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    The 4 Questions Startups Should Ask Themselves about Building with Generative AI by @ttunguz

    Venture Capitalist at Theory

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    2 minute read / Apr 7, 2023 /

    The 4 Questions Startups Should Ask Themselves about Building with Generative AI

    There are 4 questions a startup should ask themselves about building a startup that uses generative AI.

    I presented those questions & my views on their answers at Saastr’s Workshop Wednesday.

    I had a blast putting this deck together. I started with a few sentences, uploaded them to gamma.app to outline the presentation, popped over to Midjourney to generate images along the story line, & published it in IA Presenter.

    The video is here. The last slide contains the prompts for the images in the presentation.

    The narrative is :

    AI is a massive platform change that Goldman Sachs projects will increase GDP 300x more than the PC.

    GS estimates a 1.5-2.9% increase to US GDP, doubling GDP growth, net of 7% job loss. The PC increased GDP by 0.006%, according to NBER

    That alone should turn heads.

    There are 4 questions startups should ask themselves about building with generative AI.

    1. Layer : application, platform, or infrastructure? In the cloud, AWS, Azure, & GCP have created about as much market cap as all the top 100 B2B & B2C publics built on cloud (Netflix, ServiceNow, AirBnb, etc). But there are 100 applications compared to 3 infrastructure vendors.
    2. Market : how to compete with incumbents? Startups have negative time to launch in many markets with Adobe, Microsoft, & Salesforce launching Gen AI enabled software in weeks.
    3. Moats : how to develop competitive advantage? Competing on algorithms is possible but hard. Access to proprietary data provides a moat. Usage & distribution, like in classical SaaS, are likely the most sustainable & repeatable. Enterprise readiness will be an essential : ensuring buyers are safe from legal & compliance risks.
    4. AI Depth : what level of technical sophistication to bring into the company? Startups can integrate with a plug-in, build a prompt-tuning engine (a little model on top of a bigger model), or develop & train their own models. Each subsequent choice is more expensive, but provides a deeper moat. It’s likely startups start at plug-ins & then move down with scale that affords more usage & more capital to invest.

    If you’re building in the space,I’d love to hear from you.


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