Week 3 is about understanding the landscape you're operating in — who else is playing in your space, how the market is structured, and how big the opportunity might be. This kind of research used to take weeks of analyst time. With AI, you can do a first pass in an afternoon — not to replace rigorous research, but to get oriented fast and ask better questions.
The key is to use AI as a starting point, not an ending point. AI can help you map the terrain, but you still need to verify what you find with current sources like Crunchbase, recent news, and conversations with people actually in the industry.
Mapping the competitive landscape
As we discussed in the reading, the goal isn't just to find direct competitors — it's to understand the full landscape of how customers currently solve the problem your startup addresses. AI is good at thinking broadly about this.
Try this prompt
"My startup is working on [describe your idea]. What are all the ways a customer might currently solve this problem — including direct competitors, indirect alternatives, and what they might do if neither existed? Think broadly."
Once you have a list, push deeper on each category. Who are the biggest players? What are they known for? Where do they fall short?
Try this prompt
"For the competitive landscape in [your space], help me understand: what are the main segments or categories of players? Who are the leading companies in each? What do customers generally like and dislike about the existing options?"
Finding companies that failed (and why)
As the reading notes, failed competitors can be just as informative as successful ones. If a well-funded company tried your idea and shut down, you need to know why — before an investor asks you about it.
Try this prompt
"What companies have tried to solve [problem] and failed? For each one, what do you know about why they failed — was it product, market timing, distribution, economics, competition, or something else?"
Building your positioning visualization
Rather than a feature checklist (which, as we discussed, is meaningless), you want a two-dimensional visualization that shows how you and your competitors are positioned. AI can help you think through what dimensions actually matter in your space.
Try this prompt
"I'm building a competitive landscape visualization for [your space]. What two-dimensional axes would best capture the meaningful differences between the players in this market? Suggest three or four different axis combinations and explain what each one reveals."
Estimating market size (TAM / SAM / SOM)
AI can help you think through market sizing methodology and find starting points — but treat the numbers as hypotheses to verify, not facts to cite.
Try this prompt
"Help me think through the market size for [your startup idea]. Walk me through how to estimate TAM, SAM, and SOM for this space — what data would I need, what assumptions am I making, and where might I find credible sources to support or refine those estimates?"
For actual market size data, AI can point you toward the right sources — industry associations, research firms like Gartner or CB Insights, government data — but you'll need to go verify the numbers yourself.
Try this prompt
"What are the best sources for market size data in [industry]? Which industry associations, research firms, or government agencies typically publish data on this space?"
Stress-testing your positioning
Once you have a draft competitive landscape, use AI to pressure-test it. You want to find the holes in your analysis before investors do.
Try this prompt
"Here is my competitive landscape analysis for [your startup]: [describe it]. What are the weaknesses in this analysis? What competitors or alternatives might I have missed? What questions would a skeptical investor ask about my positioning?"
What AI can't do for you
AI cannot tell you what customers actually think about your competitors right now — for that you need to talk to real customers. It can't tell you about a competitor that launched last month. And it can't replace the judgment that comes from deep domain expertise.
What it can do is help you build a solid foundation of research faster, ask better questions, and show up to customer conversations and investor meetings better prepared. Use it to get oriented — then go verify everything with real sources and real people.