Andy Black
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It starts at 38 mins, which I hope this link jumps to.
View: https://music.youtube.com/watch?v=aRVx37zQGFE&t=2280&si=se7m3FkDcrnWu1-P
I added the episode into NotebookLM and then asked it to summarise his tips for "AI startups".
...
Based on the conversation with Jason Cohen in the provided transcript, here is a summary of his tips for founders building AI startups:
Focus on "Solution Space," Not "Problem Space"
Cohen advises founders not to treat AI as the problem to be solved or to assume customers "want AI". Customers still have the same fundamental problems they have always had: marketers want leads, sales teams want conversions, and engineers want bug-free code. Instead, founders should view AI as part of the "solution space"—a tool that enables a solution that was previously impossible or significantly better than existing alternatives.
Target Experts, Not "Noobs"
Cohen categorizes AI products into three buckets and recommends building for "experts":
Aim for Massive Improvements (3x to 10x)
Because AI products can be unreliable, incremental improvements (like making a process 20% or 30% faster) are not compelling enough to switch tools. Cohen suggests seeking opportunities where AI provides a "step function" change—improving outcomes by 3x, 5x, or 10x. He notes that while AI might not be a "10xer" broadly, it can be in specific contexts, such as helping a developer use a new library instantly rather than spending days learning it.
Leverage Corporate Budget Bias
While one should focus on solving real problems, Cohen notes that current corporate budgets are heavily biased toward AI projects. Therefore, including an AI component is strategically smart because it aligns with where companies are currently willing to spend money.
Differentiation in a Crowded Market
Cohen warns that the underlying technology is not a "moat" because everyone is using the same models and prompts. Since the market will be flooded with competitors using similar tech, the way to win is through a specific vision for a narrow "ideal customer profile". By focusing intensely on a specific customer type, a startup can build a product that thrills that niche in a way broad competitors cannot.
I added the episode into NotebookLM and then asked it to summarise his tips for "AI startups".
...
Based on the conversation with Jason Cohen in the provided transcript, here is a summary of his tips for founders building AI startups:
Focus on "Solution Space," Not "Problem Space"
Cohen advises founders not to treat AI as the problem to be solved or to assume customers "want AI". Customers still have the same fundamental problems they have always had: marketers want leads, sales teams want conversions, and engineers want bug-free code. Instead, founders should view AI as part of the "solution space"—a tool that enables a solution that was previously impossible or significantly better than existing alternatives.
Target Experts, Not "Noobs"
Cohen categorizes AI products into three buckets and recommends building for "experts":
- Avoid Incumbents: Do not try to insert AI into general productivity tools (like spreadsheets or docs) because incumbents like Google and Notion are already doing this.
- Avoid AI for "Noobs": While empowering non-experts (e.g., non-coders building apps) creates a large market, Cohen warns that AI often "doesn't really work" perfectly. If the AI completes 80% of the work but gets stuck, a non-expert user is stranded because they lack the skills to finish the job.
- Build AI for Experts: This is the preferred path. When building for professionals (like developers or marketers), the user has the expertise to recognize and fix the AI's mistakes. This mitigates the risk of AI hallucinations or errors.
Aim for Massive Improvements (3x to 10x)
Because AI products can be unreliable, incremental improvements (like making a process 20% or 30% faster) are not compelling enough to switch tools. Cohen suggests seeking opportunities where AI provides a "step function" change—improving outcomes by 3x, 5x, or 10x. He notes that while AI might not be a "10xer" broadly, it can be in specific contexts, such as helping a developer use a new library instantly rather than spending days learning it.
Leverage Corporate Budget Bias
While one should focus on solving real problems, Cohen notes that current corporate budgets are heavily biased toward AI projects. Therefore, including an AI component is strategically smart because it aligns with where companies are currently willing to spend money.
Differentiation in a Crowded Market
Cohen warns that the underlying technology is not a "moat" because everyone is using the same models and prompts. Since the market will be flooded with competitors using similar tech, the way to win is through a specific vision for a narrow "ideal customer profile". By focusing intensely on a specific customer type, a startup can build a product that thrills that niche in a way broad competitors cannot.
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