Sramana Mitra: So let me double click down on the second one. I will come to the picks and shovels in a moment, but my observation is that to build a vertical application on top of an LLM, you obviously need to train in domain specific data. Now, there is a benefit to kind of constraining that model. You can tell me more technically how much of this is viable and how are people doing it. If you constrain the model to a small language model, the hallucination problem should go away or at least get much more manageable. Is that a correct statement?
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More often than not, an honest Total Available Market (TAM) analysis yields small or medium sized markets. This could range from $50M, $100M, $200M to $500M.
>>>Sramana Mitra: All right, let’s discuss the second example.
Ashmeet Sidana: Sure, another example I’m very proud of is a company called Robust Intelligence. Again, I was the first investor and they did a good job. CEO Yaron Singer, PhD from Berkeley, was at Harvard when he observed the hallucination problems with the development and deployment of AI models.
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An acute lack of understanding of Ideal Customer Profile (ICP) and an inadequate Market Segmentation result in artificially bloated TAM.
Often, Segmentation is too broad.
Let’s look at an example.
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Ashmeet Sidana, Chief Engineer at Engineering Capital, talks about his AI investment thesis. It’s a wonderful discussion that not only entrepreneurs should listen to, but investors should also listen in to calibrate their own investment thesis.
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Accurate estimation of Total Available Market (TAM) is an essential component of a high velocity startup.
I see a chronic overestimation of TAM in my work with startups.
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There are over 7000 Incubators and 3000 Accelerators around the world by now. On average, they take 25-30 companies per cohort, give each $15k-$150k in pre-seed funding, and off the bat, drive them to seek exponential growth funded by Venture Capital.
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