A Primer On The AI Economy | HackerNoon

And once we are able to classify the ecosystem based on where value is created we can ask: how's value captured?Since the release of ChatGPT at the end of November, one thing is clear AI commercial viability is accelerating, giving us a glimpse into how the AI ecosystem is building up.That might be comprised of general-purpose engines like GPT-3, DALL-E, StableDiffusion, and so on.it will be built to provide more generalized solutions to any specific need.

This two-way system might bring the next evolution of the foundational model, to become general-purpose engines able to do many things.the main interface for those general-purpose engines might be natural language.Prompting though might remain a key feature of the foundational layer; it might instead disappear in the apps' layer, where those AI engines might primarily work as push-based discovery engines .

If we all build tools on top of ChatGPT or similar models, how can we actually build a competitive moat? Of course, chances are that this market might become an oligopoly, where a few players might control most of it and thus capture a good chunk of the value, which creates for them an incredible moat.If you're a small startup, trying to do it from scratch might be much harder.

For instance, imagine the case of an organization that wants to leverage AI to deliver custom experiences to its users.For instance, let's say the company wants to build a very specialized chatbot for support.Train it on the content the company has built over the years.Tackle with much more accurate questions tied to conversion based on CRM data.

By scaling AI systems, we get - unpredictable - emergent properties that, for better or for worse, might affect the evolution of AI. The Internet Business Playbook has taught us that the value of a web app might be built not only in its tech but in its ability to get better at scale.

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