best way for models to use computers is simply to write code. And so we're kind of getting to this idea where if you want to build any agent, maybe you should be building a coding agent."

**Insight:** The most effective way for AI agents to interact with and control computers is by writing and executing code rather than using accessibility APIs or visual clicking.

**Tactical advice:**
- Prioritize coding capabilities as the core competency for any functional AI agent
- Focus on 'proactivity' where the agent chimes in or takes action without a direct prompt

*Timestamp: 00:00:47*

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> "I actually think Chat is a very good interface when you don't know what you're supposed to use it for... you start using it even outside of work to just help you. You become very comfortable with the idea of being accelerated with AI. So then you get to work and you just can naturally