Show Me the Money: How to Monetize AI
In 2024, AI continues to make headlines with high valuations and eye-popping venture deals. Funding to AI companies more than doubled quarter-over-quarter to $24B [1], representing 30% of all invested dollars in Q2 2024. Since OpenAI launched ChatGPT, AI has been the leading sector in venture investing despite investor concerns about high valuations and revenue uncertainty. Companies like xAI, Figure AI, CoreWeave, Wayve, Scale AI, Perplexity, and Cognition AI all raised billion dollar rounds.
You get the picture -- all of the money is going into AI. However, more and more voices are sounding the bell --- money is going in, but how is it going to come back out to investors? Just where is all this money going to come from in the next 5-10+ years?
The AI bubble is likely approaching a tipping point soon. When that happens, the inevitable question is going to be: how do we actually create sustainable revenue from AI to justify these lofty valuations? Even the venerable OpenAI is facing business model questions as it's on track to potentially lose $5B this year. [2]
In a three-part series, I will dive into how we should think about monetizing AI. I'll focus in the AI application layer as pricing in the model and infrastructure layers will differ significantly based on the value they provide.
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Part 1 will explore the current landscape for pricing AI. The big dogs (think Microsoft and Google) have already started experimenting with pricing, largely iterating on SaaS fundamentals. But, what about the Davids of the AI world --- the smaller, less funded companies and startups?
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Part 2 will dive into the monetization levers for AI startup founders to consider. We'll explore the top levers that you can pull as a startup founder to start monetizing your AI app the right way: value metrics, GTM strategy, experimentation, and customer validation. Standard SaaS models have been priced on per seat basis, but what about usage or performance-based pricing? How do you start experimenting with pricing even if you are on v1 of your monetization model? How do you leverage your early adopter customer champions as you iterate your pricing? We'll also consider the merits of direct (charging for features) vs. indirect (not explicitly charging but adds value to your overall growth model) monetization.
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Part 3 will provide some tips to watch out for as the AI monetization model evolves. AI is the new tech that will spur a wave of innovation like mobile in the 2010s and the Internet in the 2000s. But, I would argue pricing fundamentals are tried and true. Companies may experiment with new models, but price-to-value is the ultimate axiom that they need to meet --- the value, however, is fairly ambiguous right now and is changing as we iterate on AI. What should companies and startups be on the look out for as AI applications and end buyers become more sophisticated?
Stay tuned :)
Notes
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